ActiveAI: The Effectiveness of an Interactive Tutoring System in Developing K-12 AI Literacy | SpringerLink
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ActiveAI: The Effectiveness of an Interactive Tutoring System in Developing K-12 AI Literacy

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Technology Enhanced Learning for Inclusive and Equitable Quality Education (EC-TEL 2024)

Abstract

As we witness groundbreaking advancements in Artificial Intelligence (AI), it is clear that the next generation must be equipped with AI literacy: the skill to interact, evaluate, and collaborate with AI systems. This study introduces ActiveAI, a scalable web-based tutoring system aligned with AI4K12’s five big ideas in AI, designed to foster AI literacy among K-12 students through active learning and interaction with intelligent agents. A controlled classroom study involving 171 middle school learners was conducted to assess the effectiveness of ActiveAI in fostering AI literacy skills and competency toward AI. Results showed that, compared to students in the tell-and-practice control condition, students who used ActiveAI exhibited higher post-test performance in the module about how next-word prediction and temperature work in large language models. Students also developed higher self-reported competence toward AI after using ActiveAI than in the control condition. We conclude by suggesting assessment designs that promote deeper engagement with AI concepts by addressing students’ common misconceptions, like “AI thinks just like humans”, in K-12 AI literacy education.

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References

  1. Ali, S., DiPaola, D., Lee, I., Hong, J., Breazeal, C.: Exploring generative models with middle school students. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2021)

    Google Scholar 

  2. Bell, T., Alexander, J., Freeman, I., Grimley, M.: Computer science unplugged: school students doing real computing without computers. New Zealand J. Appl. Comput. Inf. Technol. 13(1), 20–29 (2009)

    Google Scholar 

  3. Bonwell, C.C., Eison, J.A.: Active learning: creating excitement in the classroom. 1991 ASHE-ERIC higher education reports. ERIC (1991)

    Google Scholar 

  4. Chiu, T.K., Meng, H., Chai, C.S., King, I., Wong, S., Yam, Y.: Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Trans. Educ. 65(1), 30–39 (2021)

    Article  Google Scholar 

  5. Crow, T., Luxton-Reilly, A., Wuensche, B.: Intelligent tutoring systems for programming education: a systematic review. In: Proceedings of the 20th Australasian Computing Education Conference, pp. 53–62 (2018)

    Google Scholar 

  6. Druga, S.: Growing up with AI: Cognimates: from coding to teaching machines. Ph.D. thesis, Massachusetts Institute of Technology (2018)

    Google Scholar 

  7. Eguchi, A., Okada, H., Muto, Y.: Contextualizing AI education for k-12 students to enhance their learning of AI literacy through culturally responsive approaches. KI-Künstliche Intelligenz 35(2), 153–161 (2021)

    Article  Google Scholar 

  8. Felder, R.M., Brent, R.: Active learning: an introduction. ASQ Higher Educ. Brief 2(4), 1–5 (2009)

    Google Scholar 

  9. Gennari, R., Melonio, A., Pellegrino, M.A., D’Angelo, M.: How to playfully teach AI to young learners: a systematic literature review. In: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, pp. 1–9 (2023)

    Google Scholar 

  10. Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007)

    Article  Google Scholar 

  11. Henry, J., Hernalesteen, A., Collard, A.S.: Teaching artificial intelligence to k-12 through a role-playing game questioning the intelligence concept. KI-Künstliche Intelligenz 35(2), 171–179 (2021)

    Article  Google Scholar 

  12. Kaspersen, M.H., Bilstrup, K.E.K., Van Mechelen, M., Hjort, A., Bouvin, N.O., Petersen, M.G.: High school students exploring machine learning and its societal implications: opportunities and challenges. Int. J. Child-Comput. Interact., 100539 (2022)

    Google Scholar 

  13. Kluger, A.N., DeNisi, A.: The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119(2), 254 (1996)

    Article  Google Scholar 

  14. Kolb, D.A.: Experiential learning: experience as the source of learning and development. FT Press (2014)

    Google Scholar 

  15. Limón, M.: On the cognitive conflict as an instructional strategy for conceptual change: a critical appraisal. Learn. Instr. 11(4–5), 357–380 (2001)

    Article  Google Scholar 

  16. Ma, R., Sanusi, I.T., Mahipal, V., Gonzales, J.E., Martin, F.G.: Developing machine learning algorithm literacy with novel plugged and unplugged approaches. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education, vol. 1, pp. 298–304 (2023)

    Google Scholar 

  17. Mariescu-Istodor, R., Jormanainen, I.: Machine learning for high school students. In: Proceedings of the 19th Koli Calling International Conference on Computing Education Research, pp. 1–9 (2019)

    Google Scholar 

  18. Mayer, R.E.: Cognitive Theory of Multimedia Learning. Cambridge Handbook of Multimedia Learning, vol. 41, pp. 31–48 (2005)

