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The importance of effective time management in research: Strategies for improving productivity

Research is a crucial aspect of academic and professional life, but it can be challenging to balance research responsibilities with other commitments. Effective time management is essential for researchers to be productive and achieve their goals without sacrificing their personal lives. In this article, we will explore the importance of time management in research and provide strategies for improving productivity.

Importance of Time Management in Research:

Effective time management is critical in research for several reasons. First, research requires a significant amount of time, and effective time management ensures that this time is used efficiently. Poor time management can result in missed deadlines, incomplete projects, and wasted resources. Second, time management can help researchers achieve a healthy work-life balance, which is essential for maintaining well-being and avoiding burnout. Third, time management can improve research outcomes by allowing researchers to focus on their work without distractions.

Strategies for Improving Productivity:

Here are some strategies for improving productivity through effective time management:

Plan Ahead: Effective time management begins with planning. Researchers should create a research plan that includes their goals, deadlines, and milestones. Planning helps researchers stay organized and avoid last-minute rushes.

Prioritize Tasks: Not all tasks in research are equally important. Researchers should prioritize tasks based on their importance and urgency. Tasks that are urgent and important should be given priority over tasks that are less critical.

Use Time-Blocking: Time-blocking is a technique that involves scheduling specific blocks of time for different tasks. Researchers can allocate time for specific activities, such as writing, data analysis, or meetings. Time-blocking can help researchers stay focused and avoid distractions.

Take Breaks: Taking breaks is essential for maintaining productivity. Researchers should take regular breaks to rest their minds and recharge their energy. Short breaks can help researchers stay refreshed and focused.

Avoid Multitasking: Multitasking can reduce productivity and increase stress. Researchers should focus on one task at a time and avoid distractions. Switching between tasks can reduce efficiency and increase the risk of errors.

Use Productivity Tools: There are several productivity tools available that can help researchers manage their time more effectively. Tools such as calendar apps, to-do lists, and project management software can help researchers stay organized and on track.

Delegate Tasks: Delegating tasks can help researchers save time and reduce their workload. Researchers can delegate tasks to research assistants or collaborators to free up their time for more critical activities.

Effective time management is critical for researchers to achieve their goals and maintain a healthy work-life balance. By planning ahead, prioritizing tasks, using time-blocking, taking breaks, avoiding multitasking, using productivity tools, and delegating tasks, researchers can improve their productivity and achieve their research objectives. Adopting these strategies can help researchers achieve their full potential and succeed in their research endeavors.

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Strategies for Effective Time Management in Academic Research

What is academic productivity in research?

Academic productivity is the efficient and successful creation of high-quality research outputs, such as papers, presentations, and grants. Accomplishing research objectives and enhancing academic careers entails managing time, resources, and energy. Maximizing productivity in academic research is essential because it results in higher output, a better work-life balance, and less stress.

Productive researchers can publish more, get financing, establish a solid reputation, and advance their careers. Additionally, work productivity research enables researchers to have a positive influence, feel fulfilled and accomplished, and make valuable contributions to their profession.

For instance, how to improve physician productivity , Implementing effective procedures, assigning responsibilities, and using technology—such as EHRs and AI-powered tools—will increase physician productivity. Reduce paperwork, streamline administrative processes, and cut down on disruptions.

How to increase research productivity?

The strategies for boosting productivity are as follows:

• Concentrating efforts, managing time well, and setting obvious priorities and goals are also important.
• Establish a timetable and follow it to guarantee steady progress and reduce procrastination.
• Minimize distractions and interruptions, such as social media , email notifications, meetings and other non-essential tasks
• .Leverage research tools and resources, including reference management software, productivity apps, time management tools, collaboration software
• Collaborate with others and delegate tasks to share the workload, bring new perspectives and develop new skills.
• Take regular breaks and practice self-care to prevent burnout, maintain energy and motivation and improve overall well-being.

How do you maximize maximize productivity?

Use applications and tools like reference and project management software to organize work and increase efficiency as a researcher. Use the “Pomodoro timer” strategy, focusing on 25-minute work intervals and taking 5-minute breaks in between.

How can a student maximize maximize their productivity?

Maximizing productivity as a student involves a multifaceted approach that encompasses effective time management, organization, and self-care practices. One of the most fundamental steps towards enhancing productivity is to create a well-structured study schedule and adhere to it diligently. This involves allocating specific time slots for different tasks, including studying, assignments, and recreational activities. By following a consistent schedule, students can optimize their time and ensure that they cover all necessary material in a timely manner.

In addition to a study schedule, leveraging productivity tools and applications can significantly aid in organization and task management. These tools can help students keep track of deadlines, manage their workload efficiently, and prioritize tasks based on their importance and urgency. Whether it’s a digital planner, task management app, or time-tracking tool, finding the right productivity resources can streamline academic responsibilities and alleviate stress.

Another key aspect of maximizing productivity is minimizing distractions, particularly those posed by email and social media notifications. Constant interruptions can disrupt focus and impede progress on tasks. By setting boundaries and limiting access to distracting platforms during study sessions, students can maintain concentration and productivity levels.

Moreover, preventing burnout is essential for sustaining long-term productivity. Taking regular breaks, practicing relaxation techniques, and engaging in activities that promote mental and physical well-being are crucial aspects of self-care. By prioritizing self-care practices, students can replenish their energy levels, reduce stress, and maintain motivation and productivity in the long run.

In summary, efficient time management is essential for academic research to boost output, fulfil deadlines, and accomplish research objectives. Researchers can enhance their production by applying techniques like goal-setting, scheduling, distraction reduction, and tool and resource utilization and make noteworthy advancements in their domain.

To increase the productivity and success of your research, put these strategies into practice right now!

• https://www.frontiersin.org/articles/10.3389/fevo.2022.1021812#:~:text=Academic%20productivity%20is%20often%20defined,Sarli%20and%20Carpenter%2C%202014 ).
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763375/

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For students to better manage their curriculum and achieve learning objectives, time management behaviors or skills are argued to improve the positive academic output. The aim of the study is to find the impact of time management on the academic performance of students among the diagnostic radiology technology students at KAU. This study had a cross-sectional survey-based study design. It was conducted among students of diagnostic radiology technology department at King Abdul-Aziz University from Sep .  2020 to Feb .  2021. For this study, 152 students were targeted among which 142 completed the questionnaire, making the response rate of 93.4%. Among 142 participants, 75 (52.8%) were females and 52 (36.6%) were in the 2018 batch. Majority of the students i.e., 107 (75.4%) had GPA between 4.5 - 5 in 2020. Whereas, 37.3% agreed or strongly agreed that they manage their time. Here, 69.2% students with 4.5 - 5 GPA strongly agreed that they meet their deadline (p value = 0.005) and 36.7% students with 4 - 4.5 GPA strongly believed that their academic performance decreased due to mis planning (p value = 0.005). Around 71 (66.3%) students with 4.5 - 5 GPA agreed or strongly agreed to make to do list or calendar (p value 0.047). In conclusion, according to student’s perception, preplanning their studies had been beneficial for their academic performance. However, less than half of the students agreed that they manage their time.

Academic Achievement , Cross-Sectional , Radiology , Students , Time Management

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1. Introduction

Throughout the history, there has been great emphasis on the effective and efficient management of time, which has also been considered the key to success (Pugh & Nathwani, 2017; Nasrullah & Khan, 2015) . Frederick Winslow Taylor gave the concept to use time management for analyzing time and motion studies of employees with aim to decrease time wasting and unproductive work (Savino, 2016) . In this competitive era, for high performance, the organizations and directors emphasize on searching time management tools (Kumar & Aithal, 2019) . It has also been advised to start practicing time management from the early student life (Valle et al., 2016) . Time management has also been defined as a form of self-management with a clear emphasis on time in understanding what activities to do; how to do them more efficiently; in what time it should be done and when is the correct time to the particular activity (Savino, 2016) . It has also been association with low anxiety and greater academic achievement in students (Jenaabadi, Nastiezaie, & Jalalzaei, 2016) .

Whereas performing goal directed activities with the purpose of using time efficiently defines time management as “behavior” (Aeon & Aguinis, 2017) . The time management behavior has three basic surfaces that are: time attitudes, long-range planning and short-range planning (Aeon & Aguinis, 2017) . Time attitude is the positive or negative perspective towards the present, future and past time which had been directly linked to academic achievement, especially in scientific subjects (Nieuwoudt, & Brickhill, 2017) . The concept of handling everyday jobs for longer period of time and keeping up with the objectives set for significant dates is called the competence in long range planning. Whereas, planning in the short run for the day to a day activity or planning for tasks within a day or week is short-range planning. For students to better manage their curriculum and achieve learning objectives, these time management behaviors or skills are argued to improve the positive academic output (Razali et al., 2018) .

There have been many studies that found association between greater academic achievement and effective time management as students acquire strategies that helps them in meeting competing demands (Nadinloyi et al., 2013; Kharadze, Gulua, & Davit, 2017) . The curriculums in higher education institutes are designed in a way that gets to peak and troughs in the workload of student requiring them to manage between their work-life balance, often without the support of institution. The non-cognitive personal behavior i.e., perspective of students regarding time management is also an effective predictor of educational achievement as with poor time management skills it gets difficult for students to plan their studies and which causes them anxiety and agitation at the assessment time which usually takes place at the end of the course (Scherer, Talley, & Fife, 2017) . Students have previously linked negative educational results with poor time management which could be partially self-serving bias, however, there have been ample studies that establish this association (Nadinloyi et al., 2013; Kharadze, Gulua, & Davit, 2017) . There have been studies conducted on time management and educational achievement in business students (Sayari, Jalagat, & Dalluay, 2017) , nursing students (Nayak, 2019) , management students (Gupta & Chitkara, 2018) , and engineering students (Adams & Blair, 2019) . However, none have been done on radiology students.

Previous studies have determined a positive effect of time management. Time management skills have indicated a positive effect on student learning and associated outcomes. A number of studies have identified the positive impact of time management. Time management skills have been shown to have a positive impact on student learning and student outcomes (Kearns & Gardiner, 2007; Kelly, 2002; McKenzie, Gow, & Schweitzer, 2004) and Krause & Coates (2008) indicated that the ability for successfully managing their time is the benchmark of students in developing better study habits as well as strategies for success. Time management provides individuals the ways for structuring and controlling their activities (Claessens et al., 2004) . In addition, report that the capacity to successfully manage their time is the foundation of students developing good study habits and strategies for success. Time management offers individuals the means to structure and control their activities (Claessens et al., 2004) . In addition, Wang et al. (2011) have revealed that time management is essential beyond the university campus, where the ability to manage one’s free time is revealed to substantially elevates the quality of life of an individual.

Time management is an immense distinction between web-based and formal education administratively and academically. There was the type of learning opportunities in formal education that integrates a clarification of everything (Nieuwoudt & Brickhill, 2017) . Teachers, in this system, are in front of students and they can ask anything regardless of any hesitation or delay. However, distance learning students have no such type of academic environment as compared to formal learners. The presence of physical distance is evidently reported between teacher and student in online learning (Ahmad & Ch, 2017) . Thereby, it is more complex for managing time and gaining high scores in distance education. Web-based students explore and learn online that just integrates incomplete data and the rest is their business for thinking about and examine further. It is an art for managing time and every learner must command and familiar on this skill for the sake of betterment (Broadbent, 2017) . They experience in domestic as well as academic world because of this incompetence. In this regard, majority of learner’s experience issues such as errand vulnerability and repugnance, so they commence for stalling their time administrative skills.

This study is based on existentialism philosophy, which is learner centered and offers direction to students about their study. Learners are self-responsible of their academics as they just seek guidance from their teachers, but take decisions as well as manage all activities themselves. This study is relied on pickle jar theory, which is the modern and relevant theory of time management (Sharma & Would, 2004) . This theory shows that human beings should construct balance among activities and things while integrating time management techniques effectively. The success of students is determined through effective utilization of time at each phase in distance learning institutions and regular system.

Therefore, the aim of the study is to find the impact of time management on the academic performance of students among the diagnostic radiology technology students at KAU. The study objectives also include to find out the effectiveness of the preplanning and the instantaneous planning on academic performance. This research will add self-evaluation to student’s daily habit and plan. Also, it will provide knowledge and recommendation for skills that can assist student for perfect personal strategic plan. It will also help to overcome the wrong common rumors statements about time management and will raise awareness about procrastinating behavior of time management and its effect on academic performance among radiology students.

