Abstract
In this paper, we describe an AI-driven platform, ALLURE, with an embodied chatbot that teaches the user how to solve a Rubik’s Cube. Our AI algorithm consists of macro-actions, which refer to a set of moves that are not transparent or usable to users as a black box. Through the integration of explainable AI and conversational user interface designs, we created a novel AI-driven visual user interface inspired by scaffolding design paradigms to engage users. We conducted a set of initial usability testing with ten users, and usability findings imply some important AI-driven user interface designs to engage users for problem solving.
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References
Cheng, X., Sun, J., Zarifis, A.: Artificial intelligence and deep learning in educational technology research and practice. Br. J. Edu. Technol. 51(5), 1653–1656 (2020). https://doi.org/10.1111/bjet.13018
Beardsley, M., Santos, P., Hernández-Leo, D., Michos, K.: Ethics in educational technology research: Informing participants on data sharing risks. Br. J. Edu. Technol. 50(3), 1019–1034 (2019)
Agostinelli, F., et al.: Designing Children’s new learning partner: collaborative artificial intelligence for learning to solve the rubik’s cube. In: Interaction Design and Children, pp. 610–614. Athens, Greece (2021). https://doi.org/10.1145/3459990.3465175
Agostinelli, F., Panta, R., Khandelwal, V., Srivastava, B., Muppasani, B., Wu D.: Explainable Pathfinding for Inscrutable Planners with Inductive Logic Programming (2022)
Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Processes. Harvard University Press (1978)
Belland, B.R., Glazewski, K.D., Richardson, J.C.: A scaffolding framework to support the construction of evidence-based arguments among middle school students. Educ. Tech. Res. Dev. 56(4), 401–422 (2008)
Wood, D., Bruner, J.S., Ross, G.: The role of tutoring in problem solving. J. Child Psychol. Psychiatry, 17(2), 89–100 (1976). https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Lange, C., Gorbunova, A., Shmeleva, E., Costley, J.: The relationship between instructional scaffolding strategies and maintained situational interest. Interactive Learn. Environ. 1–12 (2022)
González-Calero, J.A., Arnau, D., Puig, L., Arevalillo-Herráez, M.: Intensive scaffolding in an intelligent tutoring system for the learning of algebraic word problem solving. Br. J. Edu. Technol. 46(6), 1189–1200 (2015)
Hannafin, M., Land, S., Oliver, K.: Open learning environments: foundations, methods, and models. Instruct.-Des. Theor. Mod. New Paradigm Instruct. Theory 2, 115–140 (1999)
Kim, N.J., Vicentini, C.R., Belland, B.R.: Influence of scaffolding on information literacy and argumentation skills in virtual field trips and problem-based learning for scientific problem solving. Int. J. Sci. Math. Educ. 20(2), 215–236 (2022)
Kim, N.J., Belland, B.R., Walker, A.E.: Effectiveness of computer-based scaffolding in the context of problem-based learning for STEMeducation: Bayesian meta-analysis. Educ. Psychol. Rev. 30(2), 397–429 (2018)
Kim, N.J., Belland, B.R., Lefler, M., Andreasen, L., Walker, A., Axelrod, D.: Computer-based scaffolding targeting individual versus groups in problem-centered instruction for STEM education: meta-analysis. Educ. Psychol. Rev. 32(2), 415–461 (2020)
Cai, W., et al.: Bandit algorithms to personalize educational chatbots. Mach. Learn. 110(9), 2389–2418 (2021). https://doi.org/10.1007/s10994-021-05983-y
Gupta, & Chen, Y.: Supporting inclusive learning using chatbots? A chatbot-led interview study. J. Inf. Syst. Educ. 33(1), 98–108 (2022)
Yuan, C.C., Li, C.-H., Peng, C.-C.: (in press). Development of mobile interactive courses based on an artificial intelligence chatbot on the communication software LINE. Interactive Learn. Environ. 1–15 (2021)
Yin, J., Goh, T.-T., Yang, B., Xiaobin, Y.: Conversation technology with micro-learning: the impact of chatbot-based learning on students’ learning motivation and performance. J. Educ. Comput. Res. 59(1), 154–177 (2021)
Jain, M., et al.: FarmChat: A conversational agent to answer farmer queries. Assoc. Comput. Mach. 2(4), 1-22 (2019)
Borsci, S., et al.: The chatbot usability scale: the design and pilot of a usability scale for interaction with AI-Based conversational agents. Pers. Ubiquit. Comput. 26(1), 95–119 (2022)
Creswell, J.W., Plano Clark, V.L.: Designing and Conducting Mixed Methods Research (3rd ed.). Sage (2018)
Acknowledgement
The authors would like to acknowledge the generous funding support from ASPIRE II grant at the University of South Carolina (U of SC), and partial funding support provided by UofSC’s Grant No: 80002838.
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Wu, D. et al. (2022). AI-Driven User Interface Design for Solving a Rubik’s Cube: A Scaffolding Design Perspective. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction. HCII 2022. Lecture Notes in Computer Science, vol 13516. Springer, Cham. https://doi.org/10.1007/978-3-031-17615-9_34
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