    Google Scholar 

  19. Nagashima, T., et al.: Using anticipatory diagrammatic self-explanation to support learning and performance in early algebra. Grantee Submission (2021)

    Google Scholar 

  20. Ng, D.T.K., Leung, J.K.L., Su, M.J., Yim, I.H.Y., Qiao, M.S., Chu, S.K.W.: AI literacy in K-16 classrooms. Springer International Publishing AG (2023). https://doi.org/10.1007/978-3-031-18880-0

  21. Ogan, A., Aleven, V., Jones, C.: Culture in the classroom: challenges for assessment in ill-defined domains. In: Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains at Intelligent Tutoring Systems, pp. 92–100 (2006)

    Google Scholar 

  22. Piaget, J.: The Construction of Reality in the Child, vol. 82. Routledge (2013)

    Google Scholar 

  23. Prince, M.: Does active learning work? A review of the research. J. Eng. Educ. 93(3), 223–231 (2004)

    Article  Google Scholar 

  24. Song, J., Yu, J., Yan, L., Zhang, L., Liu, B., Zhang, Y., Lu, Y.: Develop AI teaching and learning resources for compulsory education in China. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 16033–16039 (2023)

    Google Scholar 

  25. Stamper, J., Xiao, R., Hou, X.: Enhancing LLM-based feedback: insights from intelligent tutoring systems and the learning sciences. arXiv preprint arXiv:2405.04645 (2024)

  26. Su, J., Ng, D.T.K., Chu, S.K.W.: Artificial intelligence (AI) literacy in early childhood education: the challenges and opportunities. Comput. Educ.: Arti. Intell. 4, 100124 (2023)

    Google Scholar 

  27. Theophilou, E., Lomonaco, F., Donabauer, G., Ognibene, D., Sánchez-Reina, R.J., Hernàndez-Leo, D.: AI and narrative scripts to educate adolescents about social media algorithms: insights about AI overdependence, trust and awareness. In: Viberg, O., Jivet, I., Muñoz-Merino, P.J., Perifanou, M., Papathoma, T. (eds.) Responsive and Sustainable Educational Futures: 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Aveiro, Portugal, September 4–8, 2023, Proceedings, pp. 415–429. Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978-3-031-42682-7_28

    Chapter  Google Scholar 

  28. Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., Vartiainen, H.: Co-designing machine learning apps in K–12 with primary school children. In: 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT), pp. 308–310. IEEE (2020)

    Google Scholar 

  29. Touretzky, D., Gardner-McCune, C., Cox, B., Uchidiuno, J., Kolodner, J., Stapleton, P.: Lessons learned from teaching artificial intelligence to middle school students. In: Proceedings of the 54th ACM Technical Symposium on Computer Science Education, vol. 2, pp. 1371–1371 (2022)

    Google Scholar 

  30. Touretzky, D., Gardner-McCune, C., Seehorn, D.: Machine learning and the five big ideas in AI. Int. J. Artif. Intell. Educ., 1–34 (2022)

    Google Scholar 

  31. Tseng, Y.J., Xiao, R., Bogart, C., Savelka, J., Sakr, M.: Assessing the efficacy of goal-based scenarios in scaling AI literacy for non-technical learners. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education, vol. 2, pp. 1842–1843 (2024)

    Google Scholar 

  32. Voulgari, I., Zammit, M., Stouraitis, E., Liapis, A., Yannakakis, G.: Learn to machine learn: designing a game based approach for teaching machine learning to primary and secondary education students. In: Interaction Design and Children, pp. 593–598 (2021)

    Google Scholar 

  33. Wang, B., Rau, P.L.P., Yuan, T.: Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behav. Inf. Technol. 42(9), 1324–1337 (2023)

    Article  Google Scholar 

  34. Williams, R., et al.: AI+ ethics curricula for middle school youth: lessons learned from three project-based curricula. Int. J. Artif. Intell. Educ. 33(2), 325–383 (2023)

    Article  Google Scholar 

  35. Wood, D., Bruner, J.S., Ross, G.: The role of tutoring in problem solving. J. Child Psychol. Psychiatry 17(2), 89–100 (1976)

    Article  Google Scholar 

  36. Zimmerman, B.J.: Becoming a self-regulated learner: an overview. Theory Pract. 41(2), 64–70 (2002)

    Article  Google Scholar 

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Tseng, YJ. et al. (2024). ActiveAI: The Effectiveness of an Interactive Tutoring System in Developing K-12 AI Literacy. In: Ferreira Mello, R., Rummel, N., Jivet, I., Pishtari, G., Ruipérez Valiente, J.A. (eds) Technology Enhanced Learning for Inclusive and Equitable Quality Education. EC-TEL 2024. Lecture Notes in Computer Science, vol 15159. Springer, Cham. https://doi.org/10.1007/978-3-031-72315-5_31

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  • DOI: https://doi.org/10.1007/978-3-031-72315-5_31

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