This study had a cross-sectional survey-based study design. It was conducted among students of diagnostic radiology technology department at King Abdul-Aziz University. The study duration was six months from Sep 2020 to Feb 2021. Only the undergraduate students were included in the studies. Post-graduate students and students who had completed their education were excluded. The recommended sample size with 95% confidence level, 200 population size, 50% response distribution, and 5% margin error were calculated to be 132.

All registered undergraduate students were invited for taking part in the study. They were provided a brief overview of the project as well as information about confidentiality and research ethics. A total of 132 students responded by providing informed consent and completing the survey. The participation rate was just over 66% of the overall population. There might be some bias in the resultant data since the survey was self-developed. The nature of this bias is complex to establish, since the literature might recommend that both students should be responsive to complete this survey who have better time management skills and tend to procrastinate.

The academic performance of students such as GPA was acquired through the student administration system and departmental documents of the institution. Log files were also collected on students’ participation in courses on the virtual learning platform. A unique random identifier was allocated to each student that would not show his or her identification to others beyond the research team. These unique codes enabled the combination of performance data, virtual learning platform files, and questionnaire responses in excel file, which did not comprise of any personally identifiable information. Once collected, such unique codes became irrelevant and were omitted.

This study was approved by the Research and ethics committee of faculty of applied Medical Science, KAU. The questionnaire was a self-designed, and had 22 questions on Likert scale of 1 - 5 where 1 indicated strongly agree and 5 denoted strongly disagree and last question was about the GPA achieved in the last year. The questionnaire was prepared on the basis of self-management, awareness, management, preferences, and performance domains (Appendix 1).

Feedback was taken from research experts and changes were made before implementation. Also, an internal pilot study on 20 students was done before distributing it on the larger sample. The SPSS software version 25.0 was used to analyze the data. The Pearson chi-square test was conducted to find association between the time management and GPA achieved. The alpha coefficient was 0.05 and p-value below 0.05 was considered statistically significant.

Among 142 participants, 75 (52.8%) were females and 67 (47.2%) were males. Around 52 (36.6%) were in the 2018 batch, 45 (31.7%) were in the 2019 batch and 45 (31.7%) were in the 2020 batch of radiology. Figure 1 represents the GPA of student. Majority of the students i.e., 107 (75.4%) had last year GPA between 4.5 - 5. Table 1 represents the frequency of the answers given by the participants for each question related to time management. Around 64.8% disagreed or strongly disagreed that preplanning methods are inefficient, indicating that majority of the students perceive preplanning to be efficient for academic performance. Whereas, 37.3% agreed or strongly agreed that they manage their time. Majority of the students (92.3%) agreed that they meet their deadlines ( Table 1 ). Though, 74.6% agree or strongly agree that their time management skills can be approved.

Table 2 is showing the relationship between GPA of last year and student perception regarding time management. Here, 36.7% students with 4 - 4.5 GPA strongly believed that their academic performance decreased due to mis planning (p value = 0.005). Whereas, Table 2 shows the relationship between GPA of last year and time management behavior. Here, 69.2% students with 4.5 - 5 GPA strongly agreed that they meet their deadline (p value = 0.005). Similarly, Table 3 showed the relationship between various factors and last year GPA. Here, majority i.e., 71 (66.3%) students with 4.5 - 5 GPA agreed or strongly agreed to make to do list or calendar (p value 0.047) ( Table 4 ).

Figure 1 . Percentage of students with various GPAs.

Table 1 . Frequency of the response related to time management on Likert scale.

Table 2 . Relationship between student perception and GPA.

Table 3 . Relationship between behavior for time and GPA of last year.

Table 4 . Relationship between various factors of time management and GPA.

4. Discussion

This study aimed to find the impact of time management on the academic performance of students among the diagnostic radiology technology students at KAU. There were more female students that answered the questionnaire. According to student’s perception, preplanning their studies had been beneficial for their academic performance. However, less than half of the students agreed that they manage their time, which could be due to various reasons. In this study students agreed that insufficient sleep pattern makes their feel lazy and they are unable to manage time. Furthermore, majority also believed that the last year had been challenging (due to covid-19 pandemic) and affected their time (and online learning). Previous study on undergraduate nursing students also found that decreasing procrastination and increasing time management will help the students to have better academic performance, less anxiety and will be better prepared for working in hospital (Romero-Blanco et al., 2020) . Similar to the results of this stud, previous study Romero-Blanco et al. (2020) also posited that during previous year of covid-19 pandemic, sleep pattern among students has been affected. Elagra et al. (2016) suggest that decrease in quality sleep also decreases the academic performance in the students.

The students with positive attitude and who claimed to manage time and meet their deadlines were also had high academic grades. Whereas, short-term and long-term time management were not associated with the high GPA. Contrast to it, previous study had resulted time management behavior such as short range and long-range management to be effective (Ganguly, Kulkarni, & Gupta, 2017) . However, those studies are old and suggested effect in mature female students only. Therefore, a longitudinal study is required to observe the relationship between time management behavior and academic achievement for a definite relationship.

Among the high GPA holders, majority of them agreed to make a diary, to-do list or put important dates on the calendar. It might be due to the established relationship between maintaining a task list with effective time management (Caligor, 2017) . However, according to recent study (Evans, 2017) , it is important to digitalize this to-do list or task list for better accessibility, timely reminders and collaborations. This study is limited based on its design being a cross-sectional survey which is a snapshot of a situation. Therefore, for better management, it is important to have a longitudinal prospective study with long term follow up throughout the academic years to further analyze the relationship. It is also recommended for students to maintain their sleeping pattern for better time management. Furthermore, as a to-do list or making task list on calendar has been a positive factor in time management and academic achievement, it is recommended to use digitalized to-do lists for better performance.

In conclusion, according to student’s perception, preplanning their studies had been beneficial for their academic performance. However, less than half of the students agreed that they manage their time. No relationship was found between short range and long-range time management behavior on academic achievement. This study recommends better sleeping pattern and making of to-do list among students for better time management, which could lead to high academic achievement.

Acknowledgements

The author is very thankful to all the associated personnel and departments in any reference that contributed in/for the purpose of this research.

Funding Sources

No financial support was received for this study.

Ethical Considerations

This study has been reviewed and approved by the Faculty of Applied Medical Sciences, King Abdul-Aziz University.

Research questionnaire

Statement Questions (in a scale of 5 choose from strongly disagree – Strongly agree)

1) Preplanning methods are inefficient, and it does not affect academic performance.

2) I find this year challenging and it has affected my time (Online learning).

3) I manage stress when handling multiple conflicting duties.

4) My academic performance is degraded due to mis planning.

5) I prefer to manage my time daily.

6) I have a clear established plan for each week’s tasks.

7) I have a clear established plan for each month’s tasks.

8) I have a set of goals for the entire quarter/semester.

9) I have a clear established plan for each year’s tasks.

10) I feel lazy and my study mood is ruined because of insufficient sleeping-pattern.

11) I have enough time to complete my tasks during the day.

12) I usually understand the whole radiology lectures.

13) I meet the deadline for any work.

14) I often tend to delay/postpone my tasks.

15) I effectively manage workload.

16) I feel that my time management skill needs more improvement (self-assessment).

17) I priorities between various competing tasks.

18) I used to put my important dates on a single calendar (To-do-list program etc.).

19) I spend my time wisely and avoid distractions.

20) I can adapt and be flexible when changes occur, reassess priorities, and still produce quality work.

21) My participating in extracurricular activities won’t affect my academic performance.

22) I balance between my private time and study time

23) Please specify your last year GPA (√):

a) I achieve a (4.5 - 5) last year _____

b) I achieve a (4.0 - 4.4) last year _____

c) I achieve a 3.5 - 3.9 last year _____

d) I achieve a 2.5 - 3.4 last year _____

e) I achieve less than (2.5) last year _____

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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McGraw Center for Teaching and Learning

Principles of effective time management for balance, well-being, and success.

The principles below are derived from research on time management, motivation theory and much experience working with university students. Think of time management techniques as tools to help you do what you value the most. Make these tools into an expression of your values—what’s most important to you—not just a schedule to get more stuff done. Try to keep these principles in mind as you schedule and calendar your time, and when making the moment-to‐moment decisions that are crucial to effective time management for balance and well-being.

• Commitment —if you can’t commit to devoting time to a task, don’t put it in your schedule. Only schedule tasks you WILL do. Be brutally realistic, not idealistic when making your schedule. Creating a schedule you can’t actually keep is setting yourself up for frustration. If you don’t actually stick to your schedule it will soon become useless. This may have happened to you in the past.
• Pursue fun with a vengeance —Make time for enjoyable, rejuvenating and satisfying activities like organizations, sports, and entertainment. Organize your academic and other obligations AROUND these commitments to fun.
• Time vs. task focus —Think of your day in terms of time, not the tasks you have to do. Devote time to important tasks every day. It’s hard to predict how long a task will take, so it’s hard to schedule with great precision. But you can reliably schedule regular intervals of time and get into a routine. Make an appointment with yourself for a particular time period, and when playing or working, set your purpose “I'll get the most out of this time.”
• One thing at a time —Current research shows us that multi-­‐tasking is a myth. In actuality, we are switching back and forth between tasks. With each switch we pay a cognitive cost and a time cost: It takes time to get mentally back into the task, thus making us less efficient. When switching we lose the depth of our engagement, absorption. This depth is necessary at Princeton where you are expected to gain conceptual mastery, not merely a superficial understanding.
• Block out time —devote, on a regular basis, chunks of time to a specific class. Make it part of your schedule, your routine. Estimate how many hours per week you want to devote to a class. Set aside this many hours for working tasks in the course Slice up your task into pieces and allow specific blocks of time for specific pieces of a big project.
• First Things First —if you can do so, schedule the things that are most important to you first thing in the day, or at the first available time slot. Anything that gets scheduled later in the day has a greater chance of getting interrupted, put off and never gotten to. You won’t be thinking or worrying about your work during your leisure time if you get academic tasks done first.
• Routine —It takes 30 days to create a habit, but good habits make your life easier. With good habits in place you don’t have to make as many hard decisions, thus you are less likely to make unproductive ones such as talking yourself out of doing what you had planned.
• Flexibility —How do you incorporate flexibility into your schedule? Don’t schedule every hour of the day, leave empty time slots, and schedule in recreation time. Create a two-hour or three-hour block on Friday as a catch all makeup time. When things come up and you are deciding whether to diverge from your established schedule, survey future hours and days to see where you can make up lost time. Switch blocks of time so that your schedule reflects your new commitments.
• Respond vs. react —In the moment of decision-­‐making, when faced with a decision or an impulse to diverge from your schedule, don’t just react, RESPOND. Pause, take a moment to think. Remember what’s most important to you and do what will help you get it. For example, if exercise is a top priority for you, don’t let a sudden fear about a grade prevent you from exercising. Be ready to reduce the amount of time, but don't compromise on your health. Don’t let “mindgames” in which you create justifications get in the way or lead you astray.
• Choose carefully where you study and do other tasks: minimize distraction; maximize focus.
• Use physical reminders. If you want to work out more, but are getting bogged down in email or Facebook, put your running shoes on top of your laptop. Make it harder to get off track and easier to stick to your plan by changing your environment.
• Study buddy/group—work on problem sets, readings, etc. in your shared course together.
• Get a study/writing partner—same place and time, but not the same course.
• Ask friends NOT to call you at specific times. Ask them to help you stick to your schedule. Say, “tell me to leave your room” or the dining hall after one hour, etc.

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Time Management Is About More Than Life Hacks

• Erich C. Dierdorff

Your productivity hinges on these three skills.

There is certainly no shortage of advice — books and blogs, hacks and apps — all created to boost time management with a bevy of ready-to-apply tools. Yet, the frustrating reality for individuals trying to improve their time management is that tools alone won’t work. You have to develop your time management skills in three key areas: awareness, arrangement, and adaptation. The author offers evidence-based tactics to improve in all three areas.

Project creep, slipping deadlines, and a to-do list that seems to get longer each day — these experiences are all too common in both life and work. With the New Year’s resolution season upon us, many people are boldly trying to fulfill goals to “manage time better,” “be more productive,” and “focus on what matters.” Development goals like these are indeed important to career success. Look no further than large-scale surveys that routinely find time management skills among the most desired workforce skills, but at the same time among the rarest skills to find.

• Erich C. Dierdorff is a professor of management and entrepreneurship at the Richard H. Driehaus College of Business at DePaul University and is currently an associate editor at  Personnel Psychology.

