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Handbook of research on creative problem-solving skill development in higher education

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• Fostering creative problem solvers in higher education: a response to complexity of societies / Chunfang Zhou
• Thinking inside the box: educating leaders to manage constraints / Kelsey E. Medeiros, Logan L. Watts, Michael D. Mumford
• Scientific creativity in psychology: a cognitive-conative approach / Christiane Kirsch [and 3 others]
• A creativity and innovation course for engineers / Giovanni Emanuele Corazza, Sergio Agnoli, Sara Martello
• Teaching creative problem solving in engineering education / René Victor Valqui Vidal
• Creativity development through inquiry-based learning in biomedical sciences / Gemma Rodríguez, Josep-Eladi Baños, Mar Carrió
• Distributed problem-solving: how artists' participatory strategies can inspire creativity in higher education / Tatiana Chemi
• Creative life experience among students in medical education / Miao Yu, Jianxin Li, Wei Wang
• International Center for Studies in Creativity: curricular overview and impact of instruction on the creative problem-solving attitudes of graduate students / Gerard J. Puccio [and 3 others]
• Problem solving at the edge of disciplines / Andrew Connor [and 3 others]
• Enhancing students' critical thinking through portfolios: portfolio content and process of use / Zineb Djoub
• An exploration of darkness within doctoral education: creative learning approaches of doctoral students / Søren S. E. Bengtsen --
• Integrating creative thinking skills into the higher education classroom / Cyndi Burnett, Susan Keller-Mathers
• Design thinking in higher education: how students become dedicated creative problem solvers / Julia von Thienen, Adam Royalty, Christoph Meinel
• Students' learning experiences in project-based learning (PtBL): with pain comes gain / Roxanne DuVivier, Carol Logan Patitu, Sheri Stover
• Advocating problem-based learning and creative problem-solving skills in global education / Kijpokin Kasemsap
• Instructional design technology in higher education system: role and impact on developing creative learning environments / Lakshmi Sunil Prakash, Dinesh Kumar Saini
• Developing creative problem solvers and professional identity through ICT in higher education / Line Helverskov Horn, Md. Saifuddin Khalid
• Reaching "creating" in Bloom's taxonomy: the merging of heutagogy and technology in online learning / Colleen Halupa
• Applying Bloom's digital taxonomy to address creativity and second order digital divide in internet skills / Aparna Purushothaman
• Framing creative problems / Ricardo Sosa, Andy M. Connor, Bruce Corson
• On the relationships between creative learning, creative teaching, and roles of creative teachers / Chuanhua Gu
• New paradigm of creativity: from Newtonian mechanics to quantum mechanics and higher education development / Masaaki Hasegawa
• Going towards adaption, integration, and co-creation: a conclusion to developing creative problem solving skills in higher education / Chunfang Zhou.

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Creative Thinking: Innovative Solutions to Complex Challenges

Learn how to grow a culture of creativity to innovate competitive solutions.

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April 22, 2025

Overview: Creative Thinking Skills Course

The tech breakthrough that makes smartphones irrelevant, a new viral ad campaign, your company’s next big revenue generator — ideas like these could be sitting in your brain; all you need are the creative thinking skills and strategies to pull them out.

This interactive program focuses explicitly on the creative thinking skills you need to solve complex problems and design innovative solutions. Learn how to transform your thinking from the standard “why can’t we” to the powerful “how might we.” Crack the code on how to consistently leverage your team’s creative potential in order to drive innovation within your organization. Explore how to build a climate for innovation, remove barriers to creativity, cultivate courage, and create more agile, proactive, and inspired teams.

You will leave this program with new ideas about how to think more productively and how to introduce creative thinking skills into your organization. You can apply key takeaways immediately to implement a new leadership vision, inspire renewed enthusiasm, and enjoy the skills and tools to tackle challenges and seize opportunities.

Innovation experts Anne Manning and Susan Robertson bring to this highly-interactive and powerful program their decades of experience promoting corporate innovation, teaching the art of creative problem solving, and applying the principles of brain science to solve complex challenges.

Who Should Take Creative Thinking Skills Training?

This program is ideal for leaders with at least 3 years of management experience. It is designed for leaders who want to develop new strategies, frameworks, and tools for creative problem solving. Whether you are a team lead, project manager, sales director, or executive, you’ll learn powerful tools to lead your team and your organization to create innovative solutions to complex challenges.

All participants will earn a Certificate of Completion from the Harvard Division of Continuing Education.

Benefits of Creative Thinking Skills Training

The goal of this creative thinking program is to help you develop the strategic concepts and tactical skills to lead creative problem solving for your team and your organization. You will learn to:

• Retrain your brain to avoid negative cognitive biases and long-held beliefs and myths that sabotage creative problem solving and innovation
• Become a more nimble, proactive, and inspired thinker and leader
• Create the type of organizational culture that supports collaboration and nurtures rather than kills ideas
• Gain a practical toolkit for solving the “unsolvable” by incorporating creative thinking into day-to-day processes
• Understand cognitive preferences (yours and others’) to adapt the creative thinking process and drive your team’s success
• Develop techniques that promote effective brainstorming and enable you to reframe problems in a way that inspires innovative solutions

The curriculum in this highly interactive program utilizes research-based methodologies and techniques to build creative thinking skills and stimulate creative problem solving.

Through intensive group discussions and small-group exercises, you will focus on topics such as:

• The Creative Problem Solving process: a researched, learnable, repeatable process for uncovering new and useful ideas. This process includes a “how to” on clarifying, ideating, developing, and implementing new solutions to intractable problems
• The cognitive preferences that drive how we approach problems, and how to leverage those cognitive preferences for individual and team success
• How to develop—and implement— a methodology that overcomes barriers to innovative thinking and fosters the generation of new ideas, strategies, and techniques
• The role of language, including asking the right questions, in reframing problems, challenging assumptions, and driving successful creative problem solving
• Fostering a culture that values, nurtures, and rewards creative solutions

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I really enjoyed the way the instructors facilitated the program. The combination of theory and practical exercises was powerful and effectively reinforced the concepts.

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• DOI: 10.4018/978-1-5225-0643-0.CH001
• Corpus ID: 63028910

Fostering Creative Problem Solvers in Higher Education: A Response to Complexity of Societies

• Chunfang Zhou
• Published 2016
• Education, Sociology, Psychology

17 Citations

Fostering problem-based learning (pbl) in chinese universities for a creative society, pbl intervention for fostering creativity in first-year engineering students, culture of co-creation, meeting gender gaps in information and communication technology (ict), rethinking ‘practice’ in traditional thoughts and implications for science education innovation in china, bridging creativity and pbl, trends and opportunities by fostering creativity in science and engineering: a systematic review.

• Highly Influenced

Fostering Creative Talents for an “Innovation-Oriented Nation”

Usages of information communication technology (ict), development of problem-based learning (pbl), 40 references, fostering creative engineers: a key to face the complexity of engineering practice, creative thinking skill approach through problem-based learning: pedagogy and practice in the engineering classroom, fostering creativity through education – a conceptual framework of creative pedagogy, a problem and project-based learning (pbl) approach to motivate group creativity in engineering education, teaching creativity in higher education, developing conceptual frameworks for creativity, ict and teacher education, interplay between individual creativity and group creativity in problem and project-based learning (pbl) environment in engineering education, creativity in engineering education., teaching for creativity: towards sustainable and replicable pedagogical practice, problem-based learning: what and how do students learn, related papers.

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Teaching Creativity and Inventive Problem Solving in Science

• Robert L. DeHaan

Division of Educational Studies, Emory University, Atlanta, GA 30322

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Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, have not become widely known or used. In this essay, I review the evidence that creativity is not a single hard-to-measure property. The creative process can be explained by reference to increasingly well-understood cognitive skills such as cognitive flexibility and inhibitory control that are widely distributed in the population. I explore the relationship between creativity and the higher-order cognitive skills, review assessment methods, and describe several instructional strategies for enhancing creative problem solving in the college classroom. Evidence suggests that instruction to support the development of creativity requires inquiry-based teaching that includes explicit strategies to promote cognitive flexibility. Students need to be repeatedly reminded and shown how to be creative, to integrate material across subject areas, to question their own assumptions, and to imagine other viewpoints and possibilities. Further research is required to determine whether college students' learning will be enhanced by these measures.

INTRODUCTION

Dr. Dunne paces in front of his section of first-year college students, today not as their Bio 110 teacher but in the role of facilitator in their monthly “invention session.” For this meeting, the topic is stem cell therapy in heart disease. Members of each team of four students have primed themselves on the topic by reading selected articles from accessible sources such as Science, Nature, and Scientific American, and searching the World Wide Web, triangulating for up-to-date, accurate, background information. Each team knows that their first goal is to define a set of problems or limitations to overcome within the topic and to begin to think of possible solutions. Dr. Dunne starts the conversation by reminding the group of the few ground rules: one speaker at a time, listen carefully and have respect for others' ideas, question your own and others' assumptions, focus on alternative paths or solutions, maintain an atmosphere of collaboration and mutual support. He then sparks the discussion by asking one of the teams to describe a problem in need of solution.

Science in the United States is widely credited as a major source of discovery and economic development. According to the 2005 TAP Report produced by a prominent group of corporate leaders, “To maintain our country's competitiveness in the twenty-first century, we must cultivate the skilled scientists and engineers needed to create tomorrow's innovations.” ( www.tap2015.org/about/TAP_report2.pdf ). A panel of scientists, engineers, educators, and policy makers convened by the National Research Council (NRC) concurred with this view, reporting that the vitality of the nation “is derived in large part from the productivity of well-trained people and the steady stream of scientific and technical innovations they produce” ( NRC, 2007 ).

For many decades, science education reformers have promoted the idea that learners should be engaged in the excitement of science; they should be helped to discover the value of evidence-based reasoning and higher-order cognitive skills, and be taught to become innovative problem solvers (for reviews, see DeHaan, 2005 ; Hake, 2005 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). But the means to achieve these goals, especially methods to promote creative thinking in scientific problem solving, are not widely known or used. An invention session such as that led by the fictional Dr. Dunne, described above, may seem fanciful as a means of teaching students to think about science as something more than a body of facts and terms to memorize. In recent years, however, models for promoting creative problem solving were developed for classroom use, as detailed by Treffinger and Isaksen (2005) , and such techniques are often used in the real world of high technology. To promote imaginative thinking, the advertising executive Alex F. Osborn invented brainstorming ( Osborn, 1948 , 1979 ), a technique that has since been successful in stimulating inventiveness among engineers and scientists. Could such strategies be transferred to a class for college students? Could they serve as a supplement to a high-quality, scientific teaching curriculum that helps students learn the facts and conceptual frameworks of science and make progress along the novice–expert continuum? Could brainstorming or other instructional strategies that are specifically designed to promote creativity teach students to be more adaptive in their growing expertise, more innovative in their problem-solving abilities? To begin to answer those questions, we first need to understand what is meant by “creativity.”

What Is Creativity? Big-C versus Mini-C Creativity

How to define creativity is an age-old question. Justice Potter Stewart's famous dictum regarding obscenity “I know it when I see it” has also long been an accepted test of creativity. But this is not an adequate criterion for developing an instructional approach. A scientist colleague of mine recently noted that “Many of us [in the scientific community] rarely give the creative process a second thought, imagining one either ‘has it’ or doesn't.” We often think of inventiveness or creativity in scientific fields as the kind of gift associated with a Michelangelo or Einstein. This is what Kaufman and Beghetto (2008) call big-C creativity, borrowing the term that earlier workers applied to the talents of experts in various fields who were identified as particularly creative by their expert colleagues ( MacKinnon, 1978 ). In this sense, creativity is seen as the ability of individuals to generate new ideas that contribute substantially to an intellectual domain. Howard Gardner defined such a creative person as one who “regularly solves problems, fashions products, or defines new questions in a domain in a way that is initially considered novel but that ultimately comes to be accepted in a particular cultural setting” ( Gardner, 1993 , p. 35).

But there is another level of inventiveness termed by various authors as “little-c” ( Craft, 2000 ) or “mini-c” ( Kaufman and Beghetto, 2008 ) creativity that is widespread among all populations. This would be consistent with the workplace definition of creativity offered by Amabile and her coworkers: “coming up with fresh ideas for changing products, services and processes so as to better achieve the organization's goals” ( Amabile et al. , 2005 ). Mini-c creativity is based on what Craft calls “possibility thinking” ( Craft, 2000 , pp. 3–4), as experienced when a worker suddenly has the insight to visualize a new, improved way to accomplish a task; it is represented by the “aha” moment when a student first sees two previously disparate concepts or facts in a new relationship, an example of what Arthur Koestler identified as bisociation: “perceiving a situation or event in two habitually incompatible associative contexts” ( Koestler, 1964 , p. 95).

In this essay, I maintain that mini-c creativity is not a mysterious, innate endowment of rare individuals. Instead, I argue that creative thinking is a multicomponent process, mediated through social interactions, that can be explained by reference to increasingly well-understood mental abilities such as cognitive flexibility and cognitive control that are widely distributed in the population. Moreover, I explore some of the recent research evidence (though with no effort at a comprehensive literature review) showing that these mental abilities are teachable; like other higher-order cognitive skills (HOCS), they can be enhanced by explicit instruction.

Creativity Is a Multicomponent Process

Efforts to define creativity in psychological terms go back to J. P. Guilford ( Guilford, 1950 ) and E. P. Torrance ( Torrance, 1974 ), both of whom recognized that underlying the construct were other cognitive variables such as ideational fluency, originality of ideas, and sensitivity to missing elements. Many authors since then have extended the argument that a creative act is not a singular event but a process, an interplay among several interactive cognitive and affective elements. In this view, the creative act has two phases, a generative and an exploratory or evaluative phase ( Finke et al. , 1996 ). During the generative process, the creative mind pictures a set of novel mental models as potential solutions to a problem. In the exploratory phase, we evaluate the multiple options and select the best one. Early scholars of creativity, such as J. P. Guilford, characterized the two phases as divergent thinking and convergent thinking ( Guilford, 1950 ). Guilford defined divergent thinking as the ability to produce a broad range of associations to a given stimulus or to arrive at many solutions to a problem (for overviews of the field from different perspectives, see Amabile, 1996 ; Banaji et al. , 2006 ; Sawyer, 2006 ). In neurocognitive terms, divergent thinking is referred to as associative richness ( Gabora, 2002 ; Simonton, 2004 ), which is often measured experimentally by comparing the number of words that an individual generates from memory in response to stimulus words on a word association test. In contrast, convergent thinking refers to the capacity to quickly focus on the one best solution to a problem.

