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14-10-2022 | Original Paper

Are Two Heads Better Than One?: The Effect of Student-AI Collaboration on Students' Learning Task Performance

Authors: Jinhee Kim, Sang-Soog Lee

Published in: TechTrends | Issue 2/2023

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Abstract

A growing number of educators expect that artificial intelligence (AI) will augment students' capacities and rapidly transform the teaching and learning practice. However, there is a lack of convincing evidence on the effects of Student-AI Collaboration (SAC) on a learning task's performance. A critical examination of the effects on students' learning performance is a crucial step in understanding the potential benefits of SAC on learning. Through a repeated measure experiment participated by 20 undergraduate students in South Korea, this study examined the effects of SAC on a public advertisement drawing task. The findings revealed that SAC significantly affects creativity in content, expressivity in expression, and public utility in effectiveness varied depending on students' attitude toward AI or on the level of drawing skill. Implications for the design of educational AI and AI literacy education are discussed.

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Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. https://doi.org/10.1016/j.chb.2015.11.036CrossRef

Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the protégé effect: increasing the effort towards learning. Journal of Science Education and Technology, 18(4), 334–352. https://doi.org/10.1007/s10956-009-9180-4CrossRef

Chen, H., Park, H. W., Zhang, X., & Breazeal, C. (2020, March). Impact of interaction context on the student affect-learning relationship in child-robot interaction. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 389–397).

Cheng, T. (2019). Product recommendation system design. In Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (pp. 71–74).

Choi, J. M. (2015). Effects of users’ domain knowledge on user experience: Focused on the UI design of mobile applications. Journal Korea Society of Visual Design Forum, 49, 253–262.

Coffey, A. J., & Atkinson, P. (1996). Making sense of qualitative data: Complementary research strategies (and social thought). SAGE Publications Inc.

Cruz-Benito, J., Sánchez-Prieto, J. C., Therón, R., & García-Peñalvo, F. J. (2019). Measuring students’ acceptance to AI-driven assessment in eLearning: Proposing a first TAM-based research model. In International conference on human-computer interaction (pp. 15–25).

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008CrossRef

Desmarais, M. C., & Baker, R. S. J. D. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1), 9–38. https://doi.org/10.1007/s11257-011-9106-8CrossRef

Eisner, E. W. (2002). The arts and the creation of mind. Yale University Press.

Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundations, methods, and models. Instructional-Design Theories and Models: A New Paradigm of Instructional Theory, 2, 115–140.

Hogan, K. E., & Pressley, M. E. (1997). Scaffolding student learning: Instructional approaches and issues. Brookline Books.

Kanda, T., Hirano, T., Eaton, D., & Ishiguro, H. (2004). Interactive robots as social partners and peer tutors for children: A field trial. Human-Computer Interaction, 19(1), 61–84. https://doi.org/10.1080/07370024.2004.9667340CrossRef

Kaptelinin, V. (1996). Activity theory: Implications for human-computer interaction. In B. A. Nardi (Ed.), Context and consciousness: Activity theory and human-computer interaction (pp. 64–103). The MIT Press.

Kim, S., Eun, J., Oh, C., Suh, B., & Lee, J. (2020, April). Bot in the bunch: Facilitating group chat discussion by improving efficiency and participation with a chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13).

Klahr, D., & Simon, H. A. (1999). Studies of scientific discovery: Complementary approaches and convergent findings. Psychological Bulletin, 125(5), 524–543. https://doi.org/10.1037/0033-2909.125.5.524CrossRef

Ley, T., Kump, B., & Gerdenitsch, C. (2010). Scaffolding self-directed learning with personalized learning goal recommendations. In International Conference on User Modeling, Adaptation, and Personalization (pp. 75–86).

Lim, C., & Han, H. (2020). Development of instructional design strategies for integrating an online support system for creative problem solving into a University course. Asia Pacific Education Review, 21(4), 539–552. https://doi.org/10.1007/s12564-020-09638-wCrossRef

Lin, Y., Guo, J., Chen, Y., Yao, C., & Ying, F. (2020). It Is your turn: Collaborative ideation with a co-creative robot through sketch. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). NY: Association for Computing Machinery.

Liu, S. (2019). Everybody’s Song Making. Performance Research, 24(1), 120–128. https://doi.org/10.1080/13528165.2019.1594267CrossRef

Luckin, R., Holmes, W., Griffiths, M., Corcier, L. B., UCL Knowledge Lab, Pearson (Firm), & University College, L. (2016). Intelligence Unleashed. Pearson.

Marijan, D., Shang, W., & Shukla, R. (2019). Implications of resurgence in artificial intelligence for research collaborations in software engineering. ACM SIGSOFT Software Engineering Notes, 44(3), 68–70. https://doi.org/10.1145/3356773.3356813CrossRef

McNiff, J., & Whitehead, J. (2011). All you need to know about action research. Sage Publications.