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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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Time Management: 10 Strategies for Better Time Management

The term Time Management is a misnomer. You cannot manage time; you manage the events in your life in relation to time. You may often wish for more time, but you only get 24 hours, 1,440 minutes, or 86,400 seconds each day. How you use that time depends on skills learned through self-analysis, planning, evaluation, and self-control. Much like money, time is both valuable and limited. It must be protected, used wisely, and budgeted.

• Are more productive.
• Have more energy for things they need to accomplish.
• Feel less stressed.
• Have more free time to do the things they want.
• Get more things done.
• Relate more positively to others.
• Feel better about themselves. (Dodd and Subdheim, 2005)

Finding a time management strategy that works best for you depends on your personality, ability to self-motivate, and level of self-discipline. By incorporating some, or all the ten strategies below, you can more effectively manage your time.

1. Know How You Spend Your Time

A time log is a helpful way to determine how you use your time. Record what you are doing in 15-minute intervals for a week or two. Evaluate the results:

• Did everything you needed to do get done?
• Which tasks require the most time?
• What time of day when you are most productive?
• Where is most of your time is devoted (i.e. job, family, personal, recreation)?

Identifying your most time-consuming tasks and determining whether you are investing your time in the most important activities can help you to determine a course of action. Having a good sense of the time required for routine tasks can help you be more realistic in planning and estimating how much time is available for other activities. Many apps exist to help you keep track of your time, as mentioned in Strategy 3.

2. Set Priorities

Managing your time effectively requires a distinction between what is important and what is urgent (MacKenzie, 1990). Experts agree that the most important tasks usually aren’t the most urgent tasks. However, we tend to let the urgent tasks dominate our lives. Covey, Merrill, and Merrill (1994) categorize activities into four quadrants in their Time Management Matrix: urgent, not urgent, important, and not important. While activities that are both urgent and important must be done, Covey et al. suggests spending less time on activities that are not important (regardless of their urgency) to gain time for activities that are not urgent but important. Focusing on these important activities allows you to gain greater control over your time and may reduce the number of important tasks that become urgent.

Creating a "to do” list is an easy way to prioritize. Whether you need a daily, weekly, or monthly list depends on your lifestyle. Be careful to keep list-making from getting out of control. List manageable tasks rather than goals or multi-step plans. Rank the items on your “to do” list in order of priority (both important and urgent). You may choose to group items in categories such as high priority, medium priority, or low priority; number them in order of priority; or use a color-coding system. The goal is not to mark off the most items, but to mark off the highest priority items (MacKenzie, 1990). A prioritized “to do” list allows you to set boundaries so you can say “no” to activities that may be interesting or provide a sense of achievement but do not fit your basic priorities.

3. Use a Planning Tool

When using a planning tool:

• Always record your information on the tool itself. Jotting notes elsewhere that must be transferred later is inefficient and wastes more time.
• Review your planning tool daily.
• Keep a list of your priorities in your planning tool and refer to it often.
• Keep planning tools synchronized. If you keep more than one, make sure your phone, computer, and paper planning tools match.
• Keep a back-up system.

Apps on your phone can be great planning tools. Apps typically fall into one of the following categories:

• Time Trackers – Gain an awareness of how you spend your time.
• Time Savers – Increase productivity and break time-wasting habits.
• Task Managers – Prioritize and organize tasks to improve time management.
• Habit Developers – Create healthy habits to encourage time management.

4. Get Organized

Disorganization leads to poor time management. Research has shown that clutter has a strong negative impact on perceived well-being (Roster, 2016). To improve your time management, get organized.

Set up three boxes (or corners of a room) labeled "Keep," "Give Away," and "Toss." Sort items into these boxes. Discard items in your “Toss” box. Your "Give Away" box may include items you want to sell, donate, or discard.

The next step is to improve the time you spend processing information. For example, tasks such as email can eat up your day. To combat wasted time, implement an email organization system that allows you to process the information in each email as efficiently as possible. Use folders, flagging, or a color-coded system to keep track of what’s what.

5. Schedule Appropriately

Scheduling is more than just recording what must be done (e.g., meetings and appointments). Be sure to build in time for the things you want to do. Effective scheduling requires you to know yourself. Your time log should help you to identify times when you are most productive and alert. Plan your most challenging tasks for when you have the most energy. Block out time for your high priority activities first and protect that time from interruptions.

Schedule small tasks such as drafting an email, creating a grocery shopping list, reading, watching webinars or listening to podcasts for long commutes or when waiting for a call or appointment. Capitalize on what would otherwise be time lost. Avoid nonproductive activities, such as playing games or scrolling through social media. Limit scheduled time to about three-fourths of your day to allow for creative activities such as planning, dreaming, and thinking.

6. Delegate: Get Help from Others

Delegating means assigning responsibility for a task to someone else, freeing up your time for tasks that require your expertise. Identify tasks others can do and select the appropriate person(s) to do them. Select someone with the appropriate skills, experience, interest, and authority needed to accomplish the task. Be specific. Define the task and your expectations while allowing the person some freedom to personalize the task. Check how well the person is progressing periodically and provide any assistance, being careful not to take over the responsibility. Finally, reward the person for a job well done or make suggestions for improvements if needed. (Dodd and Sundheim, 2005). Another way to get help is to “buy” time by obtaining goods or services that save time. For example, paying someone to mow your lawn or clean your house, or joining a carpool for your children’s extracurricular activities frees time for other activities. The time-savings from hiring someone for specialized projects is often worth the cost.

7. Stop Procrastinating

People put off tasks for a variety of reasons. Perhaps the task seems overwhelming or unpleasant. To help stop procrastination, consider “eating the big frog first.” A quote commonly attributed to Mark Twain says, “If it’s your job to eat a frog today, it’s best to do it first thing in the morning. And if it’s your job to eat two frogs, it’s best to eat the big frog first.” Unpleasant tasks we procrastinate completing are “big frogs.” Complete these tasks as your first action of the day to get them out of the way. Another option is to “snowball” your tasks by breaking them down into smaller segments, completing preparatory tasks, and eventually completing the larger task at hand. Whether you choose the “big frog first” or “snowball” method, try building in a reward system for completed tasks to help stay motivated.

8. Manage Time-Wasters

Reduce or eliminate time spent in these activities by implementing some simple tips.

Handheld Devices

• Take advantage of voice-to-text features such as transcribed voicemails or to make notes or draft emails and text messages when you are on the go.
• Avoid small talk. Stay focused.
• Take any necessary action immediately following a call.
• Impose screen time limits and regularly monitor your digital wellness (see Strategy 10).
• Schedule breaks from your devices.
• Set aside a specific time to view and respond to email, but don’t let it accumulate to the point it becomes overwhelming to sort.
• Turn off notifications for email.
• Handle each item only once if possible.
• Immediately delete or unsubscribe from junk emails.
• Keep address books up-to-date and organized.
• Utilize built-in shortcuts to sort email.

Unexpected Visitors

• Schedule time for face-to-face visits.
• Inform visitors of your time constraints and politely offer to reschedule.
• Set a mutually agreeable time limit for the visit.
• When someone comes to the door, stand up and have your meeting standing to help keep it brief.

In-Person and Virtual Meetings

• Know the purpose of the meeting in advance.
• Arrive early.
• Start and end the meeting on time.
• Prepare an agenda and stick to it. Use a timed agenda, if necessary.
• Don’t schedule meetings unless they are necessary and have a specific purpose or agenda.
• Use recording software or designate a note-taker.

Family Obligations

• Use and sync virtual calendars for easy sharing between busy family members.
• Make each family member responsible for consulting the master calendar for potential conflicts.
• Create a central area or agreed upon app for posting communications such as appointment reminders, announcements, and messages.

9. Avoid Multi-tasking

Psychological studies have shown that multi-tasking does not save time. In fact, the opposite is often true. You lose time when switching from one task to another, resulting in a loss of productivity (Rubinsteim, Meyer, and Evans, 2001). Routine multi-tasking may lead to difficulty in concentrating and maintaining focus. Do your best to focus on just one task at a time by keeping your area clear of distractions, including turning off notifications on your devices, and set aside dedicated time for specific tasks.

10. Stay Healthy

The care and attention you give yourself is an important investment of time. Scheduling time to relax or do nothing helps you rejuvenate physically and mentally, enabling you to accomplish tasks more quickly and easily. Be sure to monitor your screen time as a part of your digital well-being, setting boundaries to stay healthy. A study conducted by Google showed that four out of five study participants who took steps to improve their digital well-being believe their overall well-being was positively impacted as well (Google, 2019). To improve your digital well-being, set time limits or utilizing built-in software on electronic devices such as phones and tablets to help maintain your digital wellness. Blue light blockers and grayscale mode may also help you improve your digital well-being. Set a time each night to shut off all digital devices to give your mind time to relax; this can also help improve your sleep schedule.

Unfortunately, poor time management and too much screen time can result in fatigue, moodiness, and more frequent illness. To reduce stress, reward yourself for time management successes. Take time to recognize that you have accomplished a major task or challenge before moving on to the next activity.

Whatever time management strategies you use, take time to evaluate how they have worked for you. Do you have a healthy balance between work and home life? Are you accomplishing the tasks that are most important in your life? Are you investing enough time in your own personal well being? If the answer is “no” to any of these questions, then reevaluate your time management strategies and transition to ones that will work better for you. Successful time management leads to greater personal happiness, more accomplishments at home and at work, and a more satisfying future.

Previously updated by: Roxie Price, University of Georgia Extension Dana Carney, University of Georgia Extension Rachael Clews, K-State Research and Extension

Originally written by: Sue W. Chapman, retired, UGA Extension Michael Rupured, retired, UGA Extension

Covey, S. R., Merrill, A. R., & Merrill, R. R. (1994). First things first: To live, to love, to learn, to leave a legacy . Simon & Schuster.

Dodd, P., & Sundheim, D. (2005). The 25 best time management tools and techniques: How to get more done without driving yourself crazy . Peak Performance Press, Inc.

Google, Global (DE, ES, FR, IT, PL, U.K., U.S.). (2019). Digital wellbeing survey (General population, 18+ years, n=97).

MacKenzie, A. (1990). The time trap (3rd ed.). American Management Association.

Roster, C., Ferrari, J., & Jurkat, M. (2016, March 16). The dark side of home: Assessing possession ‘clutter’ on subjective well-being. Journal of Environmental Psychology , 46 , 32–41. https://doi.org/10.1016/j.jenvp.2016.03.003

Rubinsteim, J., Meyer, D., & Evans, J. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27 (4), 763–797. https://doi.org/10.1037/0096-1523.27.4.763

Status and Revision History Published with Full Review on Apr 25, 2014 Published with Minor Revisions on Aug 26, 2020 Published with Full Review on Feb 19, 2024

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Importance of Time Management and 7 Tips to Improve It

Time management can be described as a skill that helps you use your time as effectively and productively as possible, especially while at work. 

But this somewhat “dictionary definition” of the term doesn’t give us the full scope of what time management actually is. Not because it is inaccurate but because time management is so much more than a single skill or ability.

Thus, let's look closely at the importance of time management.

What Is Time Management?

The importance of time management: what does the research say, 7 tips to improve time management at work.

• 1. Employ Technology
• 2. Set Clear Goals
• 3. Try out Different Time Management Techniques
• 4. Minimize Distractions at Work
• 5. Learn to Delegate
• 6. Avoid Multitasking
• 7. Take Breaks Regularly

Main Benefits of Good Time Management (At Work and Personal Life)

• 1. Higher Productivity Levels
• 2. Increased Confidence Levels
• 3. Hitting All the Deadlines
• 4. No More Procrastinating
• 5. Better Self-Discipline
• 6. Higher Motivation Levels

Time management is a combination of different strategies aimed at helping you or your employees split time between specific tasks to achieve greater productivity and efficacy, better work results, and daily, weekly, or long-term goals. 

Time management can:

• Help with your decision-making process
• Teach you how to set personal and work-related goals
• Allow you to allocate your time systematically
• Helps you organize and prioritize tasks based on importance

Effective time management not only boosts your employees' productivity and efficiency but can help you create a calm and supportive work culture. 

When employees don’t miss deadlines and complete their work obligations consistently and on time, that can create a sense of accomplishment, which can go a long way in improving their overall job satisfaction. 

This is especially true if they are praised or rewarded for their good work performance (but that’s a topic for another day).

“ Does time management work? A meta-analysis ”, a study from 2021, examined the impact of time management on work performance and well-being, and the results the researchers got were rather interesting.  

