Abstract
As artificial intelligence (AI) plays a more prominent role in our everyday lives, it becomes increasingly important to introduce basic AI concepts to K-12 students. To help do this, we combined physical robots and an augmented reality (AR) software to help students learn some of the fundamental concepts of reinforcement learning (RL). We chose RL because it is conceptually easy to understand but has received the least attention in previous research on teaching AI to K-12 students. We designed a series of activities in which students can design their own robots and train them with RL to finish a variety of tasks. We tested our platform with a pilot study conducted with 14 high school students in a rural city. Students’ engagement and learning were assessed through a qualitative analysis of students’ behavior and discussions. The result showed that students were able to understand both high-level AI concepts and specific RL terms through our activities. Also, our approach of combining virtual platforms and physical robots engaged students and inspired their curiosity to self-explore more about RL.
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All the names are pseudonym names.
References
Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: a survey. IEEE Trans. Intell. Transp. Syst. 23(6), 4909–4926 (2022)
Zamfirache, I.A., Precup, R.E., Roman, R.C., Petriu, E.M.: Policy iteration reinforcement learning-based control using a grey wolf optimizer algorithm. Inf. Sci. 585, 162–175 (2022)
He, Z., Tran, K.-P., Thomassey, S., Zeng, X., Jie, X., Yi, C.: A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Comput. Ind. 125, 103373 (2021)
Aljanabi, M., Ghazi, M., Ali, A.H., Abed, S.A.: Chatgpt: open possibilities. Iraqi J. Comput. Sci. Math. 4(1), 62–64 (2023)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. A Bradford Book, Cambridge, MA, USA (2018)
Vartiainen, H., Tedre, M., Valtonen, T.: Learning machine learning with very young children: Who is teaching whom? Int. J. Child-Comput. Interact. 100182, 06 (2020)
Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., Subsoontorn, P.: Kids making AI: integrating machine learning, gamification, and social context in stem education. In: 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 1005–1010. IEEE
Dietz, G., King Chen, J., Beason, J., Tarrow, M., Hilliard, A., Shapiro, R.B.: Artonomous: introducing middle school students to reinforcement learning through virtual robotics. IDC ’22, pp. 430–441. Association for Computing Machinery, New York, NY, USA (2022)
Coppens, Y., Bargiacchi, E., Nowé, A.: Reinforcement learning 101 with a virtual reality game. In: Proceedings of the 1st International Workshop on Education in Artificial Intelligence K-12 (2019)
Zhang, Z., Willner-Giwerc, S., Sinapov, J., Cross, J., Rogers, C.: An interactive robot platform for introducing reinforcement learning to k-12 students. In: Robotics in Education, pp. 288–301. Springer International Publishing, Cham (2021)
Su, J., Zhong, Y., Ng, D.T.K.: A meta-review of literature on educational approaches for teaching ai at the k-12 levels in the Asia-pacific region. Comput. Educ.: Artif. Intell. 3, 100065 (2022)
Vartiainen, H., Tedre, M., Valtonen, T.: Learning machine learning with very young children: who is teaching whom? Int. J. Child-Comput. Interact. 25, 100182 (2020)
Touretzky, D., Gardner-McCune, C., Martin, F., Seehorn, D.: Envisioning AI for k-12: what should every child know about AI? Proc. AAAI Conf. Artif. Intell. 33(01), 9795–9799 (2019). Jul
Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., Chen, A.: Teachable machine: approachable web-based tool for exploring machine learning classification. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI EA ’20, pp. 1–8. Association for Computing Machinery, New York, NY, USA (2020)
Druga, S.: Growing up with AI. Cognimates: from coding to teaching machines. Ph.D. thesis. Massachusetts Institute of Technology (2018)
Petrovič, P.: Spike up prime interest in physics. In: Robotics in Education, pp. 146–160. Springer International Publishing (2021)
Mandin, S., De Simone, M., Soury-Lavergne, S.: Robot moves as tangible feedback in a mathematical game at primary school. In: Robotics in Education. Advances in Intelligent Systems and Computing, pp. 245–257. Springer International Publishing, Cham (2016)
Laut, J., Kapila, V., Iskander, M.G.: Exposing middle school students to robotics and engineering through LEGO and MATLAB. In: 120th ASEE Annual Conference and Exposition, 2013. 120th ASEE Annual Conference and Exposition; Conference date: 23-06-2013 Through 26-06-2013
Whitman, L., Witherspoon, T.: Using LEGOs to interest high school students and improve K-12 stem education. Change 2, 12 (2003)
Williams, R., Park, H.W., Breazeal, C.: A is for artificial intelligence: the impact of artificial intelligence activities on young children’s perceptions of robots. In: CHI ’19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–11 (2019)
van der Vlist, B., van de Westelaken, R., Bartneck, C., Hu, J., Ahn, R., Barakova, E., Delbressine, F., Feijs, L.: Teaching machine learning to design students. In: Pan, Z., Zhang, X., El Rhalibi, A., Woo, W., Li, Y. (Eds.) Technologies for E-Learning and Digital Entertainment, pp. 206–217. Springer, Berlin Heidelberg (2008)
Cheli, M., Sinapov, J., Danahy, E.E., Rogers, C.: Towards an augmented reality framework for K-12 robotics education. In: Proceedings of the 1st International Workshop on Virtual, Augmented, and Mixed Reality for HRI (VAM-HRI) (2018)
Martínez-Tenor, Á., Cruz-Martín, A., Fernández-Madrigal, J.-A.: Teaching machine learning in robotics interactively: the case of reinforcement learning with LEGO® mindstorms. Interact. Learn. Environ. 27(3), 293–306 (2019)
Sırakaya, M., Alsancak Sırakaya, D.: Augmented reality in stem education: a systematic review. Interact. Learn. Environ. 30(8), 1556–1569 (2022)
Techakosit, S., Nilsook, P.: Using augmented reality for teaching physics (2015)
Huang, Y., Li, H., Fong, R.: Using augmented reality in early art education: a case study in Hong Kong kindergarten. Early Child Dev. Care 186(6), 879–894 (2016)
Lee, D., Park, Y.S.:. Implementation of augmented teleoperation system based on robot operating system (ROS). In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5497–5502 (2018)
Ikeda, B., Szafir, D.: An AR debugging tool for robotics programmers. In: International Workshop on Virtual, Augmented, and Mixed-Reality for Human-Robot Interaction (VAM-HRI) (2021)
Cleaver, A., Faizan, M., Amel, H., Short, E., Sinapov, J.: SENSAR: a visual tool for intelligent robots for collaborative human-robot interaction (2020). arxiv:2011.04515
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Zhang, Z., Lavigne, K., Church, W., Sinapov, J., Rogers, C. (2023). Introducing Reinforcement Learning to K-12 Students with Robots and Augmented Reality. In: Balogh, R., Obdržálek, D., Christoforou, E. (eds) Robotics in Education. RiE 2023. Lecture Notes in Networks and Systems, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-031-38454-7_29
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