Miles, M. B., Huberman, A. M., & Saldaña, J. (2020). Qualitative data analysis: A methods sourcebook. Sage Publication.

Newell, A., & Simon, H. A. (1972). Human problem solving. Prentice-Hall.

Oh, C., Song, J., Choi, J., Kim, S., Lee, S., & Suh, B. (2018). I lead, you help but only with enough details: Understanding user experience of co-creation with artificial intelligence. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–13).

Parekh, R. (2017). Designing AI at scale to power everyday life. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 27–27).

Pavlou, P. A., & Stewart, D. W. (2000). Measuring the Effects and Effectiveness of Interactive Advertising. Journal of Interactive Advertising, 1(1), 61–77. https://doi.org/10.1080/15252019.2000.10722044CrossRef

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 1–14. https://doi.org/10.1186/s40561-019-0089-yCrossRef

Pifarré, M., & Argelagós, E. (2020). Embedded information problem-solving instruction to foster learning from digital sources: Longitudinal effects on task performance. Sustainability, 12(19), 7919. https://doi.org/10.3390/su12197919CrossRef

Plano Clark, V. L., Schumacher, K., West, C., Edrington, J., Dunn, L. B., Harzstark, A., Melisko, M., Rabow, M. W., Swift, P. S., & Miaskowski, C. (2013). Practices for embedding an interpretive qualitative approach within a randomized clinical trial. Journal of Mixed Methods Research, 7(3), 219–242. https://doi.org/10.1177/1558689812474372CrossRef

Popovic, V. (2003). Expert and novice users model and their application to the design process. Journal of the Asian Design International Conference, 1, 1–12.

Rourke, L., & Anderson, T. (2004). Validity in quantitative content analysis. Educational Technology Research and Development, 52(1), 5–18.CrossRef

Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2019). How to measure teachers' acceptance of AI-driven assessment in eLearning: A TAM-based proposal. In Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 181–186).

Scardamalia, M., & Bereiter, C. (2014). Cambridge handbook of the learning sciences. In R.K. Sawyer (2nd Ed.), Knowledge building and knowledge creation: Theory, pedagogy, and technology (pp. 397–417). Cambridge University Press.

Stokes, P. D. (2005). Creativity from constraints: The psychology of breakthrough. Springer.

Stuhlfaut, M. W., & Yoo, C. Y. (2013). A tool for evaluating advertising concepts: Desirable characteristics as viewed by creative practitioners. Journal of Marketing Communications, 19(2), 81–97. https://doi.org/10.1080/13527266.2010.550009CrossRef

Susar, D., & Aquaro, V. (2019). Artificial intelligence: Opportunities and challenges for the public sector. Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance (pp. 418–426).

Torrance, E. P., Torrance, J. P. Willams, S. J., Horng, R.-Y., & Crable, A. B. (1978). Handbook for training future problem solving teams. Lincoln, Nebraska: Future Problem Solving Bowl.

Valkenburg, R., & Dorst, K. (1998). The reflective practice of design teams. Design Studies, 19(3), 249–271. https://doi.org/10.1016/s0142-694x(98)00011-8CrossRef

Valle-Cruz, D., & Sandoval-Almazan, R. (2018). Towards an understanding of artificial intelligence in government. In Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age (pp. 1–2).

VanLehn, K. (2011). The Relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369CrossRef

Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019). Human-AI collaboration in data science. In Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–24. https://doi.org/10.1145/3359313

Wang, C. W., & Horng, R. Y. (2002). The effects of creative problem solving training on creativity, cognitive type and R&D performance. R&D Management, 32(1), 35–45. https://doi.org/10.1111/1467-9310.00237CrossRef

West, D. C., Kover, A. J., & Caruana, A. (2008). Practitioner and customer views of advertising creativity: same concept, different meaning? Journal of Advertising, 37(4), 35–46. https://doi.org/10.2753/joa0091-3367370403CrossRef

Wu, Y., Mou, Y., Li, Z., & Xu, K. (2020). Investigating American and Chinese Subjects’ explicit and implicit perceptions of AI-Generated artistic work. Computers in Human Behavior, 104, 106186. https://doi.org/10.1016/j.chb.2019.106186CrossRef

Yang, Q., Steinfeld, A., & Zimmerman, J. (2019). Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–11).

Title: Are Two Heads Better Than One?: The Effect of Student-AI Collaboration on Students' Learning Task Performance
Authors: Jinhee Kim
Sang-Soog Lee
Publication date: 14-10-2022
Publisher: Springer US
Published in: TechTrends / Issue 2/2023
Print ISSN: 8756-3894
Electronic ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-022-00788-9

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Are Two Heads Better Than One?: The Effect of Student-AI Collaboration on Students' Learning Task Performance

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