The idea that there are two stages to the creative process is consistent with results from cognition research indicating that there are two distinct modes of thought, associative and analytical ( Neisser, 1963 ; Sloman, 1996 ). In the associative mode, thinking is defocused, suggestive, and intuitive, revealing remote or subtle connections between items that may be correlated, or may not, and are usually not causally related ( Burton, 2008 ). In the analytical mode, thought is focused and evaluative, more conducive to analyzing relationships of cause and effect (for a review of other cognitive aspects of creativity, see Runco, 2004 ). Science educators associate the analytical mode with the upper levels (analysis, synthesis, and evaluation) of Bloom's taxonomy (e.g., Crowe et al. , 2008 ), or with “critical thinking,” the process that underlies the “purposeful, self-regulatory judgment that drives problem-solving and decision-making” ( Quitadamo et al. , 2008 , p. 328). These modes of thinking are under cognitive control through the executive functions of the brain. The core executive functions, which are thought to underlie all planning, problem solving, and reasoning, are defined ( Blair and Razza, 2007 ) as working memory control (mentally holding and retrieving information), cognitive flexibility (considering multiple ideas and seeing different perspectives), and inhibitory control (resisting several thoughts or actions to focus on one). Readers wishing to delve further into the neuroscience of the creative process can refer to the cerebrocerebellar theory of creativity ( Vandervert et al. , 2007 ) in which these mental activities are described neurophysiologically as arising through interactions among different parts of the brain.

The main point from all of these works is that creativity is not some single hard-to-measure property or act. There is ample evidence that the creative process requires both divergent and convergent thinking and that it can be explained by reference to increasingly well-understood underlying mental abilities ( Haring-Smith, 2006 ; Kim, 2006 ; Sawyer, 2006 ; Kaufman and Sternberg, 2007 ) and cognitive processes ( Simonton, 2004 ; Diamond et al. , 2007 ; Vandervert et al. , 2007 ).

Creativity Is Widely Distributed and Occurs in a Social Context

Although it is understandable to speak of an aha moment as a creative act by the person who experiences it, authorities in the field have long recognized (e.g., Simonton, 1975 ) that creative thinking is not so much an individual trait but rather a social phenomenon involving interactions among people within their specific group or cultural settings. “Creativity isn't just a property of individuals, it is also a property of social groups” ( Sawyer, 2006 , p. 305). Indeed, Osborn introduced his brainstorming method because he was convinced that group creativity is always superior to individual creativity. He drew evidence for this conclusion from activities that demand collaborative output, for example, the improvisations of a jazz ensemble. Although each musician is individually creative during a performance, the novelty and inventiveness of each performer's playing is clearly influenced, and often enhanced, by “social and interactional processes” among the musicians ( Sawyer, 2006 , p. 120). Recently, Brophy (2006) offered evidence that for problem solving, the situation may be more nuanced. He confirmed that groups of interacting individuals were better at solving complex, multipart problems than single individuals. However, when dealing with certain kinds of single-issue problems, individual problem solvers produced a greater number of solutions than interacting groups, and those solutions were judged to be more original and useful.

Consistent with the findings of Brophy (2006) , many scholars acknowledge that creative discoveries in the real world such as solving the problems of cutting-edge science—which are usually complex and multipart—are influenced or even stimulated by social interaction among experts. The common image of the lone scientist in the laboratory experiencing a flash of creative inspiration is probably a myth from earlier days. As a case in point, the science historian Mara Beller analyzed the social processes that underlay some of the major discoveries of early twentieth-century quantum physics. Close examination of successive drafts of publications by members of the Copenhagen group revealed a remarkable degree of influence and collaboration among 10 or more colleagues, although many of these papers were published under the name of a single author ( Beller, 1999 ). Sociologists Bruno Latour and Steve Woolgar's study ( Latour and Woolgar, 1986 ) of a neuroendocrinology laboratory at the Salk Institute for Biological Studies make the related point that social interactions among the participating scientists determined to a remarkable degree what discoveries were made and how they were interpreted. In the laboratory, researchers studied the chemical structure of substances released by the brain. By analysis of the Salk scientists' verbalizations of concepts, theories, formulas, and results of their investigations, Latour and Woolgar showed that the structures and interpretations that were agreed upon, that is, the discoveries announced by the laboratory, were mediated by social interactions and power relationships among members of the laboratory group. By studying the discovery process in other fields of the natural sciences, sociologists and anthropologists have provided more cases that further illustrate how social and cultural dimensions affect scientific insights (for a thoughtful review, see Knorr Cetina, 1995 ).

In sum, when an individual experiences an aha moment that feels like a singular creative act, it may rather have resulted from a multicomponent process, under the influence of group interactions and social context. The process that led up to what may be sensed as a sudden insight will probably have included at least three diverse, but testable elements: 1) divergent thinking, including ideational fluency or cognitive flexibility, which is the cognitive executive function that underlies the ability to visualize and accept many ideas related to a problem; 2) convergent thinking or the application of inhibitory control to focus and mentally evaluate ideas; and 3) analogical thinking, the ability to understand a novel idea in terms of one that is already familiar.

LITERATURE REVIEW

What do we know about how to teach creativity.

The possibility of teaching for creative problem solving gained credence in the 1960s with the studies of Jerome Bruner, who argued that children should be encouraged to “treat a task as a problem for which one invents an answer, rather than finding one out there in a book or on the blackboard” ( Bruner, 1965 , pp. 1013–1014). Since that time, educators and psychologists have devised programs of instruction designed to promote creativity and inventiveness in virtually every student population: pre–K, elementary, high school, and college, as well as in disadvantaged students, athletes, and students in a variety of specific disciplines (for review, see Scott et al. , 2004 ). Smith (1998) identified 172 instructional approaches that have been applied at one time or another to develop divergent thinking skills.

Some of the most convincing evidence that elements of creativity can be enhanced by instruction comes from work with young children. Bodrova and Leong (2001) developed the Tools of the Mind (Tools) curriculum to improve all of the three core mental executive functions involved in creative problem solving: cognitive flexibility, working memory, and inhibitory control. In a year-long randomized study of 5-yr-olds from low-income families in 21 preschool classrooms, half of the teachers applied the districts' balanced literacy curriculum (literacy), whereas the experimenters trained the other half to teach the same academic content by using the Tools curriculum ( Diamond et al. , 2007 ). At the end of the year, when the children were tested with a battery of neurocognitive tests including a test for cognitive flexibility ( Durston et al. , 2003 ; Davidson et al. , 2006 ), those exposed to the Tools curriculum outperformed the literacy children by as much as 25% ( Diamond et al. , 2007 ). Although the Tools curriculum and literacy program were similar in academic content and in many other ways, they differed primarily in that Tools teachers spent 80% of their time explicitly reminding the children to think of alternative ways to solve a problem and building their executive function skills.

Teaching older students to be innovative also demands instruction that explicitly promotes creativity but is rigorously content-rich as well. A large body of research on the differences between novice and expert cognition indicates that creative thinking requires at least a minimal level of expertise and fluency within a knowledge domain ( Bransford et al. , 2000 ; Crawford and Brophy, 2006 ). What distinguishes experts from novices, in addition to their deeper knowledge of the subject, is their recognition of patterns in information, their ability to see relationships among disparate facts and concepts, and their capacity for organizing content into conceptual frameworks or schemata ( Bransford et al. , 2000 ; Sawyer, 2005 ).

Such expertise is often lacking in the traditional classroom. For students attempting to grapple with new subject matter, many kinds of problems that are presented in high school or college courses or that arise in the real world can be solved merely by applying newly learned algorithms or procedural knowledge. With practice, problem solving of this kind can become routine and is often considered to represent mastery of a subject, producing what Sternberg refers to as “pseudoexperts” ( Sternberg, 2003 ). But beyond such routine use of content knowledge the instructor's goal must be to produce students who have gained the HOCS needed to apply, analyze, synthesize, and evaluate knowledge ( Crowe et al. , 2008 ). The aim is to produce students who know enough about a field to grasp meaningful patterns of information, who can readily retrieve relevant knowledge from memory, and who can apply such knowledge effectively to novel problems. This condition is referred to as adaptive expertise ( Hatano and Ouro, 2003 ; Schwartz et al. , 2005 ). Instead of applying already mastered procedures, adaptive experts are able to draw on their knowledge to invent or adapt strategies for solving unique or novel problems within a knowledge domain. They are also able, ideally, to transfer conceptual frameworks and schemata from one domain to another (e.g., Schwartz et al. , 2005 ). Such flexible, innovative application of knowledge is what results in inventive or creative solutions to problems ( Crawford and Brophy, 2006 ; Crawford, 2007 ).

Promoting Creative Problem Solving in the College Classroom

In most college courses, instructors teach science primarily through lectures and textbooks that are dominated by facts and algorithmic processing rather than by concepts, principles, and evidence-based ways of thinking. This is despite ample evidence that many students gain little new knowledge from traditional lectures ( Hrepic et al. , 2007 ). Moreover, it is well documented that these methods engender passive learning rather than active engagement, boredom instead of intellectual excitement, and linear thinking rather than cognitive flexibility (e.g., Halpern and Hakel, 2003 ; Nelson, 2008 ; Perkins and Wieman, 2008 ). Cognitive flexibility, as noted, is one of the three core mental executive functions involved in creative problem solving ( Ausubel, 1963 , 2000 ). The capacity to apply ideas creatively in new contexts, referred to as the ability to “transfer” knowledge (see Mestre, 2005 ), requires that learners have opportunities to actively develop their own representations of information to convert it to a usable form. Especially when a knowledge domain is complex and fraught with ill-structured information, as in a typical introductory college biology course, instruction that emphasizes active-learning strategies is demonstrably more effective than traditional linear teaching in reducing failure rates and in promoting learning and transfer (e.g., Freeman et al. , 2007 ). Furthermore, there is already some evidence that inclusion of creativity training as part of a college curriculum can have positive effects. Hunsaker (2005) has reviewed a number of such studies. He cites work by McGregor (2001) , for example, showing that various creativity training programs including brainstorming and creative problem solving increase student scores on tests of creative-thinking abilities.

Model creativity—students develop creativity when instructors model creative thinking and inventiveness.

Repeatedly encourage idea generation—students need to be reminded to generate their own ideas and solutions in an environment free of criticism.

Cross-fertilize ideas—where possible, avoid teaching in subject-area boxes: a math box, a social studies box, etc; students' creative ideas and insights often result from learning to integrate material across subject areas.

Build self-efficacy—all students have the capacity to create and to experience the joy of having new ideas, but they must be helped to believe in their own capacity to be creative.

Constantly question assumptions—make questioning a part of the daily classroom exchange; it is more important for students to learn what questions to ask and how to ask them than to learn the answers.

Imagine other viewpoints—students broaden their perspectives by learning to reflect upon ideas and concepts from different points of view.

How Is Creativity Related to Critical Thinking and the Higher-Order Cognitive Skills?

It is not uncommon to associate creativity and ingenuity with scientific reasoning ( Sawyer, 2005 ; 2006 ). When instructors apply scientific teaching strategies ( Handelsman et al. , 2004 ; DeHaan, 2005 ; Wood, 2009 ) by using instructional methods based on learning research, according to Ebert-May and Hodder ( 2008 ), “we see students actively engaged in the thinking, creativity, rigor, and experimentation we associate with the practice of science—in much the same way we see students learn in the field and in laboratories” (p. 2). Perkins and Wieman (2008) note that “To be successful innovators in science and engineering, students must develop a deep conceptual understanding of the underlying science ideas, an ability to apply these ideas and concepts broadly in different contexts, and a vision to see their relevance and usefulness in real-world applications … An innovator is able to perceive and realize potential connections and opportunities better than others” (pp. 181–182). The results of Scott et al. (2004) suggest that nontraditional courses in science that are based on constructivist principles and that use strategies of scientific teaching to promote the HOCS and enhance content mastery and dexterity in scientific thinking ( Handelsman et al. , 2007 ; Nelson, 2008 ) also should be effective in promoting creativity and cognitive flexibility if students are explicitly guided to learn these skills.

Creativity is an essential element of problem solving ( Mumford et al. , 1991 ; Runco, 2004 ) and of critical thinking ( Abrami et al. , 2008 ). As such, it is common to think of applications of creativity such as inventiveness and ingenuity among the HOCS as defined in Bloom's taxonomy ( Crowe et al. , 2008 ). Thus, it should come as no surprise that creativity, like other elements of the HOCS, can be taught most effectively through inquiry-based instruction, informed by constructivist theory ( Ausubel, 1963 , 2000 ; Duch et al. , 2001 ; Nelson, 2008 ). In a survey of 103 instructors who taught college courses that included creativity instruction, Bull et al. (1995) asked respondents to rate the importance of various course characteristics for enhancing student creativity. Items ranking high on the list were: providing a social climate in which students feels safe, an open classroom environment that promotes tolerance for ambiguity and independence, the use of humor, metaphorical thinking, and problem defining. Many of the responses emphasized the same strategies as those advanced to promote creative problem solving (e.g., Mumford et al. , 1991 ; McFadzean, 2002 ; Treffinger and Isaksen, 2005 ) and critical thinking ( Abrami et al. , 2008 ).

In a careful meta-analysis, Scott et al. (2004) examined 70 instructional interventions designed to enhance and measure creative performance. The results were striking. Courses that stressed techniques such as critical thinking, convergent thinking, and constraint identification produced the largest positive effect sizes. More open techniques that provided less guidance in strategic approaches had less impact on the instructional outcomes. A striking finding was the effectiveness of being explicit; approaches that clearly informed students about the nature of creativity and offered clear strategies for creative thinking were most effective. Approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were found to be positively correlated to high effect sizes. The most clear-cut result to emerge from the Scott et al. (2004) study was simply to confirm that creativity instruction can be highly successful in enhancing divergent thinking, problem solving, and imaginative performance. Most importantly, of the various cognitive processes examined, those linked to the generation of new ideas such as problem finding, conceptual combination, and idea generation showed the greatest improvement. The success of creativity instruction, the authors concluded, can be attributed to “developing and providing guidance concerning the application of requisite cognitive capacities … [and] a set of heuristics or strategies for working with already available knowledge” (p. 382).