Their conclusion challenges the modern narrative that time management enhances work performance and that any benefits to our health and well-being stem from that. The study’s findings illustrate that it’s actually the other way around — time management improves well-being, and the result of that is an increase in work performance.  

A 2014 study from the University of Wuerzburg further supports this claim. This study, done on a group of students, shows that effective time management helps reduce feelings of stress and anxiety . 

With just two to four weeks of time management training, students reported a noticeable reduction in perceived stress and anxiety levels. Sadly, the study did not focus on the student’s academic results and how or even if time management had any effect on that, leaving much to be desired.

These two studies are just a small part of a larger body of research that has consistently proved the importance of time management by showing its positive effects on people’s well-being and/or work performance.

Here are some tips that can help you improve your or your employees’ overall time management.

There are numerous digital and cloud-based tools that can save you time, help you streamline tedious processes, and ultimately make you more time-efficient. 

• One of those is time and attendance software , a solution that helps you manage your employees' time, track and manage absences, quickly create and export information needed for payroll, and more.

Like any efficient tool, time and attendance software will make certain processes easier and less time-consuming. This will allow you to switch your focus to other work or personal tasks and make you more productive. 

Time-tracking software is another digital solution that can help you improve time management at work. It will allow you to manage your and your employee's work tasks while also tracking work hours, task and project progress, and more. 

Time tracking tools aim to give you all the data you need to analyze the way you or your employees spend time at work. With that information, you can identify areas that need improvement more easily, then work on them and improve either your or your employee’s overall time management.

A big part of improving time management revolves around setting clear goals and diligently working on achieving them.  

You can start working on your goals by listing them on paper, in a notebook, in a to-do list , or in a digital document (i.e., Google Docs, Word, etc.). 

After that, try to assess your goals and form a plan of action to achieve them. If that doesn’t work for you or you feel like you need more guidance, you can always try one of the many proven methods and techniques for setting goals.

One of those proven goal-setting techniques is the S.M.A.R.T method , a relatively simple yet effective way to increase your chances for success in personal development and business or project management.

To start with this method, you should:

• Set S pecific goals that are clear, defined, and focused.
• Ensure that the goals you set are M easurable and include criteria that allow you to measure progress toward achieving them.
• Make sure to set goals that are realistic and A chievable. Take into account your skills, limits, as well as available resources. Don't underestimate yourself, but also don’t overvalue your abilities.
• Make sure the goals you’re setting are R elevant to you, your core values, and your personal and career objectives.
• When you set goals, always make them T ime-bound (i.e., create deadlines for achieving goals). This will help you stay on track by creating a sense of urgency.

A good way to improve your time management at work is with proven time management techniques . They are methods used by millions of people around the world. Here are a few very popular ones:

The Eisenhower Matrix

The Eisenhower Matrix , a widely popular prioritization method, was developed by the 34th president of the United States, Dwight D. Eisenhower. 

This technique focuses on task prioritization by categorizing tasks based on their importance and urgency. The more urgent and important a task is, the higher it should be on your list of priorities.

To try it out for yourself, you should:

• Create the following four categories of tasks: Important and Urgent, Important but Not Urgent, Not Important but Urgent, and Not Important and Not Urgent.
• Evaluate your current tasks and put each of them into the appropriate category.
• You should do the tasks in the Important and Urgent category immediately.
• You should schedule a time to do the task in the Important but Not Urgent category.
• You should delegate the tasks in the Not Important but Urgent category.
• You should delete the tasks from the Not Important and Not Urgent category and not worry about them.

The Pomodoro Technique

The Pomodoro time management technique was invented by a college student, Francesco Cirillo, in the late 1980s. This method puts an almost equal emphasis on rest periods as it does on doing the actual work.  

The Pomodoro method is really simple, and if you want to try it out, you should:

• Set up a 25-minute timer and work until it goes off
• Take a 5-10 minute short break
• Set the timer again and repeat for four consecutive turns (work, short break, work…)
• After the 4th round of work, instead of taking a short break (5-10 minutes), take a longer one (15-30 minutes)

The Timeboxing Method

The Timeboxing Method is a time management technique that revolves around setting up “boxes of time” for various tasks or activities and thus limiting the amount of time you spend on them. It’s about creating fixed schedules for tasks and sticking to those schedules religiously. 

To try it out:

• Choose an activity or a task that you want to put in a timebox
• Put a strict time limit on the task or activity you chose (i.e., create a timebox)
• Do the activity until the time you’ve allocated runs out
• Adjust the timebox if the activity or task took less or more time to complete than you originally anticipated

Distractions at work are one of the biggest productivity killers. A survey by Career Builder details some of the most common work distractions that office workers face. 

Some of those distractions are:

• Texting or using cell phones
• Spending time browsing the internet
• Using or scrolling through social media sites
• Gossiping or noisy colleagues (you know who you are)

These micro-level distractions can have a huge (read negative) impact on the macro level. This means that distractions can negatively affect a company on an organizational level , leading to various issues, including:

• Compromised work quality
• Missed deadlines
• Lower morale due to other employees having to pick up the slack
• Negative effect on the manager/employee or boss/employee relationship
• Lowered revenue 

So, because micro-level distractions affect companies on a macro level, it logically stands that dealing with distractions on an individual level can prevent organizational issues . 

Whether you’re looking to minimize distractions for yourself or your employees, our advice is to try the following: 

• Turn off your phone (or at least phone notifications) when you start working and advise your employees to do the same.
• Don’t use social media at work or prevent the use of social media sites on work devices (mobile phones, computers, etc.)
• Have a set time during the day to answer emails instead of checking your email throughout the entire day. If possible, advise your employees to do the same.
• Try using earplugs if you find your work environment is too loud (can also help with chatty co-workers)

Delegation allows you to use the strengths of your employees, team members, or colleagues.

It's a good way to improve your overall time management and give yourself additional work hours to focus on more important tasks. 

According to this study and many others, delegating tasks can improve employees' job satisfaction by creating a greater sense of autonomy at work. And why is job satisfaction important? 

Simple, because job satisfaction is closely linked with productivity at work, and if high, it can drastically improve it.

So, learning how to delegate impacts not only managers but also their direct subordinates, employees, or team members. It gives managers more time to deal with higher-difficulty, higher-priority, or managerial tasks while also empowering employees and increasing their job satisfaction (which in turn positively affects employee productivity).

To start delegating, you should:

• Analyze your workload and try to identify tasks that you can delegate to others. These tasks could be standard routine tasks, tasks that take up too much of your valuable time, or tasks that you don’t have direct experience dealing with
• Make sure to choose a suitable person for the job . One of the vital, if not the most important, parts of delegation is finding the right person to delegate tasks to. Getting it wrong can nullify all the benefits of delegating, making it take up more of your time instead of freeing it up for more important/difficult tasks. When deciding on the person to delegate tasks to, consider their skills, work experience, and current workload. You can also set up short meetings with your top picks (sort of like mini job interviews), where you can discuss their skills and relevant experience in greater detail.
• Give clear instructions . Make sure to give clear instructions and provide any additional details about the tasks to the person you’re delegating them. Also, ensure they are aware of all expectations and any potential deadlines.
• Create a feedback loop and give support to the person you choose to delegate tasks to. This will help them meet deadlines and will ensure that the quality of work is not compromised.

Multitasking may seem like a good idea to increase productivity, but in practice, it does the opposite.  

Instead of allowing you to complete multiple tasks or projects, it puts most people into the position of working on multiple tasks or projects simultaneously but never finishing them (or not finishing them on time/within the deadline).

According to the study, poignantly titled “ Multicosts of Multitasking ,” working on multiple tasks concurrently can actually cause our brains to overload with information, effectively decreasing the brain’s processing power. This, in turn, lowers productivity and leads to higher chances of mistakes and, ultimately, lower quality of work.

So, instead of multitasking, try to invest your energy and time in individual tasks and work on them until they're done.

A study that analyzed the effects of micro-breaks on well-being and performance showed that regular 10-minute breaks can significantly improve one’s well-being and reduce the build-up of work fatigue.

Regular breaks are a proven method for managing and maintaining your productivity during the day. In fact, not having breaks and working long hours can lead to being overworked and can also cause burnout. Which, frankly, nobody wants.

The important thing here is to realize when you need to take a break and then simply take one . A 10-to-15-minute break is not going to hurt your work (unless you’re an ER doctor), but it can do wonders for your focus, productivity, and well-being.

There’s no better way to emphasize the importance of time management than through its many benefits. And that’s exactly what we aim to do here. Here are some of the most common benefits of good time management :

Good time management allows you to prioritize your tasks more effectively and complete them in a timely manner. 

With time management, you’ll not only be meeting all of your deadlines but also increasing your overall productivity.

The study, which examined the effects of self-esteem and time management skills on nursing students' GPA (grade point average), showed some interesting results. 

The researchers postulate a direct correlation between time management, confidence, and student’s GPA. The sample size was too small to empirically claim the connection. 

Still, the idea behind it seems logical and sound — The better someone manages their time, the more confident they’ll be and will produce better results.

Time management and deadlines are inalienably intertwined. Deadlines serve as checkpoints for time management that help you stay on track with your tasks, and never fall behind in their planning and execution.  

With time management, you can organize your work schedule and tasks in a way where you never feel overwhelmed or overworked. When you do that, meeting deadlines becomes a natural, calm part of your daily schedule.

A small study looked at the effects of time management on procrastination . Procrastination is defined as a self-regulatory issue, and the idea the researchers had was to prevent it by dealing with self-regulation problems through the use of time management. The results showed that, as expected, time management works.  

The group that used time management showed little to no signs of procrastination, while the control group (no time management) kept making the same mistakes.

This study shows that procrastination directly results from not knowing how to manage time properly. When there is no clear focus on specific goals, people almost instinctively veer into procrastination. 

On the other hand, by having clear goals and actively working on time management, people can better manage their workload, feel in control of it, and ultimately stop procrastinating.

A study done on a group of high-school athletes had some interesting findings, with one of those being particularly riveting (at least to us) — the researchers found a direct correlation between self-discipline and time management. 

The higher the self-discipline is, the better the athletes are at managing their time. From there, it’s easy to postulate the opposite; the better someone is at time management, the better they’ll be at self-discipline.

Finding motivation is a big problem for a lot of people. The same goes for managers and team leads who are always looking for new ways to motivate their employees. But what if the answer was so obvious that it was staring everybody in the face the whole time? Yes, we’re talking about time management. 

A 2020 study shows the positive effects of learning time management and self-discipline skills on students' motivation for learning. And, if time management can motivate students to study more, in this day and age, imagine what it can do for you, your employees, and ultimately your business.  

As you can see, there are many reasons to pay attention to the importance of time management. Wise use of time is essential if you or your company want to be more successful.

How to create a productive and organized working place, where people love to perform

Working remotely from home – how to do it right, how time and attendance tracking helps with employee engagement, time clocking: how and when to use it, what is workforce optimization.

Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution

• Open access
• Published: 02 September 2024
• Volume 3 , article number  135 , ( 2024 )

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• Debbie A. Sanders 1 , 2 &
• Shirley S. Mukhari 1 , 3  

In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment.

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1 Introduction

Over the past few years there have been notable advances in supporting lecturers to enhance their teaching methods, and in improving students’ learning experiences through the adoption of blended learning. Defined as a combination of face-to-face (F2F) and online learning, blended learning offers more flexible learning experiences that are also deemed to be more effective. Also known as "brick-and-click" instruction, hybrid learning, dual-mode instruction, blended pedagogies, or HyFlex, targeted, multimodal or flipped learning [ 5 , 38 ], this approach is becoming increasingly popular. The approach, which combines traditional classroom F2F learning with online components, facilitates the application of asynchronous teaching and learning in educational settings [ 16 ]. In recent years, educational institutions have widely embraced blended learning as the preferred teaching method, expressing appreciation for its flexibility, timeliness, and uninterrupted learning opportunities. As hybrid learning gains popularity, so it has become increasingly important to find new ways of improving the effectiveness thereof [ 17 ]. Recent developments in artificial intelligence (AI) are one way of enhancing the efficacy of blended learning approaches. With the integration of AI into academic environments, individualised learning experiences can be provided, administrative tasks can be automated, and such systems can be adapted to student needs [ 20 , 44 ]. For these reasons, the researchers sought to understand lecturers’ views on the relationship between AI and blended learning, as those perspectives are crucial for developing effective teaching and learning practices in higher education contexts.

