An Empirical Study on the Impact of AI-Supported Personalized Learning Paths on the Academic Achievement of Multi-ethnic Students in Xinjiang
DOI:
https://doi.org/10.5281/zenodo.17983810Keywords:
AI in Education, Personalized Learning, Multi-Ethnic Students, Academic Achievement, Xinjiang, Educational Equity, Digital TransformationAbstract
We wanted to see how AI can help students from different backgrounds in Xinjiang learn better. Since schools there are using more technology now, we tested whether AI can really meet each student's unique learning needs.To find out, we did surveys, looked at school grades, and had conversations with both students and teachers in city and rural schools.Here’s what we learned: When AI personalizes lessons for each student, it makes learning more fun, helps kids remember things easier, and connects students from different cultures.But it’s not all perfect.Some schools still have slow internet. Some teachers aren’t yet comfortable using new tech tools. And sometimes, the learning content isn’t quite right for local students.So, what’s the bottom line? AI can definitely make learning better in Xinjiang's mixed classrooms — but only if the technology, teaching, and support all work well together.
References
Badamasi, A. E., & Chinonso, O. E. (2024). Impact of AI-powered personalized learning platforms on the academic achievement and motivation of computer science senior secondary school students in Abuja Municipal Area Council (AMAC) FCT-Abuja, Nigeria. Global Academic Journal of Humanities and Social Sciences, 6(05), 203–209. https://doi.org/10.36348/gajhss.2024.v06i05.001
Mulaudzi, L. V., & Hamilton, J. (2025). Lecturer's perspective on the role of AI in personalized learning: Benefits, challenges, and ethical considerations in higher education. Journal of Academic Ethics, 23(4), 1571–1591. https://doi.org/10.1007/s10805-025-09615-1
Du, Y., Song, W., Coa, Y., et al. (2024). Design of personalized AI examination system based on reinforcement learning. In Proceedings of the 2024 6th International Conference on Computer Science and Technologies in Education (CSTE) (pp. 119–124). https://doi.org/10.1109/cste62025.2024.00029
Kang, Y. S., Jin, S. W., Hwang, C. R., et al. (2025). AI-based personalized learning support system for university students. Journal of the Semiconductor & Display Technology, 24(2), 102–107.
Vorobyeva, K. I., Belous, S., Savchenko, N. V., et al. (2025). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2), ep600. https://doi.org/10.30935/cedtech/16108
Bafandkar, S., Chung, S., Khosravian, H., et al. (2025). PAPPL: Personalized AI-powered progressive learning platform. [Preprint/Conference Proceedings without journal title].
Tanchuk, N. J., & Taylor, R. M. (2025). Personalized learning with AI tutors: Assessing and advancing epistemic trustworthiness. Educational Theory, 75(2), 182–205. https://doi.org/10.1111/edth.70009
Xu, Q., Liu, Y., & Li, X. (2025). Unlocking student potential: How AI-driven personalized feedback shapes goal achievement, self-efficacy, and learning engagement through a self-determination lens. Learning and Motivation, 91, 102138. https://doi.org/10.1016/j.lmot.2025.102138
Kallakurchi, J., & Banerji, P. (2023). Personalized learning path (PLP) – “App” for improving academic performance and prevention of dropouts in India. In T. Ahram & R. Taiar (Eds.), Human interaction and emerging technologies (IHIET-AI 2023): Artificial intelligence and future applications (pp. 475–483). AHFE International. https://doi.org/10.54941/ahfe1002935
Wang, X., Liu, Q., Pang, H., et al. (2023). What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis. Computers & Education, 194, 104703. https://doi.org/10.1016/j.compedu.2022.104703
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ziwei Liu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