Many of the scientific teaching practices that have been shown by research to foster content mastery and HOCS, and that are coming more widely into use, also would be consistent with promoting creativity. Wood (2009) has recently reviewed examples of such practices and how to apply them. These include relatively small modifications of the traditional lecture to engender more active learning, such as the use of concept tests and peer instruction ( Mazur, 1996 ), Just-in-Time-Teaching techniques ( Novak et al. , 1999 ), and student response systems known as “clickers” ( Knight and Wood, 2005 ; Crossgrove and Curran, 2008 ), all designed to allow the instructor to frequently and effortlessly elicit and respond to student thinking. Other strategies can transform the lecture hall into a workshop or studio classroom ( Gaffney et al. , 2008 ) where the teaching curriculum may emphasize problem-based (also known as project-based or case-based) learning strategies ( Duch et al. , 2001 ; Ebert-May and Hodder, 2008 ) or “community-based inquiry” in which students engage in research that enhances their critical-thinking skills ( Quitadamo et al. , 2008 ).

Another important approach that could readily subserve explicit creativity instruction is the use of computer-based interactive simulations, or “sims” ( Perkins and Wieman, 2008 ) to facilitate inquiry learning and effective, easy self-assessment. An example in the biological sciences would be Neurons in Action ( http://neuronsinaction.com/home/main ). In such educational environments, students gain conceptual understanding of scientific ideas through interactive engagement with materials (real or virtual), with each other, and with instructors. Following the tenets of scientific teaching, students are encouraged to pose and answer their own questions, to make sense of the materials, and to construct their own understanding. The question I pose here is whether an additional focus—guiding students to meet these challenges in a context that explicitly promotes creativity—would enhance learning and advance students' progress toward adaptive expertise?

Assessment of Creativity

To teach creativity, there must be measurable indicators to judge how much students have gained from instruction. Educational programs intended to teach creativity became popular after the Torrance Tests of Creative Thinking (TTCT) was introduced in the 1960s ( Torrance, 1974 ). But it soon became apparent that there were major problems in devising tests for creativity, both because of the difficulty of defining the construct and because of the number and complexity of elements that underlie it. Tests of intelligence and other personality characteristics on creative individuals revealed a host of related traits such as verbal fluency, metaphorical thinking, flexible decision making, tolerance of ambiguity, willingness to take risks, autonomy, divergent thinking, self-confidence, problem finding, ideational fluency, and belief in oneself as being “creative” ( Barron and Harrington, 1981 ; Tardif and Sternberg, 1988 ; Runco and Nemiro, 1994 ; Snyder et al. , 2004 ). Many of these traits have been the focus of extensive research of recent decades, but, as noted above, creativity is not defined by any one trait; there is now reason to believe that it is the interplay among the cognitive and affective processes that underlie inventiveness and the ability to find novel solutions to a problem.

Although the early creativity researchers recognized that assessing divergent thinking as a measure of creativity required tests for other underlying capacities ( Guilford, 1950 ; Torrance, 1974 ), these workers and their colleagues nonetheless believed that a high score for divergent thinking alone would correlate with real creative output. Unfortunately, no such correlation was shown ( Barron and Harrington, 1981 ). Results produced by many of the instruments initially designed to measure various aspects of creative thinking proved to be highly dependent on the test itself. A review of several hundred early studies showed that an individual's creativity score could be affected by simple test variables, for example, how the verbal pretest instructions were worded ( Barron and Harrington, 1981 , pp. 442–443). Most scholars now agree that divergent thinking, as originally defined, was not an adequate measure of creativity. The process of creative thinking requires a complex combination of elements that include cognitive flexibility, memory control, inhibitory control, and analogical thinking, enabling the mind to free-range and analogize, as well as to focus and test.

More recently, numerous psychometric measures have been developed and empirically tested (see Plucker and Renzulli, 1999 ) that allow more reliable and valid assessment of specific aspects of creativity. For example, the creativity quotient devised by Snyder et al. (2004) tests the ability of individuals to link different ideas and different categories of ideas into a novel synthesis. The Wallach–Kogan creativity test ( Wallach and Kogan, 1965 ) explores the uniqueness of ideas associated with a stimulus. For a more complete list and discussion, see the Creativity Tests website ( www.indiana.edu/∼bobweb/Handout/cretv_6.html ).

The most widely used measure of creativity is the TTCT, which has been modified four times since its original version in 1966 to take into account subsequent research. The TTCT-Verbal and the TTCT-Figural are two versions ( Torrance, 1998 ; see http://ststesting.com/2005giftttct.html ). The TTCT-Verbal consists of five tasks; the “stimulus” for each task is a picture to which the test-taker responds briefly in writing. A sample task that can be viewed from the TTCT Demonstrator website asks, “Suppose that people could transport themselves from place to place with just a wink of the eye or a twitch of the nose. What might be some things that would happen as a result? You have 3 min.” ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

In the TTCT-Figural, participants are asked to construct a picture from a stimulus in the form of a partial line drawing given on the test sheet (see example below; Figure 1 ). Specific instructions are to “Add lines to the incomplete figures below to make pictures out of them. Try to tell complete stories with your pictures. Give your pictures titles. You have 3 min.” In the introductory materials, test-takers are urged to “… think of a picture or object that no one else will think of. Try to make it tell as complete and as interesting a story as you can …” ( Torrance et al. , 2008 , p. 2).

Figure 1. Sample figural test item from the TTCT Demonstrator website ( www.indiana.edu/∼bobweb/Handout/d3.ttct.htm ).

How would an instructor in a biology course judge the creativity of students' responses to such an item? To assist in this task, the TTCT has scoring and norming guides ( Torrance, 1998 ; Torrance et al. , 2008 ) with numerous samples and responses representing different levels of creativity. The guides show sample evaluations based upon specific indicators such as fluency, originality, elaboration (or complexity), unusual visualization, extending or breaking boundaries, humor, and imagery. These examples are easy to use and provide a high degree of validity and generalizability to the tests. The TTCT has been more intensively researched and analyzed than any other creativity instrument, and the norming samples have longitudinal validations and high predictive validity over a wide age range. In addition to global creativity scores, the TTCT is designed to provide outcome measures in various domains and thematic areas to allow for more insightful analysis ( Kaufman and Baer, 2006 ). Kim (2006) has examined the characteristics of the TTCT, including norms, reliability, and validity, and concludes that the test is an accurate measure of creativity. When properly used, it has been shown to be fair in terms of gender, race, community status, and language background. According to Kim (2006) and other authorities in the field ( McIntyre et al. , 2003 ; Scott et al. , 2004 ), Torrance's research and the development of the TTCT have provided groundwork for the idea that creative levels can be measured and then increased through instruction and practice.

SCIENTIFIC TEACHING TO PROMOTE CREATIVITY

How could creativity instruction be integrated into scientific teaching.

Guidelines for designing specific course units that emphasize HOCS by using strategies of scientific teaching are now available from the current literature. As an example, Karen Cloud-Hansen and colleagues ( Cloud-Hansen et al. , 2008 ) describe a course titled, “Ciprofloxacin Resistance in Neisseria gonorrhoeae .” They developed this undergraduate seminar to introduce college freshmen to important concepts in biology within a real-world context and to increase their content knowledge and critical-thinking skills. The centerpiece of the unit is a case study in which teams of students are challenged to take the role of a director of a local public health clinic. One of the county commissioners overseeing the clinic is an epidemiologist who wants to know “how you plan to address the emergence of ciprofloxacin resistance in Neisseria gonorrhoeae ” (p. 304). State budget cuts limit availability of expensive antibiotics and some laboratory tests to patients. Student teams are challenged to 1) develop a plan to address the medical, economic, and political questions such a clinic director would face in dealing with ciprofloxacin-resistant N. gonorrhoeae ; 2) provide scientific data to support their conclusions; and 3) describe their clinic plan in a one- to two-page referenced written report.

Throughout the 3-wk unit, in accordance with the principles of problem-based instruction ( Duch et al. , 2001 ), course instructors encourage students to seek, interpret, and synthesize their own information to the extent possible. Students have access to a variety of instructional formats, and active-learning experiences are incorporated throughout the unit. These activities are interspersed among minilectures and give the students opportunities to apply new information to their existing base of knowledge. The active-learning activities emphasize the key concepts of the minilectures and directly confront common misconceptions about antibiotic resistance, gene expression, and evolution. Weekly classes include question/answer/discussion sessions to address student misconceptions and 20-min minilectures on such topics as antibiotic resistance, evolution, and the central dogma of molecular biology. Students gather information about antibiotic resistance in N. gonorrhoeae , epidemiology of gonorrhea, and treatment options for the disease, and each team is expected to formulate a plan to address ciprofloxacin resistance in N. gonorrhoeae .

In this project, the authors assessed student gains in terms of content knowledge regarding topics covered such as the role of evolution in antibiotic resistance, mechanisms of gene expression, and the role of oncogenes in human disease. They also measured HOCS as gains in problem solving, according to a rubric that assessed self-reported abilities to communicate ideas logically, solve difficult problems about microbiology, propose hypotheses, analyze data, and draw conclusions. Comparing the pre- and posttests, students reported significant learning of scientific content. Among the thinking skill categories, students demonstrated measurable gains in their ability to solve problems about microbiology but the unit seemed to have little impact on their more general perceived problem-solving skills ( Cloud-Hansen et al. , 2008 ).

What would such a class look like with the addition of explicit creativity-promoting approaches? Would the gains in problem-solving abilities have been greater if during the minilectures and other activities, students had been introduced explicitly to elements of creative thinking from the Sternberg and Williams (1998) list described above? Would the students have reported greater gains if their instructors had encouraged idea generation with weekly brainstorming sessions; if they had reminded students to cross-fertilize ideas by integrating material across subject areas; built self-efficacy by helping students believe in their own capacity to be creative; helped students question their own assumptions; and encouraged students to imagine other viewpoints and possibilities? Of most relevance, could the authors have been more explicit in assessing the originality of the student plans? In an experiment that required college students to develop plans of a different, but comparable, type, Osborn and Mumford (2006) created an originality rubric ( Figure 2 ) that could apply equally to assist instructors in judging student plans in any course. With such modifications, would student gains in problem-solving abilities or other HOCS have been greater? Would their plans have been measurably more imaginative?

Figure 2. Originality rubric (adapted from Osburn and Mumford, 2006 , p. 183).

Answers to these questions can only be obtained when a course like that described by Cloud-Hansen et al. (2008) is taught with explicit instruction in creativity of the type I described above. But, such answers could be based upon more than subjective impressions of the course instructors. For example, students could be pretested with items from the TTCT-Verbal or TTCT-Figural like those shown. If, during minilectures and at every contact with instructors, students were repeatedly reminded and shown how to be as creative as possible, to integrate material across subject areas, to question their own assumptions and imagine other viewpoints and possibilities, would their scores on TTCT posttest items improve? Would the plans they formulated to address ciprofloxacin resistance become more imaginative?

Recall that in their meta-analysis, Scott et al. (2004) found that explicitly informing students about the nature of creativity and offering strategies for creative thinking were the most effective components of instruction. From their careful examination of 70 experimental studies, they concluded that approaches such as social modeling, cooperative learning, and case-based (project-based) techniques that required the application of newly acquired knowledge were positively correlated with high effect sizes. The study was clear in confirming that explicit creativity instruction can be successful in enhancing divergent thinking and problem solving. Would the same strategies work for courses in ecology and environmental biology, as detailed by Ebert-May and Hodder (2008) , or for a unit elaborated by Knight and Wood (2005) that applies classroom response clickers?

Finally, I return to my opening question with the fictional Dr. Dunne. Could a weekly brainstorming “invention session” included in a course like those described here serve as the site where students are introduced to concepts and strategies of creative problem solving? As frequently applied in schools of engineering ( Paulus and Nijstad, 2003 ), brainstorming provides an opportunity for the instructor to pose a problem and to ask the students to suggest as many solutions as possible in a brief period, thus enhancing ideational fluency. Here, students can be encouraged explicitly to build on the ideas of others and to think flexibly. Would brainstorming enhance students' divergent thinking or creative abilities as measured by TTCT items or an originality rubric? Many studies have demonstrated that group interactions such as brainstorming, under the right conditions, can indeed enhance creativity ( Paulus and Nijstad, 2003 ; Scott et al. , 2004 ), but there is little information from an undergraduate science classroom setting. Intellectual Ventures, a firm founded by Nathan Myhrvold, the creator of Microsoft's Research Division, has gathered groups of engineers and scientists around a table for day-long sessions to brainstorm about a prearranged topic. Here, the method seems to work. Since it was founded in 2000, Intellectual Ventures has filed hundreds of patent applications in more than 30 technology areas, applying the “invention session” strategy ( Gladwell, 2008 ). Currently, the company ranks among the top 50 worldwide in number of patent applications filed annually. Whether such a technique could be applied successfully in a college science course will only be revealed by future research.

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Submitted: 31 December 2008 Revised: 14 May 2009 Accepted: 28 May 2009

© 2009 by The American Society for Cell Biology

Development of Problem Solving Skills with Maple in Higher Education

• Conference paper
• First Online: 20 July 2021
• Cite this conference paper

• Cecilia Fissore   ORCID: orcid.org/0000-0001-8398-265X 8 ,
• Marina Marchisio   ORCID: orcid.org/0000-0003-1007-5404 9 ,
• Fabio Roman   ORCID: orcid.org/0000-0002-6021-0275 9 &
• Matteo Sacchet   ORCID: orcid.org/0000-0002-5630-0796 9  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1414))

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Problem solving is the ability to understand the environment, identify complex problems, and review related information to develop, evaluate strategies and implement solutions to build the desired outcome. Mathematics boosts problem solving skills and, in Higher Education, all scientific degree programs deliver at least one module in Mathematics that should develop students’ problem solving skills. Mathematics Modules of the Biotechnology Bachelor Degree and of the Strategic Science Bachelor and Master Degrees at the University of Turin use innovative digital technologies, like the Advanced Computing Environment Maple, and methodologies to facilitate the learning of Mathematics and the development of problem solving skills. At the beginning of the courses, students must learn how to use Maple through dedicated lab sessions to solve contextualized problems related to their future careers. Moreover, for the final examination, students must study, present and discuss a science-based problem solved with Maple. In this paper, we investigated how the use of Maple enabled students to develop problem solving skills. We examined 110 students’ submissions through a rubric that analyzes different dimensions: comprehension, resolution strategy identified, solution process, representation, argument, use of Maple. Dimensions are correlated with module attendance, involvement, exam marks. A qualitative analysis was also performed. The research shows that the adopted approach is useful and effective: students’ scores are high and submissions indicate the presence of problem solving skills. Problem solving labs with Maple should be introduced, in connection with other disciplines, to facilitate analysis of data, visualization, communication, and deep understanding of concepts.

