The Potential of Peer Robots to Assist Human Creativity in Finding Problems and Problem Solving
by Sandra Okita - 2015
Many technological artifacts (e.g., humanoid robots, computer agents) consist of biologically inspired features of human-like appearance and behaviors that elicit a social response. As robots cross the boundaries between humans and machines, the features of human interactions can be replicated to reveal new insights into the role of social relationships in learning and creativity. Peer robots can be designed to create ideal circumstances that enable new ways for students to reflect, reason, and learn. This paper explores how peer-like robots and robotic systems may help students learn and engage in creative ways of thinking.
Recent advances in robotics technology have shifted the focus from supporting productivity (e.g., industrial robots) to supporting humans at a more personal level (e.g., companion robots). Many technological artifacts (e.g., humanoid robots, computer agents) display biologically inspired human-like features and behaviors that elicit a social response. The strong social component of technology seems to engage people in conversation to share information and ideas with artifacts. Robots can replicate features of human interactions and reveal new insights into the role of social relationships in learning and creativity. Robots are highly directable and can create ideal circumstances that enable new ways for students to reflect, reason, and learn. This, in turn, has increased expectations that robots and computer agents will enhance human learning and creativity by complementing peoples physical, social, and cognitive capabilities. This paper explores the potential robots may have for understanding the self and other, especially in creatively finding and solving problems. My goal is to identify the potentially useful traits of robots in designing activities, and conditions that may assist the creative thinking process of students in the classroom.
CREATIVITY AND THE ROLE OF INNOVATION
Creativity is widely reflected in literature and research, where the general debate centers on two poles, individuals and their environment (Larsson, 2002). Barron (1997) saw creativity as a way to decipher the mystery of the individual and the mystery of being, while Niels Bohr regarded creativity as something nurtured in and through others (Rothenberg, 1983).
Creative achievements are also explored in different ways. While some see creativity as the ability to break away from conventional norms (Gardner, 1993b), others see it as work built upon preceding discoveries and ideas (Weisberg, 1995). The many facets of creativity make it difficult to determine what exactly it is, but researchers seem to have some consensus that creativity is a product of the combined effects of many factors, including social, cultural, environmental, and situational (Csikszentmihalyi, 1988; Sternberg & Lubart, 1995). Gardner (1991) suggests, Creativity is best described as the human capacity regularly to solve problems or to fashion products in a domain, in a way that is initially novel but ultimately acceptable in a culture (p. 14). In terms of this paper, creativity is explored as a property of thinking, in the areas of problem finding (Baer, 1988; Cropley, 1992; Nickerson, 1999) and problem solving (Feldhusen & Treffinger, 1985 Runco, 1994).
Innovation is also interpreted broadly and pursued in many forms. Here, innovation is referred to as the careful selection and development of technological tools (e.g., humanoid robots, pedagogical computer agents) that bring together a well-chosen confluence of effective resources that may unfold potential knowledge and creativity in students. Robots can be used as a vehicle to examine how humans interpret, understand, and behave around technologies. The findings can then be applied to the development of technologies and activities that test theories about conditions for learning and creativity. Studies can help isolate and identify cause-and-effect relationships, enabling researchers more effectively to design relationships with technology that invite creative thinking and interaction. Optimizing learning and creativity with robots will require students to manage activities in new ways by going against individual intuitions, nurturing the self through others, and often deviating from traditional instructional norms.
PEER ROBOTS HELPING HUMANS CREATIVELY FIND AND SOLVE PROBLEMS
There are different features in robots that may be valuable for exploring creativity in the areas of problem finding and problem solving. The objective is not to make robots creative, but to examine how the unique capacity of robots may assist humans in the creative process of thinking.
CREATIVITY OF PROBLEM FINDING
Many researchers emphasize problem finding as an important aspect of creativity (Getzels & Csikszentmihalyi, 1975; Runco, 1994). However, problem finding has not been a major focus in schools (Houtz, 1994); students are typically given problems to solve, and very rarely are asked to search out problems for themselves. Studies have found that students who explore ways to define problems engage in more creative problem solving over time (Baer, 1988).