AI involves the study and development of computer programs that display ‘intelligent’ behaviour, mindful of the fact that machine intelligence is distinct from the natural intelligence that is inherent in humans and animals. Other definitions of AI examine efforts to enable computers to possess intelligence [ 19 ]. Ultimately, AI extends much further than just robotics, however, to include the human capacity to program computers and other technology-enabled devices, so that they comprehend the principles of intelligent thought and behaviour. As a key invention of the Fourth Industrial Revolution (4IR), AI is considered one of the most influential technologies of our time [ 19 ]. For the purposes of this research, AI will be taken to refer to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem solving, and decision making. From the point of view of lecturers, such integration would require them to adapt their teaching and learning approaches, to make them more efficient and effective in addressing diverse student needs [ 25 ].

It is against this background that the researchers felt the need to investigate what impact AI has on blended learning, which includes lecturers having to revisit the way in which they usually lecture (the educator teaching, and students listening and regurgitating what they have been taught), to scenarios where AI is infused into a hybrid learning approach. It is crucial to emphasise that, in the context of this paper, blended learning is deemed to comprise more than the mere incorporation of technology into an academic programme. The adoption of the term, in this instance, aligns with what Lee [ 24 ] describes as a hybrid teaching approach, integrating traditional F2F lecturing with the latest, updated technologies. This mode aims to enhance student success and promote the relevance of the course content. Interaction among students, and between lecturers and the student cohort, is accomplished through various internet-enabled learning technologies, including platforms such as online discussion forums [ 3 ]. These technologies play a crucial role in promoting communication between educational stakeholders. Consequently, the smooth integration of conventional classroom instruction with e-learning offers valuable support for students’ asynchronous and collaborative learning [ 15 ]. In addition, the use of AI supplements these interactions by providing personalised feedback, allowing for two-way discussions, and for learning resources to be adapted to individual students’ needs. This combination of traditional and e-learning environments through the adoption of AI technologies makes for a more engaging and effective educational experience. It improves educational access, and promotes inclusive and equitable education, resulting in a sustainable, efficient, and accessible system of blended learning [ 3 ].

Although blended learning is not a novel concept, its use has remained largely unchanged. Its numerous challenges require further and more in-depth research into its efficacy [ 5 , 38 ]. Various aspects, including the specific technological tools and learning approaches used, and the overall quality of the teaching and learning on offer, need examination [ 5 ]. While blended learning has long been used as an approach to enhance students’ learning experiences, much of the research has focused on countries in the Global North, such as Belgium, the United Kingdom (UK), and Italy [ 6 ]. Limited research has been conducted in the South African context in this regard [ 43 ]. Notably, a search on Google Scholar revealed that only minimal related research has been published in the past 5 years (only eight research resources), with none of them originating from South Africa. Despite the increased uptake of hybrid learning in academia, AI is often perceived as a separate technological tool with limited influence on teaching and learning approaches. To enhance the effectiveness of blended learning in higher education contexts, it is essential to identify and understand lecturers’ views on the incorporation of AI into their teaching and learning, taking into account the SAMR model [ 34 ].

The significance of the study thus lies in elucidating lecturers’ viewpoints on the impact which AI and blended learning have on teaching and learning. The researchers also set out to assist higher education institutions (HEIs) in creating, adapting or changing conditions so that they are more relevant and meaningful, and ultimately enable lecturers to ensure that students are more successful in achieving specific learning outcomes. Clearly, AI is a tool that must be embraced in this modern, ever-evolving technological world.

The main research question designed to guide the study, was:

How do lecturers perceive the influence of AI on blended learning in the context of a South African higher education institution?

Four sub-questions were also formulated in this regard:

How do lecturers in South African higher education institutions perceive and integrate AI technologies into blended learning lessons within the SAMR framework?

What challenges do lecturers identify when incorporating AI into blended learning lessons, considering the SAMR levels of substitution, augmentation, modification, and redefinition?

How does AI influence student engagement, interaction, and learning outcomes in blended learning environments?

What support mechanisms do lecturers require to ensure the successful incorporation of AI into blended learning lessons?

Following the above introductory discussion on lecturers’ perspectives on blended learning and AI integration, the sections which follow focus on a comprehensive literature review on the topic, the theoretical framework chosen for this research, an exploration of the selected research methodology, the findings, and recommendations for the successful implementation of blended learning infused with AI. Lastly, concluding remarks summarise the key findings, and outline implications for future research and educational practice.

After exploring the background and rationale for this study, it is crucial that this study examines the existing body of research related to blended learning and Artificial Intelligence integration in higher education which is the focus of the next section.

2 Literature review

2.1 blended learning as an approach to teaching and learning.

In recent years, the educational domain has experienced significant transformations, driven by the continued evolution of information technology. One notable outcome is the emergence of blended learning, a pedagogical approach that integrates diverse methods of delivering information, such as web-based software courses, coupled with the management of practical knowledge [ 33 ]. According to Damanik [ 12 ], Choi and Park [ 10 ], and Qiu et al. [ 35 ], blended learning can be implemented both on- and offline. Bozkurt [ 8 ] expands on this, emphasising that blended learning encompasses F2F interactions and online engagement through specific mediums. The positive impact of the blended learning model on students’ learning outcomes, through fostering heightened engagement, is echoed by Santosa et al. [ 39 ]. This model, as observed by Nugraha et al. [ 31 ], also enhances students’ problem-solving abilities and understanding of the module content. This ensures adaptability and flexibility that caters to individual students’ needs, preferences, and schedules [ 43 ]. While initially designed for specific modules and their content, this approach prioritises student-centred satisfaction [ 43 ], thereby supporting HEIs in pursuing their goals and ultimately achieving the successful attainment of the learning outcomes set [ 38 ]. At its core, the concept of blended learning is built on the understanding that learning is not a singular, isolated event, but rather an ongoing, continuous process [ 33 ]. This transformational shift aligns with the modification level of the SAMR model, as it goes beyond merely substituting traditional teaching methods with technology, instead modifying the entire learning experience. Admittedly, the development of efficient blended learning systems can be demanding, particularly in respect of their endurance and flexibility to adapt to modern technological developments [ 3 ].

The integration of Artificial Intelligence (AI) in blended learning environments has been the subject of increasing debate in recent years. A review of the literature reveals that while there are some global studies completed which have explored various aspects of AI in education, research originating from South Africa is notably sparse. Alshahrani [ 3 ], Ferry et al. [ 13 ], and Rahman et al. [ 37 ] have all examined the impact of AI on student engagement and learning outcomes in blended learning, highlighting the potential benefits of AI-driven feedback and personalised learning experiences. However, research from a South African context is underrepresented, which may limit the generalisability of these findings to local settings. This gap highlights the need for more region-specific studies, particularly in HEIs.

The year 2017 marked a significant milestone, with extraordinary and unique developments in our understanding of the possibilities of the merging of technology and AI. As a rapidly advancing field, AI has the potential to influence the future of information technology and, for this reason, training in that regard is imperative [ 33 ]. The study of AI is fascinating and intriguing, representing the future of information technology. AI has the potential to enhance people’s lives by ensuring that tasks are accomplished more rapidly and more accurately. Petrova [ 33 ] suggests that soon AI will be integrated into all platforms and technologies, across different spheres. This development represents a shift toward the redefinition level of Puentedura’s [ 34 ] SAMR model. It transcends the traditional roles of both lecturers and students and introduces new possibilities for teaching and learning through the use of technology. While there is still substantial work ahead, AI empowers lecturers to achieve more—and with greater efficiency—than ever before. In the past, AI was a technology that instilled fear in many. The notion that computers could think and learn like humans raised concerns about our ability to comprehend and constrain machines. However, as we move away from the pursuit of human-like AI, we can now view its progress as a tool serving to develop and enhance every industry [ 33 ].

AI stands out as a potential answer to improve the efficiency and durability of blended learning systems [ 3 , 23 ]. Through the use of AI techniques such as machine learning (ML), natural language processing (NLP), and chatbots, opportunities are created which allow for the automation of diverse features of the learning journey, including content delivery, assessment, and feedback [ 3 , 22 ]. Furthermore, AI allows for the customisation of the learning experience for individual students, ensuring increased engagement and enhancing learning outcomes [ 3 ]. The fact that AI makes it possible for lecturers to adapt and automate their teaching, represents a change in traditional teaching and learning methods, aligning with the substitution as well as modification levels of the SAMR model, as technology can be used as a direct substitute for conventional teaching and learning methods, while also accommodating or revealing new capabilities. It offers a vast range of new possibilities to help ensure the successful achievement of a module’s learning outcomes—something that was not possible with conventional approaches.

It became clear to the researchers that while relevant, limited studies on this theme exist in South Africa, Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Moreover, there is a critical need for research that addresses the localised implementation of AI-driven blended learning solutions, particularly in rural and under-resourced areas where access to technology is inconsistent [ 27 ]. Such studies would provide valuable insights into how AI can be utilised not only to enhance learning outcomes but also to bridge the educational disparities that persist across different regions of the country. While limited, some relevant studies do exist. Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Integrating AI into blended learning systems offers the potential to establish an education system that is not only efficient, but also sustainable. The use of AI in education, particularly in blended learning, revolves around delivering personalised learning experiences, and optimising course delivery [ 3 ,  2 , 24 ]. Through the adoption of AI technology in hybrid learning systems, it is easier for lecturers to analyse student performance data for a personalised learning experience which aligns with individual strengths, weaknesses, and interests [ 3 , 25 ]. The implementation of AI in blended learning streamlines tailored assistance for students. Alshahrani [ 3 ] concurs that AI allows for responsive interaction. This corresponds to the augmentation (A) level of the SAMR [ 34 ] model, where technology is used to improve the learning experience, exceeding what was achievable with traditional methods. Personalised support can easily be based on individual student needs. The personalised approach assists students in navigating complex concepts, thus helping to ensure the achievement of learning outcomes, and ultimate success.

AI is also conducive to enhancing teaching and learning methods, increasing efficiency through automated administrative tasks, and refining content delivery. Introducing AI tools to ensure a sustainable and efficient blended learning system allows lecturers to lessen the strain on the environment, by reducing paper usage and minimising the carbon dioxide emissions associated with physical (F2F) lectures or meetings. This not only improves educational effectiveness and accessibility, but also empowers students to acquire the essential knowledge and skills for building a sustainable future [ 3 ,  36 ].

Viktorivna et al. [ 40 ] point out that AI serves to enhance student engagement, and the effectiveness of their learning. AI also facilitates a more straightforward explanation of subject matter [ 32 ], thereby encouraging students to develop and enhance skills required in the twenty-first century [ 11 , 42 ]. AI is a valuable educational resource for blended learning, as it grants access to an ever-expanding range of learning materials. Furthermore, AI helps in the creation of lessons, quizzes, and rubrics which allow lecturers to reorganise the curriculum and content of a module. AI-generated resources can even be customised to align with students’ instructional preferences, thereby fostering a flexible and inclusive learning experience [ 3 ].

Various studies have shown that the infusion of AI in a blended learning module enriches the learning process for students, helping them attain specific learning outcomes [ 13 , 14 , 37 , 39 ]. The collaborative and conversational capabilities of AI enhance the overall learning experience, resulting in an enjoyment of the course and heightening active participation among the student cohort. Concerted engagement delivers improved learning outcomes, and a more profound understanding of the subject matter [ 3 ]. This aligns with the Augmentation (A) level of the SAMR model, as technology (AI in this instance) goes beyond merely enhancing traditional methods, to develop a more interactive and engaging learning environment, thus fostering increased student participation and leading to a better grasp of the subject matter.

The AI-based blended learning model boosts students’ digital literacy levels as well as their 21st-century thinking skills [ 37 ]. This innovative approach helps to improve their critical thinking skills, for use in the learning process [ 18 ]. Ultimately, models can be created using a variety of AI-based technologies, thereby saving lecturers time and enhancing students’ learning opportunities [ 37 ].

In higher education, large class sizes make it difficult for lecturers to offer individualised teaching and can impede swift and direct student support. AI negates this challenge by rendering personalised support. As such, AI delivers real-time answers and support, easing the workload on lecturers and enriching the learning experience. The rise in popularity of AI has initiated extensive discourse and research regarding its potential influence in the education sector, particularly in higher education where limited lecturer–student ratios present unique challenges [ 3 ]. Thus, it is clear that AI serves as a valuable asset in blended learning.