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Fissore, C., Marchisio, M., Roman, F., Sacchet, M. (2021). Development of Problem Solving Skills with Maple in Higher Education. In: Corless, R.M., Gerhard, J., Kotsireas, I.S. (eds) Maple in Mathematics Education and Research. MC 2020. Communications in Computer and Information Science, vol 1414. Springer, Cham. https://doi.org/10.1007/978-3-030-81698-8_15

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Creative Problem Solving in Large Language and Vision Models – What Would it Take?

We advocate for a strong integration of Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., creative problem solving. We present preliminary experiments showing how CC principles can be applied to address this limitation. Our goal is to foster discussions on creative problem solving in LLVMs and CC at prestigious ML venues.

Lakshmi Nair Georgia Institute of Technology Atlanta, GA, USA                        Evana Gizzi Tufts University Medford, MA, USA                        Jivko Sinapov Tufts University Medford, MA, USA

1 Introduction

Creativity is “ …the ability to come up with an idea which, relative to the pre-existing domain-space in one’s mind, one could not have had before. Whether any other person (or system) has already come up with it on an earlier occasion is irrelevant. ” Boden ( 1998 ) , p.216. For artificial agents, Computational Creativity (CC) is a multi-disciplinary field (spanning Philosophy, Psychology, Neuroscience, and Computer Science) that seeks to develop computational methods capable of generating creative outcomes reminiscent of creative processes in humans Gizzi et al. ( 2022 ) . Within CC, creative problem solving is a sub-area that requires an agent to discover – from its perspective – novel and previously unseen ways to accomplish a task. For example, in the absence of a ladle to scoop ingredients, an agent might creatively choose to substitute a bowl in place of the ladle. In this sense, creative problem solving encompasses creativity that is specifically task-oriented , as opposed to the generation of creative artifacts e.g., music or images.

While recent state-of-the-art large language models (LLMs) and vision-language models (VLMs) have demonstrated competency in artistic endeavours Rombach et al. ( 2021 ); Copet et al. ( 2023 ) , creative problem solving continues to be a shortcoming of these models (we use LLVM to denote the umbrella of both LLMs and VLMs). For instance, in Bubeck et al. ( 2023 ) , the authors point out that “discontinuous tasks” that require a certain “Eureka” idea, i.e., creative problem solving, is currently a limitation of models like GPT-4. Similar observations have been made in follow up work showing that state-of-the-art LLMs inherently possess poor creative problem solving capabilities compared to humans Tian et al. ( 2023 ); Naeini et al. ( 2023 ) . Given this obvious limitation, ongoing research in Machine Learning should seek to address the gap between LLVMs and creative problem solving, to further enhance the intelligent capabilities of these models. As defined in prior work, “ Intelligence is the ability to work and adapt to the environment with insufficient knowledge and resources. ” Pennachin and Goertzel ( 2007 ) , p.10. Demonstrated in hallmark examples of human ingenuity, like the makeshift C ⁢ O 2 𝐶 subscript 𝑂 2 CO_{2} italic_C italic_O start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT filter built onboard the Apollo-13 Cass ( 2005 ) , or the makeshift medical devices used to offset equipment shortages during COVID-19 Turner et al. ( 2020 ) , creative problem solving is especially important when dealing with resource-critical scenarios. Since humans may tend to “choke” under high pressure situations DeCaro et al. ( 2011 ) often limiting their CPS skills, autonomous agents equipped with LLVMs that have similar capabilities would be highly assistive and transformative to humans in high-stake environments. These include situations like rescue missions BBC ( 2012 ) or autonomous operation in human-inaccessible environments (e.g., space or underwater exploration) with limited resources Atkeson et al. ( 2018 ) . However, the exceptional degree of creative problem solving necessary for such assistance remains beyond the scope of LLVMs today, limiting their intelligence (See Appx. B.1 ).

We believe that a discussion of Computational Creativity is essential to addressing this limitation. It is our position that Machine Learning and Computational Creativity should be strongly integrated in research to enable effective creative problem solving in LLVMs and push the frontiers of their ingenuity.

2 Two Cultures Problem: Why does CC not receive a wider reception in ML?

Even though creative problem solving (CPS) is a shortcoming of existing LLVMs, Computational Creativity seldom finds its way into mainstream ML research. We believe this discrepancy aligns with the “two cultures” problem Hammond et al. ( 2013 ) (also corroborated in Van Heerden and Bas ( 2021 ); Lahikainen et al. ( 2024 ) ), and is motivated by three aspects of CC literature as it relates to creative problem solving: a) the lack of a precise definition of CPS makes it challenging to identify how existing approaches in LLVMs are deficient in CPS skills; b) the somewhat “abstract” computational descriptions of CPS in Computational Creativity is challenging to connect to practical algorithms in LLVMs; and c) the lack of standardized benchmarks make it harder to evaluate LLVMs for CPS. In our discussions relating to a) in Section 3.1 , b) in Section 4 , and c) Section 5 , we hope to address these gaps and encourage the ML community to think about how LLVMs can be augmented with creative problem solving skills through a deeper discussion of Computational Creativity.

To emphasize the applicability of principles from CC for creative problem solving in LLVMs, we discuss the seminal work of Margaret A. Boden from CC literature that introduces three forms of creativity, namely, “ exploratory ”, “ combinational ”, and “ transformational ” Boden ( 1998 ) . Prior work has discussed the extension of Boden’s forms of creativity to creative problem solving in AI Gizzi et al. ( 2022 ) , however, their work does not include recent advances in LLVMs nor how Boden’s principles can be extended to specific approaches for LLVMs.

Ongoing discussions by leading ML experts like Dr. Shane Legg, co-founder of DeepMind, have suggested that “search” could help such models perform creative problem solving, quote, “ … these foundational models are world models of a kind, and to do really creative problem solving, you need to start searching ” Patel ( 2023 ) . There has also been speculation that OpenAI’s Q ∗ superscript 𝑄 Q^{*} italic_Q start_POSTSUPERSCRIPT ∗ end_POSTSUPERSCRIPT search (described as a “significant breakthrough” in popular media) could be targeting a similar approach Wang ( 2023 ); Anna Tong and Hu ( 2023 ) . Interestingly, we note that “search” as described here, can be linked to Boden’s proposed “exploratory” approach (Section 4.1.1 ). However, in Section 4 , we posit that “combinational” and “transformational” modes should be equally emphasized to achieve creative problem solving in LLVMs.

Although we choose to expand on Boden’s work as the focal point to drive our arguments in the main paper, it is not the only theory in CC that is relevant to this discussion. For completeness, we elaborate on additional CC theories and their applicability to creative problem solving in LLVMs in Appx. B .

3 From Task Planning to Creative Problem Solving

Creative problem solving can be broadly described as the process through which agents discover novel ways of accomplishing a task that, prior to the discovery, was unsolvable. Computationally, creative problem solving can be achieved through planning, learning, or hybrid approaches Gizzi et al. ( 2022 ) . Following a review of the different definitions of creative problem solving that have been proposed (Appx. A ), we believe the following most closely connects to existing formalisms in ML.

3.1 Definition of Creative Problem Solving

Gizzi et al. ( 2022 ) define the notion of a concept , as a state (of the environment and/or agent) or action. More generally, the authors denote C X subscript 𝐶 𝑋 C_{X} italic_C start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT as the set of all concepts relating to X 𝑋 X italic_X ( X 𝑋 X italic_X denotes environment states S 𝑆 S italic_S or actions A 𝐴 A italic_A ). Hence, C S subscript 𝐶 𝑆 C_{S} italic_C start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT denotes the set of all environmental states, and C A subscript 𝐶 𝐴 C_{A} italic_C start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT denotes the set of agent actions. Formally, the authors state their definition as (Page 7, (Gizzi et al., 2022 ) ):

Given an un-achievable goal due to an insufficient conceptual space, CPS refers to the process by which the agent discovers a new conceptual space C X ′ ⊈ C X not-subset-of-nor-equals subscript superscript 𝐶 ′ 𝑋 subscript 𝐶 𝑋 C^{\prime}_{X}\nsubseteq C_{X} italic_C start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ⊈ italic_C start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT , such that C X ′ = f ⁢ ( C X ) subscript superscript 𝐶 ′ 𝑋 𝑓 subscript 𝐶 𝑋 C^{\prime}_{X}=f(C_{X}) italic_C start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT = italic_f ( italic_C start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ) is the result of applying some function f 𝑓 f italic_f on the current conceptual space, enabling the agent to solve the previously unsolvable task by using C X ′ subscript superscript 𝐶 ′ 𝑋 C^{\prime}_{X} italic_C start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT .

As a simplified example, let us assume a robot that has a goal G 𝐺 G italic_G of transferring beans from a jar to a cooker: G = 𝐺 absent G= italic_G = { i ⁢ n 𝑖 𝑛 in italic_i italic_n (beans, cooker)}. Here, the initial state is defined as C S = subscript 𝐶 𝑆 absent C_{S}= italic_C start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT = { i ⁢ n 𝑖 𝑛 in italic_i italic_n (beans, jar), h ⁢ a ⁢ s ⁢ C ⁢ o ⁢ n ⁢ t ⁢ a ⁢ i ⁢ n ⁢ a ⁢ b ⁢ i ⁢ l ⁢ i ⁢ t ⁢ y ℎ 𝑎 𝑠 𝐶 𝑜 𝑛 𝑡 𝑎 𝑖 𝑛 𝑎 𝑏 𝑖 𝑙 𝑖 𝑡 𝑦 hasContainability italic_h italic_a italic_s italic_C italic_o italic_n italic_t italic_a italic_i italic_n italic_a italic_b italic_i italic_l italic_i italic_t italic_y (spoon)}. Let the actions be defined as C A = subscript 𝐶 𝐴 absent C_{A}= italic_C start_POSTSUBSCRIPT italic_A end_POSTSUBSCRIPT = { s ⁢ c ⁢ o ⁢ o ⁢ p 𝑠 𝑐 𝑜 𝑜 𝑝 scoop italic_s italic_c italic_o italic_o italic_p (beans, X 𝑋 X italic_X , l ⁢ o ⁢ c s 𝑙 𝑜 subscript 𝑐 𝑠 loc_{s} italic_l italic_o italic_c start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT , l ⁢ o ⁢ c d 𝑙 𝑜 subscript 𝑐 𝑑 loc_{d} italic_l italic_o italic_c start_POSTSUBSCRIPT italic_d end_POSTSUBSCRIPT )}, where, X 𝑋 X italic_X refers to an object that satisfies h ⁢ a ⁢ s ⁢ C ⁢ o ⁢ n ⁢ t ⁢ a ⁢ i ⁢ n ⁢ a ⁢ b ⁢ i ⁢ l ⁢ i ⁢ t ⁢ y ⁢ ( ⋅ ) ℎ 𝑎 𝑠 𝐶 𝑜 𝑛 𝑡 𝑎 𝑖 𝑛 𝑎 𝑏 𝑖 𝑙 𝑖 𝑡 𝑦 ⋅ hasContainability(\cdot) italic_h italic_a italic_s italic_C italic_o italic_n italic_t italic_a italic_i italic_n italic_a italic_b italic_i italic_l italic_i italic_t italic_y ( ⋅ ) (e.g., spoon), to scoop beans from l ⁢ o ⁢ c s 𝑙 𝑜 subscript 𝑐 𝑠 loc_{s} italic_l italic_o italic_c start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT to l ⁢ o ⁢ c d 𝑙 𝑜 subscript 𝑐 𝑑 loc_{d} italic_l italic_o italic_c start_POSTSUBSCRIPT italic_d end_POSTSUBSCRIPT . If the robot has access to a spoon, the robot can use it to scoop the beans from the jar to the cooker. However, what if the robot did not have a spoon, but had a glass instead? By the definition of C S subscript 𝐶 𝑆 C_{S} italic_C start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT , the agent is unaware that h ⁢ a ⁢ s ⁢ C ⁢ o ⁢ n ⁢ t ⁢ a ⁢ i ⁢ n ⁢ a ⁢ b ⁢ i ⁢ l ⁢ i ⁢ t ⁢ y ℎ 𝑎 𝑠 𝐶 𝑜 𝑛 𝑡 𝑎 𝑖 𝑛 𝑎 𝑏 𝑖 𝑙 𝑖 𝑡 𝑦 hasContainability italic_h italic_a italic_s italic_C italic_o italic_n italic_t italic_a italic_i italic_n italic_a italic_b italic_i italic_l italic_i italic_t italic_y (glass) is true, making the goal un-achievable. By our definition, creative problem solving is the process by which the agent uses some function f ⁢ ( ⋅ ) 𝑓 ⋅ f(\cdot) italic_f ( ⋅ ) to discover a new conceptual space: f ⁢ ( C S ) = C S ′ = C S ⁢ ∪ 𝑓 subscript 𝐶 𝑆 subscript superscript 𝐶 ′ 𝑆 subscript 𝐶 𝑆 f(C_{S})=C^{\prime}_{S}=C_{S}\mathop{\cup} italic_f ( italic_C start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT ) = italic_C start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT = italic_C start_POSTSUBSCRIPT italic_S end_POSTSUBSCRIPT ∪ { h ⁢ a ⁢ s ⁢ C ⁢ o ⁢ n ⁢ t ⁢ a ⁢ i ⁢ n ⁢ a ⁢ b ⁢ i ⁢ l ⁢ i ⁢ t ⁢ y ℎ 𝑎 𝑠 𝐶 𝑜 𝑛 𝑡 𝑎 𝑖 𝑛 𝑎 𝑏 𝑖 𝑙 𝑖 𝑡 𝑦 hasContainability italic_h italic_a italic_s italic_C italic_o italic_n italic_t italic_a italic_i italic_n italic_a italic_b italic_i italic_l italic_i italic_t italic_y  (glass)}. This would allow the agent to solve the previously unsolvable task by using the glass to scoop the beans instead.

