The effectiveness of AI as an adaptive learning tutor to improve student understanding

Wasiqotul amaliah

Authors

Wasiqotul amaliah Universitas Islam Negeri Kiai Haji Ahmad Siddiq Jember, Indonesia, Indonesia Author

Keywords:

AI effectiveness, learning, student understanding , tutor to improve

Abstract

The development of artificial intelligence (AI) technology presents significant opportunities for adaptive learning innovation, enabling materials to be tailored to individual student needs. However, the effectiveness of AI as an adaptive tutor in increasing student motivation, engagement, and understanding still needs to be analyzed in depth. This research aims to analyze how the use of AI as an adaptive tutor can increase student motivation, engagement, and understanding in the learning process. The research results show that the use of adaptive AI tutoring has a significant positive effect on student motivation and engagement. Motivation acts as a mediator that strengthens the relationship between AI use and student understanding. These findings indicate that AI not only provides appropriate material but also increases students' emotional involvement in learning. This study concludes that AI as an adaptive learning tutor is effective in increasing students' understanding through motivation and engagement. The implications of this research encourage the development and application of AI that is more personal and interactive to support quality and inclusive learning processes.

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References

Chen, C., Wang, S., & Chen, Y. (2020). Effect of AI-based personalized gamification on motivation and engagement in online learning. Journal of Educational Technology & Society, 23(2), 45-62.

Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM).

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises Implications for Teaching and Learning. Boston: Center for Curriculum Redesign

Sage Publications.Putra, A., & Sari, R. (2022). Enhancing student engagement through AI chatbot integration in Indonesian e-learning platforms. International Journal of Educational Technology, 9(1), 75

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2019.100001

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles, and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001

Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28, 973–1018. https://doi.org/10.1007/s10639-022-11177-3

Chen, C. M., & Li, M. C. (2019). Personalized learning via adaptive AI systems: Effects on motivation and learning achievement. Educational Technology & Society, 22(3), 112–125.

Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in AI-supported personalized learning. Interactive Learning Environments, 27(4), 1–16. https://doi.org/10.1080/10494820.2018.1495654

Graesser, A. C., Hu, X., & Sottilare, R. (2018). Intelligent tutoring systems. Educational Psychologist, 53(2), 1–16. https://doi.org/10.1080/00461520.2018.1445869

Woolf, B. P. (2015). AI and education: Celebrating 30 years of innovation. AI Magazine, 36(4), 5–8. https://doi.org/10.1609/aimag.v36i4.2572

Schindler, L. A. (2017). Computers in education: A meta-analysis of engagement. Computers & Education, 110, 1–14. https://doi.org/10.1016/j.compedu.2017.02.004

Bond, M. (2020). Mapping research on student engagement and educational technology. Computers & Education, 146, 103787. https://doi.org/10.1016/j.compedu.2019.103787

Kumar, J. A., & Silva, P. A. (2023). Artificial intelligence-based adaptive learning systems: Impact on student engagement. Education and Information Technologies, 28, 12055–12078. https://doi.org/10.1007/s10639-023-11652-5

Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics and AI to support learning design. British Journal of Educational Technology, 51(6), 2140–2156. https://doi.org/10.1111/bjet.12909

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2017). Partial least squares structural equation modeling in education research. European Journal of Psychological Assessment, 33(2), 137–145. https://doi.org/10.1027/1015-5759/a000420

D’Mello, S., & Graesser, A. (2015). Feeling, thinking, and computing with affect-aware learning technologies. Educational Psychologist, 50(4), 282–299. https://doi.org/10.1080/00461520.2015.1075398