Thực trạng tương tác, tự giám sát và sử dụng AI có trách nhiệm trong học tập số của sinh viên đại học

Các tác giả

  • Phạm Tuấn Anh Cục Nhà giáo và Cán bộ quản lí giáo dục, Bộ Giáo dục và Đào tạo

Tóm tắt

This study aimed to assess the current state of digital learning interaction and collaboration, self-monitoring and self-assessment, and responsible use of AI among university students. Data was collected using Google Forms from 5,833 students across 7 universities using a convenient sampling method and analyzed using descriptive statistics. The results showed that all three aspects were rated highly by students, with self-monitoring and self-assessment of learning achieving the highest score (Mean = 4.09), followed by responsible use of AI (Mean = 4.08) and digital learning interaction and collaboration (Mean = 4.03). Students demonstrated good ability in seeking support, sharing resources, tracking results, adjusting learning methods, and awareness of the limitations and risks of AI. However, academic exchange with faculty, participation in LMS discussions, reflection on learning strategies, and maintaining independent thinking when using AI are relatively limited. The results provide a practical basis for universities to enhance digital learning interaction, develop self-assessment capabilities, and guide students in using AI safely, ethically, and appropriately for learning objectives.

Tài liệu tham khảo

AlKanaan, H. M. N. (2022). Awareness regarding the implication of artificial intelligence in science education among pre-service science teachers. International Journal of Instruction, 15(3), 895-912. https://doi.org/10.29333/iji.2022.15348a

Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28-47.

Garcia, J. G., Gañgan, M. G. T., Tolentino, M. N., Ligas, M., Moraga, S. D., & Pasilan, A. A. (2020). Canvas adoption: Assessment and acceptance of the learning management system on a web-based platform. ASEAN Journal of Open Distance Learning, 12(1), 24-38.

Guo, S., Shi, L., & Zhai, X. (2024). Validating an instrument for teachers’ acceptance of artificial intelligence in education [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2406.10506

Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, Article 101410. https://doi.org/10.1016/j.techsoc.2020.101410

Nazaretsky, T., Cukurova, M., Ariely, M., & Alexandron, G. (2021). Confirmation bias and trust: Human factors that influence teachers’ attitudes towards AI-based educational technology. CEUR Workshop Proceedings.

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning, University of Michigan. https://files.eric.ed.gov/fulltext/ED338122.pdf

Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education? Journal of Science Education and Technology, 30(2), 139-149. https://doi.org/10.1007/s10956-021-09901-8

Đã Xuất bản

15.08.2026

Cách trích dẫn

Phạm Tuấn Anh. (2026). Thực trạng tương tác, tự giám sát và sử dụng AI có trách nhiệm trong học tập số của sinh viên đại học. Tạp Chí Giáo dục, 26(đặc biệt 11), 439–446. Truy vấn từ https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/6802

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