Boden’s three forms of creativity denote three plausible functions for f ⁢ ( C X ) 𝑓 subscript 𝐶 𝑋 f(C_{X}) italic_f ( italic_C start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ) . CPS arises when the agent uses what it knows, to discover something new and the newly discovered knowledge is applied to solve a previously impossible task. We revisit the notion of conceptual spaces in Section 3.

In the remainder of this section, we discuss how typical task planning is achieved with LLVMs. We divide the discussion into three subsections based on the level of task planning abstraction where LLVMs are applied: a) high-level task planning, b) low-level task planning, and c) hybrid task planning. While not exhaustive, our review is meant to offer a general insight into how LLVMs are used for task planning, to identify entry points for introducing creative problem solving capabilities.

3.2 LLVMs for high-level task planning

Approaches for high-level task planning often involve using LLVMs to identify high-level goals for accomplishing a task. Some approaches to task planning with LLMs often take a user input specifying the task, and generate high-level task plans for accomplishing it. These approaches often use LLMs as a form of “knowledge base”, to extract actionable task plans from the models via appropriate prompting Huang et al. ( 2022 ) , further iterating over the generated task plan with repeated calls to the LLM as needed Prasad et al. ( 2023 ) .

In the context of Reinforcement Learning (RL), prior work has focused on using LLMs to suggest high-level goals for an RL agent Du et al. ( 2023 ) . Dubbed as ELLMs (Exploring with LLMs), an RL agent provides its current state to an LLM via a prompt, and receives a goal suggestion from the LLM that is then used to shape the reward and the agent exploration. Further work has extended this approach to incorporate the use of experience memory Zhang et al. ( 2023a ) . Existing approaches have also used LLMs to generate directed acyclic graphs composed of sub-goal states to aid the exploration of an RL agent Shukla et al. ( 2023 ) .

3.3 LLVMs for low-level task planning

Approaches for low-level task planning involve using LLMs to generate low-level code for performing a task. In contrast to high-level planning, where high-level goals and sub-goals are generated, these approaches use LLMs to directly generate low-level execution code via appropriate API calls Liang et al. ( 2023 ) . Other approaches have also investigated the capacity of LLMs to generate task plans via a low-level planning language such as PDDL Silver et al. ( 2023 ) , including iterating over the generated plan descriptions in case of errors Guan et al. ( 2023 ) . In terms of low-level planning using VLMs, prior work has introduced an approach that uses a diffusion model to generate robot trajectories conditioned on language and the current visual state of the robot Chen et al. ( 2023 ) .

3.4 Hybrid high and low-level planning with LLVMs

Hybrid approaches use LLVMs both for high-level goal generation as well as low-level planning. For instance, in Li et al. ( 2023 ) , user inputs are passed as LLM prompts to generate high-level plans. The high-level plans are then converted to low-level plans for robot execution via LLMs specialized for coding. Other approaches have used a high-level LLM planner, a VLM perceiver, and a low-level LLM planner for re-planning with both visual and language inputs Skreta et al. ( 2024 ) .

3.5 Summary

Given this overview, we see that LLVMs both at the high-level and low-level, can be modified to incorporate creative problem solving into task planning. For instance, the high-level task plans generated can encompass a novel substitution for a missing object, whereas the low-level task plan can generate an appropriate trajectory for creatively using the object. While the above approaches could, in principle, be studied within the framework of creative problem solving, that is not usually how the problem is formulated; there is a lack of paradigms for studying creative problem solving beyond just, “do you solve the problem or not?” . Creative problem solving needs a fundamental rethinking of the typical problem formulations and approaches in ML. The next section is aimed at ways in which ML approaches in LLVMs can be reformulated from the perspective of CC.

4 Augmenting LLVM embedding spaces for creative problem solving

In this section, we discuss how principles from CC can be extended to LLVMs for creative problem solving. We begin with Boden’s definition of “conceptual spaces” as “ [conceptual space] is the generative system that underlies the domain and defines a certain range of possibilities: chess moves, or molecular structures, or jazz melodies ” Boden ( 2005 ) , p.18 and “ … in short, any reasonably disciplined way of thinking ” Boden ( 1998 ) , p.214. By this definition, the embedding space of an LLVM describes its conceptual space or “ its way of thinking ”. Some evidence for this also comes from existing work that introduces an approach for enabling LLMs to interpret continuous embedding spaces via natural language. Given an embedding vector representing an interpolation of different concepts, the model is able to interpret a text prompt in the context of the supplied embedding Tennenholtz et al. ( 2023 ) . The embedding thus determines the model’s way of thinking. Hence, a discussion of enabling creative problem solving in LLVMs should target their embedding space. To this end, we explore two questions: a) how can LLVM embedding spaces be augmented to achieve creative problem solving, and b) what information should they be augmented with? Aligning with our original position, we show that CC literature can offer insights into these questions.

4.1 How can LLVM embedding spaces be augmented?

In this section, we draw parallels between Boden’s three forms of creativity and existing approaches in LLVMs. We further elaborate on how the three forms of creativity may enhance the potential of LLVMs to perform creative problem solving. We note that the ML approaches discussed in this section do not specifically perform creative problem solving. However, we discuss how they could potentially be extended to do so, by leveraging references from the CC literature.

4.1.1 Exploratory Creativity

Exploratory approaches involve exploration within the conceptual or equivalently, the embedding space of the model, and most closely relates to “search”. Note that the term “exploration” here differs from its usage in RL, instead referring to exploration through the model’s embedding space . Several existing approaches in the ML literature involve searching the output space of LLMs with the goal of improving the performance of these models. The “tree-of-thought” model generates a “tree” of next possible LLM outputs, and searches through the states via Breadth-first or Depth-first search to reach the desired goal state, often guided by heuristics Yao et al. ( 2023 ) . Numerous other approaches have built upon a similar strategy, such as using Monte-Carlo Tree Search (MCTS) Zhou et al. ( 2023 ); Feng et al. ( 2023 ) , beam search Zhang et al. ( 2023b ) or integrating pruning to remove sub-par candidates Golovneva et al. ( 2023 ) .

Extension of exploratory creativity to LLVMs: An important point to note here is that these approaches involve searching exclusively within the output “solution space” of the LLMs rather than directly operating in the embedding space itself. In contrast to operating in the solution space of the LLM, exploratory approaches directly within the LLMs’ embedding space would not be limited by what the LLM can generate as output – “ Some exploration merely shows us the nature of the relevant conceptual space that we had not explicitly noticed before ” Boden ( 2005 ) , p.18. To effectively reveal the full extent of the conceptual space for creative problem solving, the approach should not be limited by the outputs the LLVM can generate. Rather, the generated (creative) outputs itself should be the result of heuristic or non-heuristic based search within the model’s embedding space. However, to the best of our knowledge current approaches have not focused on LLVMs from this perspective, and have also not applied search to embedding spaces of Vision-LMs. Regardless, exploratory approaches are still limited by the dimensions of the model’s embedding space. “ To overcome a limitation in the conceptual space, one must change it in some way ” Boden ( 2005 ) , p.18 - this leads us to combinational and transformational creativity.

4.1.2 Combinational Creativity

Combinational approaches involve combining two concepts to create something new - “ A novel combination of two familiar ideas is something which did not happen before. ” Boden ( 1998 ) , p.213. We can broadly translate this to a function that takes in multiple concepts within an LLVM’s embedding space to output a novel concept.

One way of extending this definition to LLVMs involves applying cross-attention layers. The attention operation is defined as Vaswani et al. ( 2017 ) :

where, Q 𝑄 Q italic_Q , K 𝐾 K italic_K and V 𝑉 V italic_V denote query, keys and values respectively, and d k subscript 𝑑 𝑘 d_{k} italic_d start_POSTSUBSCRIPT italic_k end_POSTSUBSCRIPT denotes the dimensionality of the keys. Cross-attention involves passing K 𝐾 K italic_K and V 𝑉 V italic_V from a different model, e.g., in Flamingo Alayrac et al. ( 2022 ) , the keys and values represent visual input (from a separate vision encoder) and queries represent a language input. By applying cross attention in this manner, the embedding space of a model can be extended with capabilities of another model. In Bansal et al. ( 2024 ) the authors show that using cross-attention layers can help augment an anchor LLM with an augmenting LLM’s capabilities to perform a task that the anchor LLM was incapable of achieving before - hinting at some creative possibilities of this method.

Other approaches in LLVMs, while using “combinations” in some way, do not conform to the notion of combinational creativity . This includes, for instance, approaches that perform arithmetic combination of LLM weights to enhance the model performance Matena and Raffel ( 2022 ); Ilharco et al. ( 2022 ) . Or approaches that combine image and text embeddings via concatenation Kim et al. ( 2021 ) or a scaled dot product at the output Radford et al. ( 2021 ) . While these approaches may be useful in imparting multi-modal capabilities, however, they do not lead to combinational creativity since the combination occurs external to the models as opposed to within the model’s embedding space.

Extension of Combinational Creativity to LLVMs: The ML approaches described here involve combining embedding spaces across models. Existing approaches have not looked at combining concepts within the same model’s embedding space. The extension of combinational creativity to LLVMs is much more apparent in the sense of conceptual blending Fauconnier and Turner ( 2003 ) for generation of creative artifacts, e.g., via blending of artistic styles. However, the extension of combinational creativity to creative problem solving is less obvious, and CC literature offers us further insights for making this connection. Typical conceptual blending corresponds to a form of “aesthetic combination”, whereas creative problem solving would benefit from “functional combinations” Chen et al. ( 2018 ) . Functional combination combines the functions (as opposed to aesthetic) of two components, e.g., a coin combined with pliers could function as a makeshift screwdriver. The authors extend this framework to a combination of two nouns with a “base” noun (e.g., “pliers”) and “additive” noun (e.g., “coin”). An interesting possibility stems from this notion: Can a combination of embeddings of the same LLVM, corresponding to “base” and “additive” nouns (perhaps with some prior denoting the task), enable the LLVM to generate creative combinations of objects for solving a task? This question remains unexplored, and points to a potential research direction for LLVMs inspired by CC.

4.1.3 Transformational Creativity

Transformational approaches involve transforming existing conceptual spaces to produce new ones. Transforming conceptual spaces can involve “ altering existing rules ” Boden ( 1998 ) , p.216. One way of transforming a model’s embedding space involves fine-tuning or training Franceschelli and Musolesi ( 2023 ) . However, additional insight into transformational creative problem solving comes from prior work in CC, that describes creative problems as those with a poorly defined structure where a solution is not immediately apparent Olteteanu ( 2014 ) . And in such cases, “… re-representation being the process which transforms an ill-structured problem into a well-structured one with direct inference to a problem solution ” Olteteanu ( 2014 ) , p.1. The notion of “re-representing” or “redefining” the problem can be best captured in the input prompts provided to an LLVM. This most closely connects to prompt engineering and in-context learning (ICL).

Prompt engineering augments LLVMs with task specific hints, called prompts, to adapt the LLVM to new tasks Gu et al. ( 2023 ) . Relatedly, in-context learning is a prompting method that provides the LLVM with instructions for solving a new task without requiring additional training. Prior work has shown that in-context learning and gradient-based optimization are equivalent Von Oswald et al. ( 2023 ) , thus connecting ICL to training or fine-tuning.

Extension of transformational creativity to LLVMs: Task re-representations for creative problem solving, through prompting or ICL, has not been well explored within ML. Prompt engineering and ICL is a challenging task, since model performance depends strongly on the chosen prompts Rubin et al. ( 2021 ) , further compounded by the fact that creative problems are inherently poorly defined Olteteanu ( 2014 ) . However, useful insights can be derived from CC literature. For instance, regarding problems that require creatively re-purposing objects, the Object-replacement-object-composition (OROC) framework Olteţeanu and Falomir ( 2016 ) illustrates re-representations of tasks, that can be translated into prompts. The paper defines three different types of creative tasks involving objects, and their task re-representations as (from Olteţeanu and Falomir ( 2016 ) , p.16):

Replace an unfound object needed for a task with other objects present in the environment: “If I do not have an object X, which I would normally use because of its affordance 1 1 1 Affordance is defined as the relation between an agent, action and object, e.g., bowls have the “contain” affordance for humans. A ⁢ f X 𝐴 subscript 𝑓 𝑋 Af_{X} italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT , what other object Y could I use, so that I can get a similar affordance, A ⁢ f X ≈ A ⁢ f Y 𝐴 subscript 𝑓 𝑋 𝐴 subscript 𝑓 𝑌 Af_{X}\approx Af_{Y} italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ≈ italic_A italic_f start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT ? ”

𝐴 subscript 𝑓 𝑌 1 𝐴 subscript 𝑓 𝑌 2 … 𝐴 subscript 𝑓 𝑌 𝑛 Af_{X}\approx Af_{X^{\prime}},Af_{X}\approx Af_{Y1}+Af_{Y2}+...+Af_{Yn} italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ≈ italic_A italic_f start_POSTSUBSCRIPT italic_X start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT end_POSTSUBSCRIPT , italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ≈ italic_A italic_f start_POSTSUBSCRIPT italic_Y 1 end_POSTSUBSCRIPT + italic_A italic_f start_POSTSUBSCRIPT italic_Y 2 end_POSTSUBSCRIPT + … + italic_A italic_f start_POSTSUBSCRIPT italic_Y italic_n end_POSTSUBSCRIPT ? ”

• subscript 𝑌 1 subscript 𝑌 2 … subscript 𝑌 𝑛 Y_{1};Y_{2};...;Y_{n} italic_Y start_POSTSUBSCRIPT 1 end_POSTSUBSCRIPT ; italic_Y start_POSTSUBSCRIPT 2 end_POSTSUBSCRIPT ; … ; italic_Y start_POSTSUBSCRIPT italic_n end_POSTSUBSCRIPT which are components of object Y 𝑌 Y italic_Y could I use to obtain an object Y i ′ subscript superscript 𝑌 ′ 𝑖 Y^{\prime}_{i} italic_Y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT with an equivalent or similar affordance, A ⁢ f X ≈ A ⁢ f Y ′ ⁢ i 𝐴 subscript 𝑓 𝑋 𝐴 subscript 𝑓 superscript 𝑌 ′ 𝑖 Af_{X}\approx Af_{Y^{\prime}i} italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT ≈ italic_A italic_f start_POSTSUBSCRIPT italic_Y start_POSTSUPERSCRIPT ′ end_POSTSUPERSCRIPT italic_i end_POSTSUBSCRIPT ? ”

For task re-representation, affordances can refer to object properties that are relevant to the task, e.g., in some cases the shape may be relevant and in other cases, the material Olteţeanu and Falomir ( 2016 ) . Within LLVMs, the affordances A ⁢ f X 𝐴 subscript 𝑓 𝑋 Af_{X} italic_A italic_f start_POSTSUBSCRIPT italic_X end_POSTSUBSCRIPT or A ⁢ f Y 𝐴 subscript 𝑓 𝑌 Af_{Y} italic_A italic_f start_POSTSUBSCRIPT italic_Y end_POSTSUBSCRIPT can be defined via natural language, or other modalities such as images. In the following section, we present preliminary experiments on using LLVMs for object replacement, with prompts that are inspired by the above task re-representations. However, an in-depth application of these re-representations as defined in CC to in-context learning in LLVMs remains unexplored.