Robots have boundary-like properties that may be valuable for problem finding. Biologically inspired robots take on forms and functions similar to those of real humans and animals, but these forms and functions possess machine-like properties. Because social robots belong simultaneously to two categories that are mutually exclusive (e.g., human and machine), this boundary-like quality often elicits strong responses that bewilder peoples beliefs. McCloud (1993) found that artifacts bearing a simple resemblance to humans allowed people to understand their own internal shapes better, left room for more interpretation, and elicited greater empathy. MacDorman (2005) mentioned that confusion and curiosity arose not because of the robots human-like appearance or physical properties, but because of its response timing and character movements. The complex nature of these robots seems to evoke a deep desire for explanations, along with some skepticism about obvious explanations, which are both important traits for students to learn and search out problems (Bruner, 1962).
Studies have found that when children encounter the complex nature of these robots, they do some serious thinking when asked to make inferences about biological properties, intelligence, and agency (Okita & Schwartz, 2006). For example, children said robots needed a remote control to move, but when asked if the robots could be bad pets and jump on the couch when no one was looking, some hesitated before answering the question. When children inferred that robotic dogs could not grow because of their hard exterior, some wondered whether a robotic dog still needed food. Childrens answers were more inconsistent in some domains (i.e., biology and agency) than others (i.e., intelligence), and they brought a syncretic set of beliefs when slowly developing their understanding of robots in a piecemeal fashion. Rather than replacing one theory with another, they changed discrete beliefs based on a mixture of facts they acquired through observations and interactions. Curiosity also plays a large part in creativity (Csikszentmihalyi, 1996). Children interacting with life-like robots would ask questions such as, I am allergic to cats, but I am not so sure about robot cats, and Asimo, do you remember me from Disneyland? (Honda humanoid robots are displayed around the world). Nickerson (1999) suggests that children exposed to lots of creative thought-provoking products or situations in stimulating ways are more likely to find something that will genuinely interest them deeply.
CREATIVITY OF PROBLEM SOLVING
Feldhusen and Treffinger (1985) suggest that creativity and problem solving can be combined into a single complex concept. They argue, Creative abilities such as fluency, flexibility, and originality . . . are in reality indispensable components of realistic and complex problem solving behavior (p. 2). Runco (1990) suggests that creative thinkers need to find ways to utilize their strengths and mitigate work around their weaknesses. Discovering conditions that facilitate ones own creativity in problem solving and overcoming ones weaknesses can be a challenge without informative feedback. Through the reciprocal lens of learning-by-teaching (LBT) and recursive feedback (Okita & Schwartz, 2013), robots can be designed to create ideal circumstances that enable students to reflect, reason, and learn about their strengths and weaknesses.
Robots can make ideal peer learners, as they are usually less than perfect like their human peers, but robots can carefully craft discrepancies for the human learner to notice and reflect on (Okita, Ng-Thow-Hing, & Sarvadevabhatla, 2011). An LBT situation (Bargh & Schul, 1980) can involve students tutoring a robot or programming the robots behavior. There is an additional step in LBT that stresses the importance of watching ones pupil perform (in this case the robot taking the role of the pupil). Recursive feedback refers to the information that flows back to tutors when observing their pupils independent use of what they have been taught in a relevant performance context (Okita & Schwartz, 2013). This step allows students to map their understanding of what they observe in their pupil robot. Based on what they observe in their peer robot, any discrepancies can lead to the realization that the problem was not rooted in their tutoring method per se, but in their own misunderstanding of the concept. By carefully designing this feedback, robots can provide an informative assessment of ones own content knowledge. Similar effects are seen in a non-teaching situation (i.e., programming robots). In programming robots, students formalize the rules in a learning environment (e.g., programming interface) and then observe an external source (e.g., robot) interpret and carry out the command in its entirety. Students observe the robots output, and if it is unfavorable, students will backtrack to ferret out the algorithms that dictate the robots behaviors. In robot programming, there is a recursive loop where students map their understanding of what they observe in their robots behavior, and any discrepancies they notice maps back to any misunderstanding of the formalized rules when programming the robot (Okita, 2014a).
