Bansal, U. (2023). Artificial Intelligence in Indian Education: Navigating Challenges and Embracing Opportunities. JOURNAL GLOBAL VALUES, XIV (S.Issue). https://doi.org/10.31995/jgv.2023.v14is3.024
Bers, M. U. (2020). Coding as a Playground. Routledge. https://doi.org/10.4324/9781003022602
Burke, Q., & Kafai, Y. B. (2014). Decade of Game Making for Learning: From Tools to Communities. In Handbook of Digital Games (pp. 689–709). Wiley. https://doi.org/10.1002/9781118796443.ch26
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations. In U.S. Department of Education’s.
du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
Grover, S., & Pea, R. (2013). Computational Thinking in K–12. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Grover, S., & Pea, R. (2018). Computational Thinking: A Competency Whose Time Has Come. In Computer Science Education. Bloomsbury Academic. https://doi.org/10.5040/9781350057142.ch-003
Lampou, R. (2023). The Integration of Artificial Intelligence in Education: Opportunities and Challenges. Review of Artificial Intelligence in Education, 4(00). https://doi.org/10.37497/rev.artif.intell.educ.v4i00.15
Lohr, D., Berges, M., Chugh, A., & Striewe, M. (2024). Adaptive Learning Systems in Programming Education: A Prototype for Enhanced Formative Feedback. Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. https://doi.org/10.18420/delfi2024_57
Miller, D. P., & Nourbakhsh, I. (2016). Robotics for Education (pp. 2115–2134). https://doi.org/10.1007/978-3-319-32552-1_79
Nadeem, M., Oroszlanyova, M., & Farag, W. (2023). Effect of Digital Game-Based Learning on Student Engagement and Motivation. Computers, 12(9). https://doi.org/10.3390/computers12090177
Nagaraj, B. K., Kalaivani, A., Begum, R. S., Akila, S., Sachdev, H. K., & Kumar, N. S. (2023). The Emerging Role of Artificial Intelligence in STEM Higher Education: A Critical Review. In International Research Journal of Multidisciplinary Technovation (Vol. 5, Issue 5). https://doi.org/10.54392/irjmt2351
Ocaña, J. M., Morales-Urrutia, E. K., Pérez-Marín, D., & Pizarro, C. (2023). About Gamifying an Emotional Learning Companion to Teach Programming to Primary Education Students. Simulation and Gaming, 54(4). https://doi.org/10.1177/10468781231175013
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch. Communications of the ACM, 52(11), 60–67. https://doi.org/10.1145/1592761.1592779
Sáez-López, J.-M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129–141. https://doi.org/10.1016/j.compedu.2016.03.003
Seymour P. (1980). Mindstorms: children, computers, and powerful ideas. Basic Books, Inc.
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962
Top, O., & Arabacıoglu, T. (2024). Integrating Computational Thinking into Mathematics Education: Its Effects On Achievement, Motivation, And Learning Strategies. Bayburt Eğitim Fakültesi Dergisi, 19(42), 2034–2066. https://doi.org/10.35675/befdergi.1385749
Zhang, W., Guan, Y., & Hu, Z. (2024). The efficacy of project-based learning in enhancing computational thinking among students: A meta-analysis of 31 experiments and quasi-experiments. Education and Information Technologies, 29(11), 14513–14545. https://doi.org/10.1007/s10639-023-12392-2
Zolfagharnasab, M. H., & Damari, S. (2024). A Comparative Analysis of Machine Learning Models in News Categorization. U.Porto Journal of Engineering, 10(3), 23–38. https://doi.org/10.24840/2183-6493_0010-003_002464