AI also enables the delivery of customised support, feedback, and motivation to students. Investigating these aspects will further our understanding of AI’s integration in blended learning, unveiling fresh insights to guide the design, implementation, and ethical use of related technologies in educational environments that adopt a hybrid learning approach [ 3 ]. Since this is a relatively new technological development and thus a relatively novel approach to teaching and learning, further research is needed, especially at HEIs, to analyse the exact impact on students’ performance. As Rahman et al. [ 37 ] concur, this approach needs further development. This research paper extends the current knowledge base in the field of blended learning, particularly in higher education, by providing insights into the integration of AI for enhanced student literacy, thereby filling a significant gap in the existing literature. Closing this gap will not only expand our understanding of emerging educational practices, but also provide valuable insights for educators, institutions, and policymakers aiming to optimise the student learning experience.

The follow section reviews the theoretical framework that guided this research.

3 Theoretical framework

For this research, the Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model was selected, to establish a solid foundation for investigating intricate aspects of AI’s influence on blended learning in HEIs. The model was chosen for its applicability to an understanding of the transformative impact of AI on blended learning, within the South African higher education milieu.

As per Puentedura’s [ 34 ] SAMR model, digital technologies can either enhance or transform educational practice. Enhancement involves substitution without functional change, or augmentation with functional improvement. Transformation, by contrast, requires significant task redesign or redefinition, leading to the creation of new tasks that were previously inconceivable. The model, which explores the creative application of technology to enhance the learning experience, serves as a useful guide for lecturers facing pedagogical changes as a result of using new learning technologies in their courses [ 30 ].

The SAMR model comprises four hierarchical levels. Firstly, the substitution level in which technology is used as a direct substitute for a traditional tool, with no functional change. At this level, the lecturer is tasked with substituting an older technology to perform the same activities as previously. While this may set the stage for future development, it is unlikely to have a significant impact on student outcomes at this stage [ 30 ]. The second level, augmentation, prompts lecturers to consider whether or not the available technology improves their teaching and learning. Instead of merely observing how students performed a given task before, lecturers must now focus on specific features of the technology, to accomplish the task more effectively, informatively, and swiftly. This approach aims to enhance students’ performance in completing assigned tasks [ 30 ]. Thus, technology acts as a direct substitute, with some functional improvement. The third level is the modification level in which technology allows for significant task redesign and, during modification, the lecturers’ objectives are to successfully achieve lesson outcomes with technological assistance. Teaching methods are thus adapted to ensure the incorporation of technology. While the syllabus remains unchanged, teaching approaches are modified to enable students to attain new goals that were previously deemed challenging [ 30 ]. The final level of redefinition empowers lecturers to replace older teaching techniques with newer, more effective teaching ideas. This is achieved through the use of technology, which allows for the creation of tasks once deemed inconceivable [ 7 ]. These teaching methods mainly seek to capture and retain students’ attention [ 30 ].

The SAMR framework enhances the value of the accumulated data, by offering a decision-making model for assessing the design of research interventions. The lowest levels—substitution and augmentation—encourage participants to actively engage, thus overcoming challenges related to technology, pedagogy, and their consequences. At the higher levels—modification and redefinition—the design of research questions becomes crucial for considering potential challenges in participants’ understanding of increasingly complex topics. This approach aims to purposefully overcome obstacles associated with the evolving nature of the scheduled tasks [ 4 ].

The application of the SAMR model in the context of this research involved a comprehensive examination of how AI influences blended learning practices. At the Substitution level, the study explored how AI replaces or replicates traditional teaching methods, offering insights into its role in directly substituting conventional approaches. Moving to the Augmentation level, the research assessed how AI enhances or improves existing educational practices, particularly in terms of providing additional features or functionalities that support teaching and learning. The Modification level focused on analysing how AI introduces significant changes in the execution of educational tasks, transforming traditional methods into more dynamic and effective practices. Finally, at the Redefinition level, the study evaluated how AI facilitates entirely new and transformative educational practices that were previously unattainable, showcasing its potential to revolutionise blended learning environments in ways that were not possible before.

Using this framework ensured that the research could follow a systematic approach to assessing the influence AI exerts on blended learning. It allowed the research to progress from simple enhancements to transformative changes. By offering a structured method for assessing the extent of AI integration in various facets of teaching and learning, the researchers gained valuable insights into the evolving landscape of educational technologies.

It is against this background that the chosen methodology is discussed next.

4 Methodology

4.1 research approach.

This study applied a qualitative methodology to investigate lecturers’ views on the influence AI has on blended learning. A qualitative approach involves a thorough exploration and grasp of phenomena, using non-numerical data and highlighting context, meanings, and subjective experiences [ 21 ]. The researchers deemed this method best suited for its exploratory nature of extracting relevant information. Focus group interviews were conducted, as Islam and Aldaihani [ 21 ] suggest, to allow for the coordination of discussions among a small group of participants, to mine and gather their views on a specific topic or phenomenon. For this research the focus was on the modules, lessons, and assessments of the participating lecturers. In addition, the researchers employed document analysis, which enabled them to explore the actual course content, lesson plans, and discussion forums.

In this way, the researchers arrived at an in-depth understanding of the specific phenomenon under investigation, as proposed by Morgan [ 29 ]. This approach enabled the researchers to scrutinise the lecturers’ experiences and opinions, focusing on their knowledge of, and encounters with, AI and blended learning.

The researchers applied a singular case study research design. This involved focusing on a single participant or unit of analysis, for an in-depth exploration of the intricacies and dynamics of a specific case [ 1 ]. This approach made it possible to conduct a thorough examination of the perceptions of lecturers employed in the College of Education at the HEI in question. The choice of design was prompted by ongoing developments in both AI and blended learning, which enabled the researchers to gain insights from lecturers actively engaged in related emerging educational practices.

4.2 Population and sample

Identifying a population for a particular research study enables the researchers to gather pertinent information from a smaller representative sample. This ensures that each distinct element of the collected information with similar characteristics is given the opportunity to be part of the sample. The researchers opted to employ a homogeneous purposeful sampling technique, intentionally selecting a group of participants who shared specific characteristics or traits deemed relevant to the research objectives. Participants were thus chosen based on shared traits, including gender, age, years of experience, the college in which they lectured, and their use of AI and blended learning, in order to align with the study’s purpose and objectives (Table 1 ).

Here, the group of participants selected were part of the same college at the specific HEI. The criteria for selection encompassed their approachability, availability to actively participate in the study, responsiveness to the interview questions, and willingness to share the content of their modules, lessons, and assessments. For this study, 15 lecturers agreed to participate: two males and 13 females, ranging in age between 32 and 63. Importantly, age has an impact on a user’s acceptance and embrace of AI in teaching and learning. Older lecturers often express discomfort with new technology adoption, and tend to be resistant to change. They are usually more comfortable with traditional ways of teaching and are fearful of using cutting-edge technological innovations. The participants’ readiness to openly share their course content, lessons and assessments, assisted the researchers in effectively analysing the collected data through the chosen document analysis data-collection technique. Consequently, the participants contributed valuable information that enhanced the depth of the study. Their active involvement in university affairs (especially the teaching and learning programmes) provided information that was highly relevant to this research .

4.3 Data collection

For this study, data were acquired by conducting interviews with the participating lecturers, enhanced by document analysis (see appendices A and B). The application of these data-collection techniques enabled the researchers to gather pertinent insights into the lecturers’ practical encounters with AI and blended learning in their teaching and learning. The use of open-ended, semi-structured interviews, along with document analysis, facilitated the analysis of the data, thus ensuring a thorough and precise in-depth study of the subject matter. The thematic approach adopted in this research aimed to pinpoint repeated topics identified in the data gathered. This enabled the researchers to concentrate on emerging themes specific to the realm of AI and blended learning, rather than providing mere synopses of the data [ 9 ].

4.4 Data analysis

To gain valuable insights from the participants’ answers to the interview questions, and information derived from the document analysis, a thorough study and interpretation of the collected data was imperative—an analytical process which is crucial for answering the research questions effectively. The researchers actively engaged in interpreting, consolidating, and synthesising the lecturer participants’ statements, to assign meaning to the data. This involved transcribing, comparing, and scrutinising the interview responses, along with the content of the modules, lessons, and assessments. The participant responses were coded manually, using letters of the alphabet, to ensure anonymity. Each response was tagged with a corresponding letter, making it possible to trace every piece of data back to the specific participant who supplied it. Each statement was carefully linked to specific codes and themes, especially given the fact that AI does not replace F2F lecturing, but rather augments teaching and learning. The coding process involved categorising data into the SAMR [ 34 ] levels, to reach conclusions about how lecturers perceive AI's influence on different aspects of blended learning.

The thematic approach was used to identify patterns and themes in the data, which were then related back to Puentedura’s [ 34 ] SAMR model. This allowed for a comprehensive review of how AI is being used at different levels of integration in a specific hybrid learning environment. An inductive approach, specifically axial coding, was followed to analyse the data collected. This involved a systematic comparison of the gathered data to identify codes, categories, and subcategories. A natural analysis of the data, without preconceived notions, was achieved by using an inductive approach, which enabled an unbiased analysis of the lecturers’ actual experiences. Through this comparative analysis, the researchers aligned the collected data with information derived from the literature review. The adoption of these methodologies facilitated the analysis of findings, reinforcing the credibility and reliability of the data. The theoretical justifications for this approach included grounding the findings within the SAMR framework, to enable the data-analysis process to align with the study objectives and research questions throughout.

4.5 Trustworthiness in data collection and analysis

Ensuring the credibility and trustworthiness of research findings is the prerogative of every qualitative researcher. In this study, the researchers developed a lasting, reliable, and open relationship with the participants. This approach guaranteed the latter’s willingness to actively participate in the study, and to share their personal experiences of the impact which AI has on blended learning. Moreover, the lecturers were encouraged to review and offer feedback on the researchers’ summary of the interview responses, further confirming the accurate representation of all data, and strengthening the trustworthiness of the research.

The coding process for this study was primarily conducted by Researcher A who began the initial coding of the qualitative data, identifying preliminary themes and patterns. To enhance the reliability of the analysis, researcher B participated in the second phase, where both researchers reviewed and validated the initial codes and themes. This collaborative approach involved both open coding and axial coding and ensured a thorough and unbiased interpretation of the data. A critical reader provided feedback and suggestions, which helped refine the coding framework and resolve any discrepancies. This process promoted credibility by introducing different perspectives, which prevented individual prejudice and improved the accuracy of the data interpretation. Transparency was achieved by clearly documenting each researcher’s role and contributions, making the process open to scrutiny and validation by other future researchers. The method ensured the reliability and comprehensiveness of the data analysis and actual results.

The researchers adhered strictly to qualitative research principles, ensuring transparency in their data-collection methods and meticulousness in their data-analysis techniques. Participants were continually asked to check the researchers’ notes, interpretation of the interviews, and transcriptions (member checking). Detailed descriptions of the participants’ experiences were provided to enable the transferability of the findings. This precise approach guaranteed the reliability and validity of the findings. By integrating the findings from the interviews, document analysis, and literature review, the validity and trustworthiness of the conclusions were further enhanced. Through this methodological approach, the researchers ensured the trustworthiness of the research findings and were able to make informed recommendations based on the results reported on here.

4.6 Ethical issues

The chair of the department in which the research was undertaken, obtained comprehensive ethical clearance covering the entire department from the Research Ethics Review Committee of the College of Education of the particular HEI. This clearance authorises all researchers in the department to conduct research within the institution, under ethics clearance number 90060059MC.

In ensuring that the highest ethical standards were maintained, the researchers pledged to use codes to protect the identity and privacy of the participating lecturers. The lecturers were also required to give the researchers permission to record the interviews, and to analyse their module content, lessons, and assignments. They were explicitly informed that their participation was voluntary, and that they were free to withdraw from the study at any stage without fear of penalty.

4.7 Research findings

Here, the researchers summarise the outcomes of the research based on insights derived from the responses provided during the interviews with the participating lecturers, and the document analysis. The findings are organised to address the main research question and sub-questions.

4.8 Lecturers’ perceptions of incorporating AI technologies in their blended learning and teaching approach

Of the 15 participants interviewed, 12 reported using AI to ensure that student queries were answered, and that they could find additional information as required, thus personalising the entire academic journey. In the words of Lecturer H:

I use AI in my modules to ensure that students can easily obtain answers to their questions. It is an amazing tool which helps suggest supplementary resources based on students' progress. This ensures a learning experience which is better, as it is adapted to my students’ progress.

Lecturer C corroborated this:

These systems can answer questions, provide information, and simulate conversation, creating an amazing and enjoyable interactive environment.