A critical ingredient in the creativity of problem solving is the ability to self-assess ones learning progress (Jausovec, 1994). This growing interest in metacognition has led researchers such as Runco (1990) to stress the link between self-monitoring and self-evaluative skills for creative thinking. The ability to think well requires both creative and critical thinking, where idea generation and evaluation need to occur simultaneously. Nickerson (1999) suggests, a creative thinking process is like an ongoing dialogue between two agents, one of which puts out ideas without restraint and the other of which evaluates those ideas (p. 399). Robots can be designed to play the different roles of agents to help students develop the metacognitive skills of self-monitoring and self-assessment.
Self-assessing is cognitively demanding because students confront the dual task of solving the problem and assessing their performance at the same time. Letting students monitor problem solving by robots may alleviate some of the pressure of these dual demands (Gelman & Meck, 1983), as students do not need to attend fully to solving the problem themselves. Developing self-monitoring and self-assessing skills on an external plane (e.g., robots, computer agents) can put children in a better position to internalize these skills. In a previous study, elementary school students practiced metacognitive strategies externally on robots and agents when solving mathematics problems. Practicing monitoring on robots and agents helped students develop awareness and proficiency in monitoring, and, over time, the students started to turn this external monitoring behavior inward (Okita, 2014b). Self-other monitoring using robots has turned out to be a powerful form of self-assessment in which students assess the knowledge of someone else and then implicitly assess their own knowledge.
Robots have the information-processing capacity to create and access an archive of data a student generates. This strength in technology can allow the computer system in robots to provide teachers and students with a precise replay of past learning experiences and performance, or have the robot use behavioral data to simulate the thoughts an individual might use to reason about a situation. Robots can also provide a safe environment for students to externalize their thought processes. Externalization can support creativity by making thoughts and intentions more accessible for personal reflections. For example, robots can take vague mental conceptualizations of an idea and produce more concrete representations using visualization tools.
IMPLICATIONS FOR STUDENTS AND TEACHERS IN THE CLASSROOM
In the classroom, robots may enhance learning and creativity in collaborative groups. Students often resort to familiar patterns in group collaborations and become less creative, preferring more familiar, efficient modes of thinking and minimal effort. Peer-like robots can be placed within each group to record and explore different problem-solving strategies (Miyake & Okita, 2012). Robots are ideal in this situation as they can craft desirable difficulties to trigger creative thinking, and identify useful situational variations for problem solving.
Robots and robotic systems have the potential to play an important role in Science, Technology, Engineering, and Mathematics (STEM) education. STEM education is a unique and integrative discipline that brings together basic science, mathematics, and applied engineering, which require creativity when linking ideas and concepts. Involving students early in the development and design of collaborative activities with robots can generate interest in the field and address concerns over decreasing enthusiasm toward STEM careers.
Baer, J. M. (1988). Long-term effects of creativity training with middle school students. The Journal of Early Adolescence, 8(2), 183193.
Bargh, J., & Schul, Y. (1980). On the cognitive benefits of teaching. Journal of Educational Psychology, 72, 593604.
Barron, F. X., Montuori, A., & Barron, A. (Eds.). (1997). Creators on creating: Awakening and cultivating the imaginative mind. New York, NY: Putnam.
Bruner, J. S. (1962). The conditions of creativity. In H. Gruber, G. Terrell, & M. Wertheimer (Eds.), Contemporary approaches to creative thinking (pp. 130). New York, NY: Atherton.
Cropley, A. J. (1992). More ways than one: Fostering creativity. Norwood, NJ: Ablex.
Csikszentmihalyi, M. (1988). Society, culture, and person: A systems view of creativity. In R. J. Sternberg (Ed.), The nature of creativity (pp. 325339). New York, NY: Cambridge University Press.
Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. New York, NY: Harper Collins.