4.1.4 Summary

In the previous sections, we drew parallels between Boden’s three forms of creativity and approaches in LLVMs, further emphasizing how principles from CC can potentially help enable creative problem solving skills in these models.

Integration with task planning: Given the three methods, we see that transformational and combinational approaches may be especially aligned with LLVMs for high-level task planning. In contrast, exploratory methods may be suited to low-level planning, e.g., trajectory generation.

Creative problem solving as a combination of the three methods: An effective approach to creative problem solving may require all the three methods described in this section. While papers have explored chaining of LLMs within frameworks (often via prompts) Karpas et al. ( 2022 ); Ling et al. ( 2023 ) , the individual LLMs themselves do not exhibit the characteristics described here. Existing frameworks in CC have shown that achieving creative problem solving would take a combination of all three methods, each of which is triggered in different contexts Olteteanu ( 2014 ) . This presents potential opportunities for ML approaches that develop frameworks using multiple LLVMs, e.g., extending CC frameworks such as “ CreaCogs ” Olteţeanu and Falomir ( 2016 ) can be highly beneficial for productive developments in ML.

4.2 What information should LLVM embeddings be augemented with?

In the previous section, we discussed three methods for augmenting LLVM embedding spaces. In this section, we explore the question: “What information should be targeted by the three methods when augmenting the embedding space for creative problem solving?”. In the previous section, we discussed this in the context of OROC. According to the OROC framework Olteţeanu and Falomir ( 2016 ) , information about object affordances could enable models to re-represent the task, such that the solution becomes evident. We propose a small experiment to validate whether the principles of transformational creativity from OROC are useful to LLVMs. We note that creativity can occur in various contexts, e.g., creatively solving a math problem or creatively playing a chess move, each of which would require different information. However, to facilitate the discussion in this paper, we focus our scope on tasks that require innovatively replacing missing objects (OROC Task #1).

Note on embeddings vs. concepts: Our work connects “conceptual spaces” (or “concepts”) as defined in Computational Creativity literature, to “embedding spaces” (or “embeddings”) as defined in typical LM literature. We use “concepts” and “embeddings” interchangeably in this context. We make this connection to note that existing methods in Computational Creativity that operate on conceptual spaces translate to ML algorithms that operate on the LM’s embedding space. In this section, we connect the concept of “affordances” to the “embeddings” of the LLVMs in our experiments. Our goal is to show how the model can be prompted via an approach inspired by transformational creativity, to connect affordances of two seemingly distinct objects, e.g., a bowl and a spoon that appear distinct, but share the containability affordance.

4.2.1 Experiment Setup

We create a simple experiment setup that tests the “object replacement” principle from OROC, where we create test sets composed of images of objects for replacing one of five core objects: “Scoop”, “Hammer”, “Spatula”, “Toothpick”, and “Pliers”. We create two groups of tests: a) a nominal group where the actual object itself is available in each test set and requires no replacement (which serves as a form of baseline), and b) an object replacement group, where the nominal tool is missing and a creative replacement object should be chosen.

For each group, we create test sets with 4 objects each, chosen from a set of RGB images of 16 objects (Appendix Figure 3 ). We create 10 such test sets per core object (total 50 samples per model). Each test set only includes one ground truth object, along with three other random objects that will not suit as an appropriate replacement. In the nominal group, the ground truth is the actual object itself. In the object replacement group, the replacements are chosen based on self-assessment of the authors as (core object → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW replacement): “Scoop” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Bowl”; “Hammer” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Saucepan”; “Spatula” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Knife”; “Toothpick” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Safety pin”; “Pliers” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Scissors”. For each test case, we pass the images in the test set along with a prompt. We record whether the ground truth object image was chosen by the model for the prompt (i.e., assigned highest output probability) 2 2 2 CLIP generates probabilities that given images correspond to a text. ViLT and LLaVA respond with a text, and we evaluate if the model responded “yes” with a high probability for the ground truth. .

The nominal group is subjected to one type of prompt: “ Can this object be used as a ⟨ c o r e _ o b j e c t ⟩ ? \bigl{\langle}core\_object\bigl{\rangle}? ⟨ italic_c italic_o italic_r italic_e _ italic_o italic_b italic_j italic_e italic_c italic_t ⟩ ? ”. In the object replacement group, each test case is subjected to four types of prompts:

Baseline (regular) prompt: Same prompt as used in the nominal cases to obtain a baseline.

Prompt prepended with affordance information: the prompt includes additional information about the desired object affordances specified as object features.

Prompt prepended with task information: the prompt includes additional information about the desired task.

Prompt prepended with task and affordance information: the prompt includes additional information on the task and object affordance.

Case #2 aligns with task re-representations of OROC, and we explore cases #3 and #4 for comparison. We formulate our affordance prompts as brief versions of OROC’s task re-representations. According to Olteţeanu and Falomir ( 2016 ) affordances can be defined using shape features, which we apply to the prompts here. The full set of prompts is shown in Appendix Table 2 . The models that we explore include versions of CLIP Radford et al. ( 2021 ) , LLaVA Liu et al. ( 2024 ) , and ViLT Kim et al. ( 2021 ) obtained from HuggingFace. We use different model sizes ( B ase, L arge, H uge) and patch sizes (14, 16, 32). The open-source code for reproducing our experiment results (including our dataset and test cases) is available at: https://github.com/lnairGT/creative-problem-solving-LLMs . Appendix C includes more details on the experiments.

4.2.2 Results

In Table 1 , we see the performances of the different models in the nominal test group, where the object requires no creative replacement. The models perform > 90 % absent percent 90 >90\% > 90 % in such cases (except for ViLT). In Figure 2 , we see the performances (accuracy shown on a 0.0 − 1.0 0.0 1.0 0.0-1.0 0.0 - 1.0 scale) of the models in the object replacement test cases, where the object requires a creative replacement. For reference, a model that randomly picks an object achieves about 30% overall accuracy. Figure 2 shows average accuracies for the different prompting strategies across random test sets. From Table 1 to Figure 2 (“regular”), the models perform poorly when they need to creatively reason about object replacements, highlighting their limitation. Comparing the “Regular” tab in Figure 2 to “Affordance”, we see a general improvement in model performances, when object affordance information is provided , consistent with description of the OROC framework Olteţeanu and Falomir ( 2016 ) . However, information about the task (Figure 2 , “Task” ) leads to mostly detrimental results. Information about task and affordances (Figure 2 , “Task + Affordance”) does not lead to substantial improvements either, and is also detrimental in certain cases. We note that there is quite a variance in performances across the different models, which may be partially attributed to the original training datasets of the models. These observations warrant further exploration beyond the scope of this paper. Appendix D includes a detailed, class-wise breakdown of the results.

4.2.3 Summary

While the experiments that we conducted are only preliminary, they offer some validity that the extension of principles in Computational Creativity can help overcome limitations of LLVMs in creative problem solving. The notion of task re-representation via improved prompting warrants further investigation in LLVMs, with regards to how the prompts can be generated automatically based on the creative task.

The models used in our experiments have all been trained jointly in visual and text domains. Multi-modal prompting capabilities may be useful for achieving creative problem solving. It can be quite challenging to describe affordances in words (example of “hammers” in our tests) and they may be better described through other means, e.g., images or depth maps or spectral data for material properties Erickson et al. ( 2020 ) . This would require application of multi-modal LLVMs that can process a variety of data types Girdhar et al. ( 2023 ); Han et al. ( 2023 ) . Computational creativity can offer insights into meaningful representations of these different modalities that would help achieve creative problem solving, e.g., whether object material or shape matters more for one task vs. another Olteţeanu and Falomir ( 2016 ) .

It is also worth noting that the creative problem solving examples in our experiments are human-centric. For instance, robots may not have similar capabilities as humans to manipulate bowls for scooping. In such cases, LLVMs need to account for the affordances as described with respect to the agent , in order to derive creative solutions. However, that adds another level of complexity, yet to be explored, since these models are typically trained on human-centric data.

5 Evaluation of Creativity

An important discussion in the context of creative problem solving is, how can creative problem solving be evaluated? . Prior work has proposed that creativity necessitates both novelty and value Boden ( 1998 ); Runco and Jaeger ( 2012 ) , where the former guarantees that the generated outputs of a creative process are original, and the latter ensures that the generated outputs are useful. In the context of CPS, novelty refers to the discovery of new concepts (as defined in section 3.1 ), whereas value insists that the newly discovered concepts successfully solve the task. Hence, benchmarks for CPS should specifically evaluate how the task was solved (novelty and value) rather than the typical ML evaluation of whether the task was successful or not (value only). Some existing approaches that make this distinction describe problem settings that can be used to measure CPS skills of LLMs through the implicit integration of novelty and value measurements Tian et al. ( 2023 ); Naeini et al. ( 2023 ); Bisk et al. ( 2020 ); Talmor et al. ( 2022 ) . In Tian et al. ( 2023 ) , the authors create a dataset of 1600 real-world problems that necessarily involve creative reasoning abilities. Their proposed benchmark involves identifying novel approaches that can accomplish the given task (value). Similarly, in Naeini et al. ( 2023 ) , the authors introduce the Only-Connect-Wall (OCW) dataset to measure CPS capabilities of LLMs. The authors in Bisk et al. ( 2020 ) explore physical commonsense reasoning that is more generally applicable, beyond object-based creative problems. The authors introduce Physical Interaction: Question Answering, or PIQA consisting of 16,000 QA pairs where each question is paired with two possible common-sense solutions with a ground truth. In Talmor et al. ( 2022 ) , the authors introduce CommonSenseQA 2.0 (CSQA2) dataset consisting of both object-based and non-object based creative problems. The dataset consists of 14,343 questions distributed across 1,868 distinct topics. Currently, to the best of our knowledge, there are no standard benchmarks available to measure CPS skills of VLMs, although our preliminary experiments show one way to measure this using the task of object substitution.

6 Conclusion and Future Work

In this paper, we argued that an effective approach for enabling creative problem solving – currently a key limitation of LLVMs – should derive from Computational Creativity literature. To emphasize this at each juncture, we discussed the specific principles from CC that can be extended to achieve creative problem solving in LLVMs, describing the potential for further research with these insights. It is rare to see special tracks or workshops targeted at Computational Creativity within more prestigious ML conferences. These programs typically focus on creative artifact generation and art (such as the NeurIPS Workshop on Machine Learning for Creativity and Design NeurIPS ( 2022 ) or the recent tutorial at EMNLP on Creative Natural Language Generation Chakrabarty et al. ( 2023 ) ), but do not discuss CPS, thus failing to bridge the gap between CC and ML. We hope to see a deeper integration of the CC communities at such strong ML venues. We hope to encourage the reader to view creative problem solving and ML holistically, through the lens of Computational Creativity.

7 Limitations

Literature outside of Computational Creativity that enables CPS is unexplored: Our paper predominantly focuses on CC literature. This work does not cover literature beyond CC that can potentially inform creative problem solving in LLVMs. Although CC literature broadly encompasses psychology, neuroscience and philosophy, our future work seeks to explore specific literature within these sub-domains and discuss their applicability to creative problem solving and ML.

Lack of an explicit creative problem solving algorithm for LLVMs: Since the scope of our work aligns with a position paper, we have not focused on developing a concrete algorithm for creative problem solving in LLVMs. The prompting strategies explored in our preliminary experiments are manually specified, and our work does not elaborate on how these prompts may be automatically discovered. While our paper seeks to address some of the key gaps that prevent the application of CC literature to ML, there are still several unanswered questions when it comes to the practical implementation of an ML approach: e.g., what is a good representation for concepts that facilitate creative problem solving (symbolic, non-symbolic, or hybrid)? What is a good problem formulation for a given creative problem solving task (planning or learning)? etc. However, these questions are not directly answered within the scope of our work.

8 Ethical Considerations

The authors do not have specific ethical considerations to be highlighted with respect to this work.

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Appendix A Alternate Definitions of Creative Problem Solving

Prior work by Olteţeanu Olteteanu ( 2014 ) defines CPS from an object affordance perspective, where affordances broadly refer to action possibilities for objects, e.g., cups are pour-able and doors are open-able. The authors in Olteteanu ( 2014 ) define creative problems as nominal problem solving tasks that have a poor representational structure, and as “ the ability of a cognitive, natural, or artificial system to use new objects to solve a problem, other than the ones that have been stored in its memory as tools for that specific purpose (if any), or to create those objects by putting together objects or parts of objects the system has access to. Depending on the problem, objects can be either physical or abstract/informational (concepts, problem templates, heuristics or other forms of representations) ”. However, this definition is primarily object-creativity centered, and does not cover a wider range of creative problems.

