KH Integration of AI Applications in High School Physics, Kolb’s Convergent Style

Hassane Kemouss; Omar Abdannour; Khaldi Mohamed

doi:10.58355/dirosat.v2i3.80

Authors

Hassane Kemouss University Pedagogical Engineering, Équipe S2IPU, Higher Normal School of Tétouan, Abdelmalek Essaadi University, Morocco
Omar Abdannour University Pedagogical Engineering, Équipe S2IPU, Higher Normal School of Tétouan, Abdelmalek Essaadi University, Morocco
Khaldi Mohamed University Pedagogical Engineering, Équipe S2IPU, Higher Normal School of Tétouan, Abdelmalek Essaadi University, Morocco

DOI:

https://doi.org/10.58355/dirosat.v2i3.80

Keywords:

AI, Kolb Model, Abstract Concepts, High School Physics.

Abstract

This study explores the integration of artificial intelligence (AI) applications in physics teaching at the secondary level. An interactive approach based on Kolb's learning model; The authors evaluated the convergent style of the kolb cycle with the use of simulations and AI-based data analysis systems to improve students' understanding of abstract physics concepts. The results show that these AI tools allow a more intuitive visualization of phenomena and facilitate the interpretation of experimental data. Students who used these resources demonstrated better performance and greater engagement with the subject. This research highlights the potential of AI to enrich physics learning in high school and opens the way to new innovative educational approaches.

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References

Alqahtani, R., Kaliappen, N. and Alqahtani, M. A quality review of adaptive versus non-adaptive learning tools. International journal for quality research, 2021, 15 (1), 45.

Al-Samarraie, H., & Saeed, N. An adaptive e-learning system based on Kolb's learning styles model. Journal of Educational Computing Research, 2019, 56(2), 272-297.

Andreja Istenič Starčič, Human learning and learning analytics in the age of artificial intelligence, British Journal of Educational Technology, 2019, 10.1111/bjet.12879, 50, 6, (2974-2976),

Basu, A., Biswas, G., Kinnebrew, J. S., & Clark, D, A tutor for physics problem-solving: Experimental results. Journal of Interactive Learning Research, 2013, 24(1), 1-35

Emily Zhao, David Moreno, Jessica Huang, "Adaptive Learning Recommendation Engine for Science Curricula".2022, Journal of Computer Assisted Learning

Felder, R. M., & Silverman, L. K. Learning and teaching styles in engineering education. Engineering education, 1988, 78(7), 674-681

Jian Li, Olivia Nguyen, Mark Hernandez, "Intelligent Assessment and Remediation System for Science Learning". 2023, Computers & Education

Huijuan Fu, Samuel Chu and Wenxia Kang "Affordances and Constraints of a Wiki for Primary school Students Group Projects, "Handbook of Research on Educational Communications and Technology" by J. Michael Spector, M. David Merrill, and Marcy P. Driscoll.2013 .

Kolb, D. A. Experiential learning: Experience as the source of learning and development (Vol. 1). 1984. Englewood Cliffs, NJ: Prentice-Hall.)

Lynch, C., Ashley, K. D., Pinkwart, N., & Aleven, V. Artificial intelligence in education: Promises and implications for teaching and learning. Educational Technology,2019, 59(2), 3-9. doi:10.1145/3292051.3300233.

Luckin, R., & Holmes, W. Intelligence unleashed: An argument for AI in education, 2016.

Mahligawati, F., Allanas, E., Butarbutar, MH and Nordin, NAN, Artificial intelligence in physics education: a comprehensive literature review. In Journal of Physics: Conference Series, September 2023, (Vol. 2596, No. 1, pp. 012080). IOP publication.

Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments,2021, 29(1), 142-163.

Papanikolaou, K. A., Grigoriadou, M., & Kornilakis, H. Towards new forms of computer-based assessment: Integrating simulation-based testing and adaptive testing. International Journal of Artificial Intelligence in Education, 2003, 13(3), 227-256.

Sak, R. Differentiated Instruction for Gifted Students. Roeper Review, 2018, 40(4), 237-248

Samantha Johnson, Michael Chen, et al, "An Intelligent Tutoring System for Interactive Physics Simulations", 2022, International Journal of Artificial Intelligence in Education

Sophia Maier, Ethan Wilde, Tanya Gupta, "Dynamic Simulations with Intelligent Tutoring: Enhancing Physics Learning through Interactive Visualizations" 2022, IEEE Transactions on Visualization and Computer Graphics

Tanya Gupta, Ethan Wilde, Sophia Maier, "AI-Driven Visualization and Modeling for Enhanced Science Education", 2022. IEEE Transactions on Learning Technologies