GenerationAI_IO1_Pedagogical Framework

The European Commission's support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. [Project Number: 2020-1- NL01-KA201-064712] www.generation-ai.eu/ overview. Procedia Computer Science , 136 , 16–24. https://doi.org/10.1016/j.procs.2018.08.233 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 P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Designing Learning Experiences (pp. 15–25). Springer International Publishing. https://doi.org/10.1007/978-3-030-21814- 0_2 Hooshyar, D., Malva, L., Yang, Y., Pedaste, M., Wang, M., & Lim, H. (2021). An adaptive educational computer game: Effects on students' knowledge and learning attitude in computational thinking. Computers in Human Behavior , 114 , 106575. Hwang, G. J., Chang, S. C., Song, Y., & Hsieh, M. C. (2020). Powering up flipped learning: an online learning environment with a concept map ‐ guided problem ‐ posing strategy. Journal of Computer Assisted Learning , (2020101 2) . https://doi.org/10.1111/jcal.12499 OECD. (2019). OECD Future of Education and Skills 2030: Conceptual Learning Framework . OECD. http://www.oecd.org/education/2030- project/teaching-and- learning/learning/skills/Skills%20for%202030.pdf Popenci, S. A. D. & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning , 12 (1), 1–13. https://doi.org/10.1186/s41039-017-0062-8

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