Follow-up work by Sarathy and Scheutz Sarathy and Scheutz ( 2018 ) , define “ Macgyver-esque ” creativity as a planning task that involves “ generating, executing, and learning strategies for identifying and solving seemingly unsolvable real-world problems ”. They introduce the “ MacGyver Problem ” (MGP) as a planning problem with an unreachable goal state. Through the modification of the agent’s domain knowledge (through domain expansion and domain contraction ), the agent must discover new information and incorporate it into its existing domain knowledge, allowing the agent to accomplish the task. The domain expansion and contraction processes align with the divergent-convergent model of creative problem solving Guilford ( 1967 ) . The definition of an MGP aligns well with the formulation of planning problems in ML, but less with learning or hybrid planning-learning approaches.

Appendix B Alternate theories on creative problem solving and their applications to ML

While there is exhaustive literature regarding theories on general creativity, we focus specifically on creative problem solving, with three well received works: Divergent-Convergent Thinking Guilford ( 1967 ) , Explicit-Implicit Interaction Theory Hélie and Sun ( 2010 ) , and the Creative Systems Framework Wiggins ( 2006 ) . We discuss their applicability to ML in addition to the literature discussed in the main body of this paper. Our goal in this section is to further widen the discussion on integrating CC and ML to achieve creative problem solving in LLVMs, with additional literature.

B.0.1 Divergent-Convergent Thinking

In Guilford ( 1967 ) , the authors discuss the notion of “divergent-convergent” thinking. Divergent thinking or “divergent-production” (DP) abilities involve a more open-ended generation of a variety of ideas, whereas convergent thinking focuses on applying specific ideas to solve the problem.

Applicability to CPS in LLVMs: Prior work by Tian et al. ( 2023 ) have demonstrated the applicability of “divergent-convergent” thinking towards solving Macgyver problems. Similar in spirit to our experiments with VLMs in Section 4.2.1 , the authors prompt LLMs with descriptions of objects to enable the LLMs to reason about solving the task. Their work is, to the best of our knowledge, the only direct example demonstrating the value of CC literature in enabling CPS in LLMs.

B.0.2 Explicit-Implicit Interaction Theory

In Hélie and Sun ( 2010 ) , the authors introduce the Explicit-Implicit Interaction (EII) theory, building upon the seminal work in Wallas ( 1926 ) , that describes four stages of creativity: Preparation, incubation, illumination (i.e., insight), and verification. Preparation refers to the initial stage of searching in many different directions, which may fail to find a solution (i.e., impasse) in case of ill-defined problems (as is the case with CPS). Following an impasse, the incubation phase begins, where attention is not devoted to solving the problem. Over a period of time, illumination is the manifestation of the solution to the problem within the conscious thought (i.e., “Aha” moment). Finally, verification involves using deliberative thinking to assess if the solution indeed solves the problem.

Applicability to CPS in LLVMs: The authors in Hélie and Sun ( 2010 ) incorporate the four stages via a concrete computational method into the CLARION cognitive architecture. Prior work has also introduced a CPS framework for ML approaches inspired by the four stages Gizzi et al. ( 2022 ) . In their work, “preparation” aligns with problem formulation, either task learning or planning. Incubation and illumination aligns with knowledge representation (symbolic, non-symbolic, or hybrid), and knowledge manipulation (functions that manipulate the conceptual space). Lastly, verification aligns with evaluation (via simulation, real-world platforms, or benchmarks). Although these works do not explicitly cover LLVMs and related algorithms, they demonstrate the value of integrating CC literature in ML, and can serve as useful starting points for ML approaches towards creative problem solving in LLVMs.

B.0.3 Creative Systems Framework

In Wiggins ( 2006 ) , the author expands on Boden’s levels further in the context of a framework that formalizes creative systems. The paper defines: a) creative system, b) creative behavior, c) novelty, and d) value. The paper also discusses formalized notion of a universe of possibilities , and conceptual spaces . Crucially, the work describes the characteristics of a creative agent, that can help distinguish modes of failures within a creative system, namely: a) hopeless uninspiration – where there are no valued concepts within the universe; b) conceptual uninspiration – where there are no valued concepts within the conceptual space of the agent; and c) generative uninspiration – where an agent is unable to find a valued concept owing to the specific method (e.g., search) employed.

Applicability to CPS in LLVMs: While the discussion of novelty, value and conceptual spaces in Wiggins ( 2006 ) aligns with our descriptions in Section 4 , the different modes of uninspiration offers potential ways to assess failure modes in LLVMs. This allows agents to distinguish between systems where creative problem solving is not possible (hopeless uninspiration), as compared to systems where the conceptual space or the methodology for searching the conceptual space, may be at fault (conceptual or generative uninspiration). Although this approach has not been expanded in existing literature, it presents a promising direction for an evaluation framework that can distinguish CPS from non-CPS problems.

B.1 A potential link between creative problem solving and general intelligence

Existing literature hints at a potential link between creative problem solving and Artificial General Intelligence (AGI) - systems that are broadly capable of solving almost all tasks that humans can Shevlin et al. ( 2019 ) . For instance, in Moruzzi ( 2020 ) , p.85., the author argues that there exists a strong correlation between creativity and AGI: “ … features that systems need to develop in order to achieve general intelligence are aspects that they need to possess also to earn the attribute creative ”. In Goertzel ( 2014 ) , the author compiles a list of competencies deemed essential for achieving AGI, including creative capacities like “ conceptual invention ” and “ creative constructive play with objects ”. The processes of “insight” or “incubation” often associated with creative problem solving Hélie and Sun ( 2010 ); Gilhooly ( 2016 ) is also considered important for AGI Ventura ( 2014 ) . Taken together, it is likely that any promising vision of AGI would be incomplete without creative problem solving .

Alongside the heavy ongoing discussion of AGI surrounding LLVMs Bubeck et al. ( 2023 ); Fei et al. ( 2022 ); Ma et al. ( 2023 ); Xi et al. ( 2023 ); Moor et al. ( 2023 ); Grudin and Jacques ( 2019 ) , there is often little to no discussion of creative problem solving or Computational Creativity within mainstream ML. As described in Moruzzi ( 2020 ) , p.96, “ The investigation on the nature of creativity and on how it manifests itself not only in human but also in animal and artificial systems should, thus, not be intended as a niche discussion but, rather, as a fundamental research which can lay the foundations for further studies in artificial intelligence and its relation to humans ”. We hope that this work will encourage discussions of creative problem solving and Computational Creativity alongside discussions on AGI.