The same 12 lecturers deemed AI very useful for facilitating discussions between lecturers and students, and students amongst themselves. This was achieved because AI streamlined communication, enhanced interaction, and provided valuable support. Lecturer F said:

AI has significantly improved communication channels; it allows me to develop interactive and engaging discussions between students and between students and myself, and even encourages students to discuss the course content amongst themselves.

Lecturer H concurred:

The use of AI chatbots has created a space for students to collaborate effectively. This offers immediate assistance and helps develop a sense of collaboration in our blended learning environment.

All the interviewees maintained that the use of AI to generate relevant and customised learning materials and assessments was a very useful feature that could easily be adopted in blended learning modules. In this regard, Lecturer C said:

I use AI to create customised learning materials, quizzes and even games that align with the specific learning outcomes of my modules.

Lecturer G stated:

I find that the fact that AI can create adaptive assessments that adjust difficulty levels based on the individual performance of my students, is very useful.

Five participants highlighted the value of AI for translation. This was considered extremely useful, particularly in the South African context with 11 official languages. Lecturer M explained:

The ability of AI to facilitate translation greatly benefits our students from diverse backgrounds. It is so easy for any of us [lecturers and students] to quickly translate a word or even a whole paragraph, which makes the understanding of the module so much easier.

Lecturer H added:

I find that it helps students who are more comfortable in their home language to participate in the course content. This ensures that learning materials are accessible to everyone, regardless of their language preference.

The researchers’ document analysis showed that lecturers who mentioned the benefits of AI for creating customised learning materials and adaptive assessments had indeed merged these elements into their module sites. This correlated with the findings obtained from the interviews, where 12 of the 15 participating lecturers highlighted the positive impact AI had on facilitating communication, enhancing interaction, and offering support in hybrid learning environments. In addition, the analysis revealed instances where AI tools were used to support F2F classes by providing real-time feedback and interactive activities, thus enriching the blended learning experience. Using technology to individualise learning experiences and adapt teaching strategies in real-time helps students adapt to such approaches, thereby supporting traditional teaching methods and enriches learning environments.

Lecturer C had integrated AI-generated, scenario-based case studies into the course material. The document analysis revealed a scenario related to cultural integration through language teaching and learning. Students were presented with a case study involving a classroom with learners from diverse linguistic and cultural backgrounds. They were tasked with designing a language lesson that not only focused on language acquisition, but also promoted cultural understanding and integration. AI was used to evaluate the students’ answers to the case study. Based on individual performance, the system provided feedback to each individual student, and suggested additional resources or challenges to focus on specific areas of improvement in designing the language lesson.

The document analysis (as outlined in the second criterion, which aimed to “examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes”) also ascertained the presence of adaptive assessments that were able to adjust complexity levels based on individual student performance. Lecturer G, who felt that AI was beneficial for creating such assessments, had incorporated quizzes with dynamic difficulty levels into the module site. Students were able to complete personalised assessments, with questions based on their previous performance.

The researchers noted the integration of AI-based translation services. Lecturer M, who highlighted the value of AI for translation, had implemented an AI-driven language translation tool on the module site. The researchers noted that some students had translated sections of the course content into their preferred language, promoting inclusivity and ensuring that the specific learning materials were clear to everyone, regardless of their language preference.

4.9 Lecturers’ perspectives on the challenges of incorporating AI into blended learning

Four of the lecturers interviewed, described the adaptation of new methods of teaching and learning, when using AI in their blended learning modules, as a challenge. In response to interview question 5 (What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?), Lecturer H commented:

Incorporating AI into my modules requires a delicate balance. I found that at times AI tends to minimise the importance of traditional teaching and learning methods, and not actually enhance them.

Lecturer B said:

Finding the right blend is crucial, so students benefit from the best of both worlds. AI must enrich my module and definitely not disrupt it … [We have to find] a balance between the technology and the personalised touch.

Lecturer C indicated:

… it can be a challenge to decide exactly where AI should be incorporated into the actual content of the course. Determining this often requires me to rethink my learning outcomes and approaches to teaching the content of my modules.

Twelve participants expressed the view that resistance to change was a major impediment to the successful adaptation of AI in blended learning modules. This aligns with responses to Interview Question 9 (In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?) where Lecturer G noted:

Change is always met with resistance, especially when it comes to technology, particularly amongst us older lecturers. Some may see AI as a threat to the traditional way of teaching.

Lecturer H stated:

There's a comfort in the familiar, and AI represents a significant shift. Overcoming resistance requires effective communication. It also requires practically exploring the uses and benefits of AI.

All the participants mentioned that, although AI definitely saved time, problems were experienced with finding additional time to investigate new technologies and adapt their modules accordingly. In the words of Lecturer A:

While AI streamlines certain processes, the challenge lies in actually finding dedicated time for exploring its full potential to ensure that AI helps both me and my students successfully achieve the outcomes of the specific module.

Lecturer G mentioned:

Despite the efficiency AI brings, we must confront the reality of time constraints. It is essential to find a balance between adopting new technologies and meeting existing teaching demands.

Eight participants mentioned that it was becoming increasingly challenging to cope with the problem of the “digital divide”, which pertains to the technological proficiency of the students. Lecturer E noted:

There's a noticeable difference in access to technology among our students, and it's becoming increasingly challenging for us lecturers to bridge this gap as a result of the fast pace of new technological developments.

Lecturer F concurred, adding:

The issue of unequal access is growing. We need effective strategies to ensure all students are given equal learning experiences, regardless of their experience using computers for actual learning.

Several lecturers discussed ethical and privacy-related challenges with regard to the integration of AI in their blended learning module. As Lecturer F indicated:

I find that a huge challenge is that of ethical considerations, especially with regard to the privacy of student data. Finding the correct balance between using AI and protecting our students' privacy is an ongoing challenge. Additionally, there's a need for clear guidelines from management on how AI should be used ethically in our teaching, to avoid unintended consequences.

Lecturer G opined:

The challenge lies in providing the benefits of using AI to achieve the outcomes of our modules without compromising the privacy rights of our students. Open discussions on ethical guidelines and continuous awareness among lecturers and management as well as lecturers and students [are] essential to overcoming these challenges successfully.

4.10 Lecturers’ perspectives on AI's impact on student engagement

All 15 participating lecturers noted that using AI in their blended learning modules was beneficial, but not all believed they were using AI to its full potential, admitting there was room for improvement. Lecturer F stated:

While AI has enhanced certain aspects of my lecturing and interaction with my students, I really feel that there's much further potential for the use of AI in my modules, especially with regard to the advanced AI functionalities and typing in the correct prompts.

Lecturer O opined:

Integrating AI into blended learning helps me improve the actual teaching of the content of my modules. This allows me to individualise the learning experiences of each of my students, to ensure that their needs and preferences are met.

Lecturer A agreed:

Using AI in my blended learning course helps me adapt to my students' needs. This makes the teaching and learning much more flexible and meaningful, as it allows me to develop an individualised teaching approach to each student's strengths and weaknesses.

Nine of the participants highlighted the significance of AI’s prompt feedback to the inputs provided and queries posted on the AI system. In response to interview question 6 ("Have you received any feedback from students regarding their experiences with AI-infused blended learning?"), Lecturer B mentioned,

The quick feedback of AI has really changed the learning experience. Students receive real-time feedback [on] their progress, allowing them to make [the] necessary changes immediately.

Lecturer K echoed this:

I see AI as a game changer. Its ability to offer instant, personalised feedback has been a real […] eye-opener. It helps students understand their strengths and weaknesses without delay. This helps ensure a more integrated and authentic learning environment. It helps in identifying gaps in understanding and adapting teaching strategies.

Lecturer N concurred, adding:

From where I stand, AI's ability to analyse student data can provide valuable insights for personalised teaching and learning, and allows for instantaneous feedback. As a result, students' entire learning process is enhanced, resulting in an improved ability to achieve their learning goals.

Lecturer B, who viewed the instant feedback of AI as beneficial for enhancing teaching and learning, had used AI to create scenario-based feedback activities. The document analysis identified instances where students were presented with virtual scenarios representing diverse language teaching situations, such as classroom settings, one-on-one tutoring sessions, and language immersion programmes. AI was able to instantaneously analyse students' responses and actions in each scenario, providing immediate, real-time personalised feedback on their answers. This integration of AI thus enhanced both asynchronous learning and synchronous F2F interactions, by offering immediate feedback during live sessions.

The interview responses of seven of the participants revealed that AI is able to easily automate administrative tasks, through machine learning algorithms and natural language processing. This analytical capability allows instructional approaches to be adapted to individual student needs, ensuring that they successfully attain the learning outcomes of the module. Lecturer C said:

AI tools can streamline administrative tasks, allowing me to devote more time to my students and support them, especially where they are encountering challenges.

Lecturer F added:

I've used AI to analyse student performance data, which helps me adapt the content of my modules and teaching methods to make them more interactive. This can easily be based on my individual students’ needs.

The document analysis, which aimed to examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes (the fourth criterion on the document analysis) also showed that modules where AI was integrated into feedback mechanisms saw improved student engagement. Studying the module site of Lecturer F, the researchers discovered that s/he used AI to automatically grade assignments (multiple-choice and written) and give immediate feedback. The reports generated were instantaneous and showed specific trends which helped the lecturer adapt the teaching and learning of this particular module.

It is indeed important to note how AI supports F2F teaching in class. As a result of this approach, learning during live lectures is made more dynamic and responsive to student needs. This point was highlighted by Lecturer M, who said:

The use of AI tools allows for instantaneous feedback to my students’ questions during lectures. It can give them various suggestions for additional materials and let them engage in interactive activities during face-to-face classes that will allow them to engage more deeply with the material.

4.11 Lecturers’ perspectives on the support they require to successfully implement AI in blended learning

All the participating lecturers confirmed the importance of comprehensive training and professional development. The need for comprehensive training and institutional support emerged as a critical theme. Interview Question 8 ("What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?") prompted responses highlighting the importance of ongoing professional development. In the words of Lecturer G:

Access to ongoing professional development courses focused on AI is essential for us lecturers to keep up to date with the latest developments in this field.

Lecturer M noted:

Professional development should include […] theoretical knowledge of AI as well as, specifically for us, its practical application in blended learning contexts.

Four participants stated that technological support was imperative if AI was to be instituted successfully. Lecturer O suggested:

Dedicated support teams must be specifically set up to assist with any technical challenges we may come across during the implementation of AI into our teaching and learning. This includes prompt responses to technical glitches and troubleshooting, to ensure that everything works properly for both me and my students.

We need assistance with initial setup and implementation, and with ongoing technical issues that may arise. This could be problematic as our IT help desk is already so overburdened. More IT staff definitely need to be employed.

Having institutional support for incorporating AI into the curriculum, is crucial. This involves not only providing resources, but also creating a culture that values and encourages the integration of AI technologies into teaching practices. This was echoed by all the lecturers interviewed. In the words of Lecturer A:

Having institutional support for incorporating AI into the curriculum is crucial. This involves providing resources as well as creating an institution that values and encourages the integration of AI into our teaching and learning.

Lecturer O echoed this:

Institutional commitment is key to the successful integration of AI. This should also include dedicated policies, so that we lecturers know exactly the correct process of AI.

Additionally, setting aside dedicated time for lecturers to adopt AI technologies was deemed imperative, as mentioned by ten of the participants. Lecturer H opined:

Allocating specific time for training and hands-on experience with AI tools is crucial. We need the opportunity to explore and familiarise ourselves with this new, exciting technology. This will definitely help us.

Lecturer E noted:

Having dedicated time for learning and experimentation is essential. This would give us more confidence in the actual implementation. But our schedules are already so busy that I have to wonder if this is at all possible.

Next, we examine the findings of the research.

5 Discussion of research findings

Using the research findings as a starting point for drawing meaningful conclusions and contributing to scholarly discourse on the subject, this section provides a summary of the findings that correlate with the literature review. From the utterances of many of the participants it became clear that there is a positive attitude towards AI, its significance for blended learning, and the benefits for tertiary students, as long as HEIs make certain adaptations. This aligns with the Redefinition and Modification aspects of the SAMR [ 34 ] model used for this study.