Feldhusen, J. F., & Treffinger, D. J. (1985). Creative thinking and problem solving in gifted education. Dubuque, IA: Kendall Hunt Publishing Company.
Gardner, H. (1991). To open minds. New York, NY: Basic Books.
Gardner, H. (1993). Seven creators of the modern era. In J. Brockman (Ed.), Creativity (pp. 2847). New York, NY: Simon & Schuster.
Gelman, R., & Meck, E. (1983). Preschoolers counting: Principles before skill. Cognition, 13, 343359.
Getzels, J. W., & Csikszentmihalyi, M. (1975). From problem solving to problem finding. In I. A. Taylor & J. W. Getzels (Eds.), Perspectives in creativity (pp. 90116). Chicago, IL: Aldine.
Houtz, J. C. (1994). Creative problem solving in the classroom: Contributions of four psychological approaches. In M. A. Runco (Ed.), Problem finding, problem solving, and creativity (pp. 153173).
Norwood, NJ: Ablex.
Jausovec, N. (1994). Metacognition in creative problem solving. In M. A. Runco (Ed.), Problem finding, problem solving, and creativity (pp. 7795). Norwood, NJ: Ablex.
Larsson, L. (Ed.). (2002). Cultures of creativity: The centennial exhibition of the Nobel Prize (Rev. ed.). Canton, MA: Science History Publications.
MacDorman, K. F. (2005). Androids as an experimental apparatus: Why is there an uncanny valley and can we exploit it? In Cognitive Science 2005 Workshop: Toward Social Mechanisms of Android Science (pp. 106118).
McCloud, S. (1993). Understanding comics. Amherst, MA: Kitchen Sink Press.
Miyake, N., & Okita, S. Y. (2012). Robot facilitation as dynamic support for collaborative learning. In J. Aalst, K. Thompson, M. J. Jacobson, & P. Reimann (Eds.), Proceedings of the 10th International Conference of the Learning Sciences (ICLS). (Vol. 2, pp. 5763), Sydney, Australia: University of Sydney: The International Conference of the Learning Sciences.
Nickerson, R. S. (1999). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392430). Cambridge, England: Cambridge University Press.
Okita, S. Y. (2014a). The relative merits of transparency: Investigating situations that support the use of robotics in developing student learning adaptability across virtual and physical computing platforms. British Journal of Educational Technology, 45(5), 844862.
Okita, S. Y. (2014b). Learning from the folly of others: Learning to self-correct by monitoring the reasoning of virtual characters in a computer-supported mathematics learning environment. Computers and Education, 71, 257278.
Okita, S. Y., & Schwartz, D. L. (2006). Young childrens understanding of animacy and entertainment robots. International Journal of Humanoid Robotics (IJHR), World Scientific, 3(3), 393412.
Okita, S. Y., Ng-Thow-Hing, V., & Sarvadevabhatla, R. K. (2011). Multimodal approach to affective human-robot interaction design with children. ACM Transactions on Interactive Intelligent Systems (TiiS), 1(1), 129.
Okita, S. Y., & Schwartz, D. L. (2013). Learning by teaching human pupils and teachable agents: The importance of recursive feedback. Journal of the Learning Sciences, 22(3), 375412.
Rothenberg, A. (1983). Janusian process and scientific creativity: The case of Niels Bohr. Contemporary Psychoanalysis, 19(1), 100119.
Runco, M. A. (1990). Implicit theories and ideational creativity. In M. A. Runco & R. S. Albert (Eds.), Theories of creativity (pp. 234252). Newbury Park, CA: Sage Publications, Inc.
Runco, M. A. (Ed.). (1994). Problem finding, problem solving, and creativity. Norwood, NJ: Ablex.
Sternberg, R. J., & Lubart, T. I. (1995). Defying the crowd: Cultivating creativity in a culture of conformity. New York, NY: Free Press.
Weisberg, R. W. (1995). Case studies of creative thinking: Reproduction versus restructuring in the real world. In S. M. Smith, T. B. Ward, & R. A. Finke (Eds.), The creative cognition approach (pp. 5372). Cambridge, MA: MIT Press.