Appendix C Experiment Settings

Prompt type

Prompt

Regular

“can this object be used as a scoop?” “can this object be used as a hammer?” “can this object be used as a spatula?” “can this object be used as a toothpick?” “can this object be used as pliers?” “scoops must be concave and hollow. can this object be used as a scoop?” “hammers must be heavy and have a handle attached to a cylinder at the end. can this object be used as a hammer?” “spatulas must have a handle attached to a flat surface at the end. can this object be used as a spatula?” “toothpicks must have a pointed tip. can this object be used as a toothpick?” “pliers must have two-prongs. can this object be used as pliers?” “scoops can transfer beans from one jar to another jar. can this object be used as a scoop?” “hammers can hit a nail into the wall. can this object be used as a hammer?” “spatulas can spread butter onto a pan. can this object be used as a spatula?” “toothpicks can pick food caught between the teeth. can this object be used as a toothpick?” “pliers can grab a coin. can this object be used as pliers?” “scoops can transfer beans from one jar to another jar. scoops are concave and hollow. can this object be used as a scoop?” “hammers can hit a nail into the wall. hammers have a handle attached to a cylinder at the end. can this object be used as a hammer?” “spatulas can spread butter onto a pan. spatulas have a handle attached to a flat surface at the end. can this object be used as a spatula?” “toothpicks can pick food caught between the teeth. toothpicks have a pointed tip. can this object be used as a toothpick?” “pliers can grab a coin. pliers have two-prongs. can this object be used as pliers?” C.1 Data: Test images Figure 3 shows the test set of 16 RGB images of objects used for the object substitution task. From the shown image dataset, we create test sets with 4 objects each, chosen from the set of 16 object images. We create 10 such test sets per core object (total 50 samples per model). Each test set only includes one ground truth object, along with three other random objects that will not suit as an appropriate replacement. In the nominal group, the ground truth is the actual object itself. In the object replacement group, the replacements are chosen based on self-assessment of the authors as (core object → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW replacement): “Scoop” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Bowl”; “Hammer” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Saucepan”; “Spatula” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Knife”; “Toothpick” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Safety pin”; “Pliers” → absent → \xrightarrow{} start_ARROW start_OVERACCENT end_OVERACCENT → end_ARROW “Scissors”. C.2 Model: Checkpoints For all the models, we use pre-trained HuggingFace checkpoints, with no additional training or fine-tuning. The models are of different architecture sizes and patch sizes: “CLIP-B-32” uses the “openai/clip-vit-base-patch32” which is a base model with a patch size of 32. “CLIP-B-16” uses “openai/clip-vit-base-patch16” – a base model with patch size of 16. “CLIP-L-14” uses “openai/clip-vit-large-patch14” – a large model with patch size of 14. “CLIP-H-14” uses “laion/CLIP-ViT-H-14-laion2B-s32B-b79K” which is a “huge” model, with a patch size of 14. This model is trained with the 2 billion sample English subset of LAION-5B. For LLaVA, we use the “llava-hf/llava-1.5-7b-hf” with 7B parameters, version 1.5. Lastly, “VILT-B-32” uses “dandelin/vilt-b32-finetuned-vqa” trained for visual question answering. However, there is limited data available on HuggingFace regarding the model. C.3 Prompts used in testing In this section, we discuss the prompts used in the different testing conditions (see Table 2 ). We explore four classes of prompts for the creative object substitution task: “Regular”, “Affordance”, “Task” and “Task and affordance”. Regular prompts involve a direct prompt as to whether a given object will suffice as a substitute for the missing object. Affordance prompts, adds information about the desired affordances that are essential for replacing the missing object. Task prompts adds additional information on the task to be performed as context for whether a given object can be used as replacement for the missing object. Lastly, task and affordance prompts combine the task and object affordance information within the prompt. C.4 Testing Procedure For each test case, we pass the images in the test set along with a prompt belonging to one of the four classes described in Table 2 . We record whether the ground truth object image was chosen by the model for the prompt (i.e., assigned highest output probability). CLIP generates probabilities that given images correspond to a text. ViLT responds with a text, and we evaluate if the model responded “yes” with a high probability for the ground truth. C.5 Testing Infrastructure We used NVIDIA-A100 GPUs to run the evaluation. However, the models are not too large and we have tested and confirmed that the code can be executed on CPU only as well. Appendix D Continued Experiment Results In this section, we show the class-wise breakdown of the different models for the different prompting strategies (Figures 4 - 7 ). We note that “hammers” present a particularly challenging case for all the models, perhaps due to the fact that correlating affordance of a hammer to a saucepan textually is difficult. In contrast, all models with the augmented prompts typically perform well in the case of creatively replacing “toothpick” with “safety pin” – presumably indicating that specifying the relevant affordance textually in this case provides sufficient information. We repeated each experiment across multiple random seeds and found similar performances, showing that our general findings hold across different random cases. Generally, specifying object affordance information in the prompts leads to improved model performance. Business Essentials Leadership & Management Credential of Leadership, Impact, and Management in Business (CLIMB) Entrepreneurship & Innovation Digital Transformation Finance & Accounting Business in Society For Organizations Support Portal Media Coverage Founding Donors Leadership Team Harvard Business School → HBS Online → Business Insights → Business Insights Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills. Career Development Communication Decision-Making Earning Your MBA Negotiation News & Events Productivity Staff Spotlight Student Profiles Work-Life Balance AI Essentials for Business Alternative Investments Business Analytics Business Strategy Business and Climate Change Creating Brand Value Design Thinking and Innovation Digital Marketing Strategy Disruptive Strategy Economics for Managers Entrepreneurship Essentials Financial Accounting Global Business Launching Tech Ventures Leadership Principles Leadership, Ethics, and Corporate Accountability Leading Change and Organizational Renewal Leading with Finance Management Essentials Negotiation Mastery Organizational Leadership Power and Influence for Positive Impact Strategy Execution Sustainable Business Strategy Sustainable Investing Winning with Digital Platforms What Is Creative Problem-Solving & Why Is It Important? 01 Feb 2022 One of the biggest hindrances to innovation is complacency—it can be more comfortable to do what you know than venture into the unknown. Business leaders can overcome this barrier by mobilizing creative team members and providing space to innovate. There are several tools you can use to encourage creativity in the workplace. Creative problem-solving is one of them, which facilitates the development of innovative solutions to difficult problems. Here’s an overview of creative problem-solving and why it’s important in business. Access your free e-book today. What Is Creative Problem-Solving? Research is necessary when solving a problem. But there are situations where a problem’s specific cause is difficult to pinpoint. This can occur when there’s not enough time to narrow down the problem’s source or there are differing opinions about its root cause. In such cases, you can use creative problem-solving , which allows you to explore potential solutions regardless of whether a problem has been defined. Creative problem-solving is less structured than other innovation processes and encourages exploring open-ended solutions. It also focuses on developing new perspectives and fostering creativity in the workplace . Its benefits include: Finding creative solutions to complex problems : User research can insufficiently illustrate a situation’s complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it. Adapting to change : Business is constantly changing, and business leaders need to adapt. Creative problem-solving helps overcome unforeseen challenges and find solutions to unconventional problems. Fueling innovation and growth : In addition to solutions, creative problem-solving can spark innovative ideas that drive company growth. These ideas can lead to new product lines, services, or a modified operations structure that improves efficiency. Creative problem-solving is traditionally based on the following key principles : 1. Balance Divergent and Convergent Thinking Creative problem-solving uses two primary tools to find solutions: divergence and convergence. Divergence generates ideas in response to a problem, while convergence narrows them down to a shortlist. It balances these two practices and turns ideas into concrete solutions. 2. Reframe Problems as Questions By framing problems as questions, you shift from focusing on obstacles to solutions. This provides the freedom to brainstorm potential ideas. 3. Defer Judgment of Ideas When brainstorming, it can be natural to reject or accept ideas right away. Yet, immediate judgments interfere with the idea generation process. Even ideas that seem implausible can turn into outstanding innovations upon further exploration and development. 4. Focus on "Yes, And" Instead of "No, But" Using negative words like "no" discourages creative thinking. Instead, use positive language to build and maintain an environment that fosters the development of creative and innovative ideas. Creative Problem-Solving and Design Thinking Whereas creative problem-solving facilitates developing innovative ideas through a less structured workflow, design thinking takes a far more organized approach. Design thinking is a human-centered, solutions-based process that fosters the ideation and development of solutions. In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase framework to explain design thinking. The four stages are: Clarify: The clarification stage allows you to empathize with the user and identify problems. Observations and insights are informed by thorough research. Findings are then reframed as problem statements or questions. Ideate: Ideation is the process of coming up with innovative ideas. The divergence of ideas involved with creative problem-solving is a major focus. Develop: In the development stage, ideas evolve into experiments and tests. Ideas converge and are explored through prototyping and open critique. Implement: Implementation involves continuing to test and experiment to refine the solution and encourage its adoption. Creative problem-solving primarily operates in the ideate phase of design thinking but can be applied to others. This is because design thinking is an iterative process that moves between the stages as ideas are generated and pursued. This is normal and encouraged, as innovation requires exploring multiple ideas. Creative Problem-Solving Tools While there are many useful tools in the creative problem-solving process, here are three you should know: Creating a Problem Story One way to innovate is by creating a story about a problem to understand how it affects users and what solutions best fit their needs. Here are the steps you need to take to use this tool properly. 1. Identify a UDP Create a problem story to identify the undesired phenomena (UDP). For example, consider a company that produces printers that overheat. In this case, the UDP is "our printers overheat." 2. Move Forward in Time To move forward in time, ask: “Why is this a problem?” For example, minor damage could be one result of the machines overheating. In more extreme cases, printers may catch fire. Don't be afraid to create multiple problem stories if you think of more than one UDP. 3. Move Backward in Time To move backward in time, ask: “What caused this UDP?” If you can't identify the root problem, think about what typically causes the UDP to occur. For the overheating printers, overuse could be a cause. Following the three-step framework above helps illustrate a clear problem story: The printer is overused. The printer overheats. The printer breaks down. You can extend the problem story in either direction if you think of additional cause-and-effect relationships. 4. Break the Chains By this point, you’ll have multiple UDP storylines. Take two that are similar and focus on breaking the chains connecting them. This can be accomplished through inversion or neutralization. Inversion: Inversion changes the relationship between two UDPs so the cause is the same but the effect is the opposite. For example, if the UDP is "the more X happens, the more likely Y is to happen," inversion changes the equation to "the more X happens, the less likely Y is to happen." Using the printer example, inversion would consider: "What if the more a printer is used, the less likely it’s going to overheat?" Innovation requires an open mind. Just because a solution initially seems unlikely doesn't mean it can't be pursued further or spark additional ideas. Neutralization: Neutralization completely eliminates the cause-and-effect relationship between X and Y. This changes the above equation to "the more or less X happens has no effect on Y." In the case of the printers, neutralization would rephrase the relationship to "the more or less a printer is used has no effect on whether it overheats." Even if creating a problem story doesn't provide a solution, it can offer useful context to users’ problems and additional ideas to be explored. Given that divergence is one of the fundamental practices of creative problem-solving, it’s a good idea to incorporate it into each tool you use. Brainstorming Brainstorming is a tool that can be highly effective when guided by the iterative qualities of the design thinking process. It involves openly discussing and debating ideas and topics in a group setting. This facilitates idea generation and exploration as different team members consider the same concept from multiple perspectives. Hosting brainstorming sessions can result in problems, such as groupthink or social loafing. To combat this, leverage a three-step brainstorming method involving divergence and convergence : Have each group member come up with as many ideas as possible and write them down to ensure the brainstorming session is productive. Continue the divergence of ideas by collectively sharing and exploring each idea as a group. The goal is to create a setting where new ideas are inspired by open discussion. Begin the convergence of ideas by narrowing them down to a few explorable options. There’s no "right number of ideas." Don't be afraid to consider exploring all of them, as long as you have the resources to do so. Alternate Worlds The alternate worlds tool is an empathetic approach to creative problem-solving. It encourages you to consider how someone in another world would approach your situation. For example, if you’re concerned that the printers you produce overheat and catch fire, consider how a different industry would approach the problem. How would an automotive expert solve it? How would a firefighter? Be creative as you consider and research alternate worlds. The purpose is not to nail down a solution right away but to continue the ideation process through diverging and exploring ideas. Continue Developing Your Skills Whether you’re an entrepreneur, marketer, or business leader, learning the ropes of design thinking can be an effective way to build your skills and foster creativity and innovation in any setting. If you're ready to develop your design thinking and creative problem-solving skills, explore Design Thinking and Innovation , one of our online entrepreneurship and innovation courses. If you aren't sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals. About the Author Crash of a Douglas DC-8-62 in Moscow: 61 killed Get IGI Global News All Products Book Chapters Journal Articles Video Lessons Teaching Cases Handbook of Research on Creative Problem-Solving Skill Development in Higher Education Printed-On-Demand (POD) Usually ships one day from order Multi-user license (no added fee) Immediate access after purchase PDF download Receive a 10% Discount on eBooks Purchase individual chapters from this book Immediate PDF download after purchase or access through your personal library Developing students’ creative problem-solving skills is paramount to today’s teachers, due to the exponentially growing demand for cognitive plasticity and critical thinking in the workforce. In today’s knowledge economy, workers must be able to participate in creative dialogue and complex problem-solving. This has prompted institutions of higher education to implement new pedagogical methods such as problem-based and case-based education. The Handbook of Research on Creative Problem-Solving Skill Development in Higher Education is an essential, comprehensive collection of the newest research in higher education, creativity, problem solving, and pedagogical design. It provides the framework for further research opportunities in these dynamic, necessary fields. Featuring work regarding problem-oriented curriculum and its applications and challenges, this book is essential for policy makers, teachers, researchers, administrators, students of education. The many academic areas covered in this publication include, but are not limited to: Competency-Based Training Creativity Techniques Critical Thinking Curriculum Design Instruction Methods Leadership of Students Learning Style Peer Learning Self-directed Learning Student-Centered Learning Scholars of engineering, business, art, medicine, and other fields explore methods and technologies for teaching creative problem solving in higher education. They discuss the context of higher education and the significance, difficulties, and strategies in developing students' creative problem-solving skills. The topics include thinking inside the box: educating leaders to manage constraints, distributed problem solving: how artists' participatory strategies can inspire creativity in higher education, students' learning experience in project-based learning, and a new paradigm of creativity: from Newtonian mechanics to quantum mechanics and higher education. Nominations Disciplines Award Winners (Russia, Russian Soviet Federative Socialist Republic, Soviet Union, Moscow Oblast, Europe) Lev Yashin Check all the awards nominated and won by Lev Yashin. Recent Awards The 19th annual panasonic gobel awards : winners announced, shoemaker won wsfa small press award, mcdonald won gaylactic spectrum awards, silvia moreno-garcia and leah bobet won copper cylinder award, winners of the british fantasy awards 2016 were announced, famous awards, youtube videos, award groups. Back to top 290 IMAGES Handbook of Research on Creative Problem-Solving Skill Development in Creative Problem Solving Teaching Strategies What Is Creative Problem-Solving and How to Master It with These 8 Creative Problem Solving Toolkit Creative Problem Solving Creative Problem Solving: An Introduction, Fourth Edition by Donald J COMMENTS PDF Creative Problem Solving CPS is a comprehensive system built on our own natural thinking processes that deliberately ignites creative thinking and produces innovative solutions. Through alternating phases of divergent and convergent thinking, CPS provides a process for managing thinking and action, while avoiding premature or inappropriate judgment. It is built upon a ... Full article: Assessing creative thinking skills in higher education Assessment of creative thinking in higher education is hampered by the lack of good measures of creativity (Baer Citation 2016; ... Creative problem-solving is a distinct class of problem-solving characterised by novelty, unconventionality, and persistence. These broad themes link to the formal definitions of creative thinking, including ... Connecting for Creativity in Higher Education Educators and researchers agree that creativity in higher education is relevant (Jahnke & Liebscher, 2020).It is the basis of discovery (Tanggaard, 2018), considered a key skill for twenty-first century learning (Egan, et al., 2017) and is drawn on in times of stress, for example during the current COVID pandemic (Mercier, et al., 2021).It would be reasonable to argue, as Tosey foretold, that ... Creativity-Fostering Teacher Behaviors in Higher Education: A For example, teachers let students experience prolonged ambiguous states of not-knowing, through open-ended, ill-defined tasks that lack information, direction, and readily available solutions—in other words, tasks in which creative problem-solving approaches need to be created, rather than recreated from what has been done, tested, and feels ... Enhancing creative cognition through project-based learning: An in The impact of the outcome optimization stage on students' creative problem-solving and critical thinking skills is profound. Chen ... Additionally, Voronov and Gerashchenko [67]highlight that timely guidance enhances problem-solving abilities in higher education, supporting the notion that educators play a crucial role in guiding students ... Promoting perceived creativity and innovative behavior: Benefits of A game mechanism was recommended to be used to enable students to experience in-depth creative problem-solving processes. Creativity was also discussed in the context of engineering education (Nazzal & Kaufman, 2020). University students solved an engineering-related problem including four stages of the creative problem-solving process. PDF Building Passion and Potential for Creative Learning in Higher Education The first step toward a more deliberate development of creative learning and teaching practices in higher education is to make explicit three aspects of creative growth. First, the recognition of. 1. the urgent need for creativity and problem solving is necessary. To grasp this fully, a basic understanding of the dimensions of creativity is ... Teaching Creatively in Higher Education: The Roles of Personal In terms of the perceptions of creative teaching, our findings are, hence, in line with the findings by Tanggaard (Citation 2011), who in the Danish secondary school context, reported that teachers perceive creative teaching as a problem-solving approach and a willingness to experiment whenever appropriate. Tanggaard further adds that, in such ... (PDF) Creative Problem Solving Process Instructional Design in the The Creative Problem Solving (CPS) process is a conceptual model that focuses on using higher-order thinking skills in order to overcome authentic problems during learning. Handbook of research on creative problem-solving skill development in The Handbook of Research on Creative Problem-Solving Skill Development in Higher Education is an essential, comprehensive collection of the newest research in higher education, creativity, problem solving, and pedagogical design. It provides the framework for further research opportunities in these dynamic, necessary fields. Teach creativity in science higher education In 2023, creativity became the second-most in-demand skill, after analytical thinking, across industries (1), reflecting the increasingly complex challenges faced by organizations and individuals. The teaching of creativity has been integrated into higher education in the arts (2), business (3), and engineering (4). Creativity should also be taught in basic science programs. Creative Thinking: Innovative Solutions to Complex Challenges All participants will earn a Certificate of Completion from the Harvard Division of Continuing Education. Benefits of Creative Thinking Skills Training. The goal of this creative thinking program is to help you develop the strategic concepts and tactical skills to lead creative problem solving for your team and your organization. You will learn to: Assessing creative thinking skills in higher education: deficits and Each facet in this core links to four domains that assess students creative thinking. These ' four domains: written expression, visual expression, scienti c problem solving, and social problem. fi. solving, assess the primary learning outcomes of the assessment task students completed: students. [PDF] Fostering Creative Problem Solvers in Higher Education: A This chapter contributes to bridge the complexity of societies, creative problem solving skills, and higher education development in one theoretical framework. Recent studies have emphasized issues of social emergence based on thinking of societies as complex systems. The complexity of professional practice has been recognized as the root of challenges for higher education. To foster creative ... Creativity in Higher Education: Challenges and Facilitating Factors creativity in higher education has been a challenge for faculty. Although there is agreement that college. students should be creative, college faculty are generally not familiar with learning and ... Teaching Creativity and Inventive Problem Solving in Science Abstract. Engaging learners in the excitement of science, helping them discover the value of evidence-based reasoning and higher-order cognitive skills, and teaching them to become creative problem solvers have long been goals of science education reformers. But the means to achieve these goals, especially methods to promote creative thinking ... Development of Problem Solving Skills with Maple in Higher Education Zhou, C.: Handbook of Research on Creative Problem-Solving Skill Development in Higher Education. IGI Global (2016) Google Scholar Hämäläinen, R., De Wever, B., Nissinen, K., Cincinnato, S.: What makes the difference - PIAAC as a resource for understanding the problem-solving skills of Europe's higher-education adults. Comput. Creative Problem Solving in Large Language and Vision Models While recent state-of-the-art large language models (LLMs) and vision-language models (VLMs) have demonstrated competency in artistic endeavours Rombach et al. (); Copet et al. (), creative problem solving continues to be a shortcoming of these models (we use LLVM to denote the umbrella of both LLMs and VLMs). For instance, in Bubeck et al. (), the authors point out that "discontinuous tasks ... What Is Creative Problem-Solving & Why Is It Important? Its benefits include: Finding creative solutions to complex problems: User research can insufficiently illustrate a situation's complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it. Adapting to change: Business is constantly changing, and business leaders need to adapt. Crash of a Douglas DC-8-62 in Moscow: 61 killed 61. Circumstances: A McDonnell Douglas DC-8-62 passenger plane, JA8040, was destroyed when it crashed on takeoff from Moscow's -Sheremetyevo Airport (SVO), Russia. Five of the 14 crew members and ten of the 62 passengers survived the accident. JAL flight JL446 was a scheduled international flight from Copenhagen (CPH), Denmark to Tokyo-Haneda ... Handbook of Research on Creative Problem-Solving Skill Development in The Handbook of Research on Creative Problem-Solving Skill Development in Higher Education is an essential, comprehensive collection of the newest research in higher education, creativity, problem solving, and pedagogical design. It provides the framework for further research opportunities in these dynamic, necessary fields. (Cyber) GRU (VIII): Structure. Unit 74455 Unit 74455. (Cyber) GRU (VIII): Structure. Unit 74455. April 02, 2019. Apparently, Unit 74455 is linked to operations of disinformation, influence, propaganda … which would reconfirm the broad concept of information warfare of the Russian military doctrine. We have already referred to it repeatedly, and to the mixture of the purely technical ... Lev Yashin He is also a member of the World Team of the 20th Century. 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