The research questions sought to explore how AI influences student engagement, interaction, and learning outcomes in blended learning environments. Lecturer N’s opinion, that AI boosts the learning process as a whole, resulting in an improved ability to successfully complete the course , is consistent with the findings of Alshahrani [ 3 ], Ferry et al. [ 13 ], Fradila et al. [ 14 ], Rahman et al. [ 37 ] and Santosa et al. [ 39 ], who found that infusing AI into a blended learning module enriches the learning process for students, helping them to achieve the specified learning outcomes. The collaborative and conversational capabilities of AI enhance the overall learning experience, leading to an enjoyment of the course, and active participation by students. These findings support the SAMR [ 34 ] model’s Redefinition level, where AI transforms the learning experience. Accordingly, the researchers of this study recognised that while AI does enhance learning experiences, its integration must be carefully managed to avoid over-reliance on technology at the expense of fundamental pedagogical principles.

The research findings corroborate the potential benefits AI holds for blended learning, as identified by the interviewees. Lecturer H's use of AI for immediate student support aligns with the views of Alsaleem and Alghalith [ 2 ], Alshahrani [ 3 ] and Lee [ 24 ], who emphasise AI’s capacity for personalising learning experiences. Moreover, Lecturer B's opinion on the importance of using AI for the prompt integration of AI-driven feedback, is consistent with the findings of Alshahrani [ 3 ] and Khosravi and Heidari [ 22 ], which emphasise AI’s functionality of supplying instantaneous feedback to enhance the learning experience. This aligns with the Augmentation level of the SAMR [ 34 ] model, where AI enhances existing teaching and learning practices. This made it clear to the researchers that while AI-driven feedback can significantly improve learning efficiency, it also raises concerns about data privacy and the need for transparent feedback mechanisms.

The views of Weber et al. [ 41 ]—that resistance to change may be an obstacle to the effective implementation of AI—are consistent with the opinions of 12 of the study participants. Specifically, Lecturer G noted that transformation is often met with resistance, especially when it comes to technology, and AI may be perceived as a risk to the conventional mode of teaching. Addressing this resistance requires policy interventions and professional development programs to ease the transition and encourage AI adoption. This indicated to the researchers that creating a culture of continuous improvement and gradually embracing this new approach may prevent resistance to adopting AI by lecturers and their higher education institutions.

The perspectives of all the participants, as regards the significance of tailored training and professional development which are customised to their specific needs, align with the findings of Luckin et al. [ 26 ]. According to that study, training should be more specific, and be contextually relevant to the unique demands and settings of the educational environment. This approach encourages active engagement and participation. Lecturer M specifically noted that any related training should focus mainly on its application to blended learning, to be successful. This highlights the importance of ongoing professional development to keep pace with technological advances. Clearly, HEIs need to adapt their policies to integrate AI tools that support personalised and interactive learning experiences. This suggested to the researchers that for AI technologies to be successful in higher education, professional development programmes must be made easily accessible for lecturers.

Finally, as featured in Alshahrani’s [ 3 ] study, the ethical use of AI in educational environments that adopt a blended learning approach, must be considered. Two participants (F and G) expressed the same sentiment, stating that open discussions on ethical guidelines and continuous dialogue among lecturers, management, and students are essential for navigating these issues. This suggests that policy should include ethical guidelines for AI use in education, ensuring that such integration supports not only academic integrity, but also responsible teaching and learning practices. In view of these findings, the researchers concluded that there was a distinct need for the creation of specific ethical frameworks that would assist all stakeholders to address the emerging ethical concerns associated with AI use in higher education institutions.

5.1 Limitations of the research

It is important to note the limitations of this study, which affect the generalisability of the findings. First, the study was restricted to a single South African higher HEI and one specific college, which may limit the applicability of the results to other contexts or institutions. Additionally, the full impact of AI on the blended learning approach may only become apparent in the future, as the students from this cohort progress in their careers and enter their respective professions. Furthermore, AI is a rapidly evolving field, and its continual advancements could mean that the study’s findings might become outdated relatively quickly. Finally, the successful implementation of AI in blended learning modules may be hindered by the lack of requisite technological resources and infrastructure in some educational institutions, potentially affecting the feasibility and effectiveness of AI integration.

6 Conclusion and recommendations

This paper discussed the impact of AI on a blended approach to teaching and learning in a particular HEI. It was based on the perceptions of 15 participating lecturers who lecture in the same college, albeit in different departments. The insights were based on the lecturers’ familiarity, experiences of, and involvement with, AI, and its impact on their teaching and learning. This positioned them to discuss the perceived advantages, disadvantages and supportive measures needed for such an approach to be successful. The use of focus group interviews and document analysis enabled the researchers to correlate what was actually taking place in this field of research, with the literature review undertaken.

Puentedura’s [ 34 ] SAMR model was chosen as theoretical framework to guide this undertaking, since it enabled the researchers to investigate how AI could bring about transformative changes in blended learning within the domain of higher education. The results highlight the significance of using AI in hybrid learning contexts, which has great potential for transforming traditional teaching methods. The study highlighted the implications of adopting AI to enhance the effectiveness of blended learning which offers personalised feedback, interactive discussions, and adaptive resources to cater to individual student needs. The findings draw attention to the crucial role of supportive measures such as management backing, improved training and professional development opportunities, reliable technological infrastructure, and improved internet connectivity, in ensuring the successful use of AI for blended learning modules. The findings thus enhance the knowledge base of this emerging field of study, by clarifying the perspectives of the lecturer participants at a particular HEI. Moreover, the findings can support future research on this topic, and may be used by other educational institutions—even those catering for different age groups.

6.1 Recommendations for further research

Recommendations for further research include several key areas to enhance the understanding and implementation of AI in blended learning environments. First, investigating AI and blended learning across various HEIs, both within South Africa and internationally, would provide a more comprehensive understanding of lecturers' perceptions of AI's impact. Additionally, research should focus on the effect of AI on students’ achievement of learning outcomes, their engagement with modules, and their overall enjoyment of learning within hybrid environments. Examining specific support measures, particularly relevant training, could further assist lecturers in effectively integrating AI into their modules. Longitudinal studies are also recommended to track changes in lecturers’ perceptions as they adapt to and integrate AI over time. A thorough exploration of the challenges HEIs face during the implementation process should be considered to address potential barriers. Furthermore, research into the ethical implications of AI in education, including the development of necessary guidelines, is essential. Finally, future studies should aim to validate and expand upon these findings using quantitative methods, as this study was purely qualitative.

Data availability

The data that support the findings of this study are not openly available due to the privacy and confidentiality agreements with the participants. However, the data will be made available by the corresponding author upon reasonable request, subject to review and approval by the research ethics committee of the involved institution. Requests for data access can be made by contacting the corresponding author at [email protected].

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Acknowledgements

The authors acknowledge the cooperation of the lecturers who participated in the data-collection process, and the HEI under study, for allowing the research to be conducted.

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Debbie A. Sanders & Shirley S. Mukhari

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Authors both reviewed the manuscript and checked the references. Author A was responsible for the introduction and literature reviewer. Both authors were responsible for the data collection. Author B analysed the data and drafted the results section and then both authors together discussed the results and decided on the conclusion.

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1.1 Appendix A: Interview question

The following is the set of open-ended interview questions the researchers used by the researchers to assess the lecturer’s view of the impact of AI on blended learning:

Can you describe your experience incorporating AI technologies into your blended learning lessons?

What specific AI technologies or tools have you used in your blended learning approach?

Can you share examples of instances where AI enhanced the effectiveness of your blended learning lessons?

In your opinion, what are the key advantages of integrating AI into blended learning?

What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?

Have you received any feedback from students regarding their experiences with AI-infused blended learning?

Have you noticed any differences in student performance or understanding between traditional and AI-infused blended learning?

What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?

In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?

1.2 Appendix B: document analysis guide

The researchers used the following guidelines when analysing the module contents, lessons and assessments:

Assess whether the content and learning objectives of the module feature the integration of AI technologies ─ look for objectives that explicitly mention the use of AI to enhance specific skills or competencies.

Identify specific occurrences where AI enhances interactivity within lessons.

Look for evidence that assessments capture the unique contributions of AI to student learning outcomes.

Search for features that assist in the immediacy and effectiveness of feedback mechanisms through AI.

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Sanders, D.A., Mukhari, S.S. Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution. Discov Educ 3 , 135 (2024). https://doi.org/10.1007/s44217-024-00235-2

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Insights from 20 years (2004–2023) of supply chain disruption research: trends and future directions based on a bibliometric analysis.

1. Introduction

2. literature review, 3. materials and methods, 3.1. sample creation.

• Only papers published in international journals were retrieved, while other types of publications were not considered;
• Similarly, only papers written in English were considered.
• The paper’s metadata: authors, journal, bibliographic data, publication year, document title, and document type (article vs. review);
• The authors’ keywords;
• The publication option (traditional vs. open access);
• Funding information.

3.2. Descriptive Analyses

3.3. keyword analysis and trend.

• Authors often use slightly different terms to express the same concept. This is the case for singular or plural forms, British or American spelling of words, usage of capital letters/lower case letters, usage of hyphenation, or abbreviations (e.g., COVID vs. Coronavirus);
• Acronyms can sometimes be used as keywords instead of the full text.
• Well-established (‘core’), with high frequency and high persistence. They are expected to denote themes that have long been studied by many authors in the field;
• Intermittent, with low frequency and high persistence. Terms in this category denote themes that have been known for many years, but have been studied with low continuity;
• Phantom/emerging, with low frequency and low persistence. These topics could be relatively new to the research field or could describe themes that have progressively disappeared;
• Trendy, with high frequency and low persistence. These topics are relatively new but have already attracted the attention of many researchers.
• The subset of keywords that were observed in all periods of analysis, as these terms are expected to reflect relevant themes to the selected field of research. For those terms, their classification across the four periods was mapped, so as to delineate a trend in the interest toward the specific theme;
• The subset of keywords with a minimum frequency of 30, thus indicating a high recurrence of the related topics in the targeted field of research. These keywords were first grouped by macro-areas; then, their trend in time was evaluated jointly with that of some core topics of the targeted field of research to identify possible correlations.

4.1. Descriptive Analyses

4.2. keyword analysis and trend.

• Query-related terms: as the query settings expressively included terms such as “supply chain” and “disruption”, these terms (and their combination “supply chain disruption”) were grouped in a single query-related category;
• COVID-relates terms: this category includes the terms “COVID-19” and “COVID-19 pandemic”;
• Disruption-related terms: these terms are semantically related to the topic of “disruption”, which, however, is not necessarily used as a keyword. Those terms are supply disruption; pandemic; disruption risk; disruption management; uncertainty; ripple effect; demand disruption; and disaster;
• Risk- or resilience-related terms: this category includes terms that were not used in the query settings but that appear to be related to the more general theme of risk management or resilience, whose relationship with supply chain disruptions is obvious. These terms include (supply chain) resilience, (supply chain) risk management, (supply chain) risk, robustness; resilient supply chain, supply risk, reliability, risk assessment; or vulnerability;
• Supply chain-related terms: as per the classification made previously, these terms do not strictly refer to disruptions, but to more general problems in the area of supply chain or supply chain management. These terms include supply chain management, supplier selection, logistics, supply chain design, supply chain network, global supply chain, supply chain network design, collaboration, supply chain coordination, or inventory management;
• Sustainability-related terms: the sustainability perspective includes four terms, namely sustainability, closed-loop supply chain, climate change, and circular economy;
• Technology-related terms: this category includes terms such as Industry 4.0, artificial intelligence, machine learning, additive manufacturing, or blockchain;
• Tools and methodologies: this group of terms includes typical engineering tools and techniques, such as simulation, game theory, (robust) optimization, stochastic programming, system dynamics, case study, or multi-criteria decision making;
• Interrelated topics: terms in this category do not strictly refer to the area of supply chain disruptions, nor the more general area of risk or supply chain management. Rather, they introduce complementary topics, such as food security, food supply chain, small and medium enterprises, innovation, agility, or systematic review.

5. Discussion

6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Solari, F.; Lysova, N.; Romagnoli, G.; Montanari, R.; Bottani, E. Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis. Sustainability 2024 , 16 , 7530. https://doi.org/10.3390/su16177530

Solari F, Lysova N, Romagnoli G, Montanari R, Bottani E. Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis. Sustainability . 2024; 16(17):7530. https://doi.org/10.3390/su16177530

Solari, Federico, Natalya Lysova, Giovanni Romagnoli, Roberto Montanari, and Eleonora Bottani. 2024. "Insights from 20 Years (2004–2023) of Supply Chain Disruption Research: Trends and Future Directions Based on a Bibliometric Analysis" Sustainability 16, no. 17: 7530. https://doi.org/10.3390/su16177530

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