International Journal of Artificial Intelligence and Education Technology
2835-2432ISSN (Online)
Volume 4 , Issue 1 , PP: 36–49, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Marina Sagatovna Abdurashidova 1 * , Muhammad Eid Balbaa 2
Modern higher education campuses now use intelligent learning tools as standard educational resources yet students learning results depend on their understanding of these tools and their implementation in academic work. The study analyzes how students pre-pare to use educational tools while investigating the connection between their preparedness and their judgment of educational benefits. The study uses an open student-perception dataset to conduct empirical research which includes developing constructs and profiling readiness and creating predictive models and establishing pathways. The study introduces two measurement methods which include source breadth to measure how students acquire knowledge about intelligent tools through different information channels and an advantage score to present perceived benefits for educational activities. The three-profile segmentation method shows that different groups in the sample display distinct levels of preparedness and value assessment. The Random Forest model demonstrates superior performance because it achieves the highest accuracy among all tested models in the predictive stage. The selected model exhibits an accuracy rate of 0.789 and a precision rate of 0.714 and a recall rate of 1.000 and an F1 score of 0.833 and an area under the receiver operating characteristic curve of 0.806 in hold-out evaluation. The analysis of variable importance indicates that AI knowledge and grade-point average and information breadth and profile membership serve as the main factors that explain the results. The final stage of the process transforms analytical results into distinct educational pathways which focus on developing essential literacy skills and implementing structured curriculum materials and providing support for governance matters and enabling advanced collaborative learning. The results demonstrate that the educational benefits of intelligent tools depend more on students’ preparedness to use them than on their initial exposure to the tools.
Education technology , Higher education , Student readiness , Intelligent learning tools , Adoption archetypes , Educational value
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j. caeai.2024.100215
Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, Article 100294. https://doi.org/10.1016/j.caeai.2024.100294
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, Article 63. https://doi.org/10.1186/s41239-024-00493-y
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j. caeai.2022.100118
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, Article 2. https://doi.org/10.1007/s40979-024-00149-4
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, Article 100221. https://doi. org/10.1016/j.caeai.2024.100221
Mansoor, H. M. H., Bawazir, A., Alsabri, M. A., Alharbi, A. A., & Okela, A. H. (2024). Artificial intelligence literacy among university students: A comparative transnational survey. Frontiers in Communication, 9, Article 1478476. https://doi.org/10.3389/fcomm.2024.1478476
Palanci, A., Yılmaz, R. M., & Turan, Z. (2024). Learning analytics in distance education: A systematic review study. Education and Information Technologies, 29, 22629–22650. https://doi.org/10. 1007/s10639-024-12737-5
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education Artificial Intelligence, 7, Article 100259. https://doi.org/10.1016/j.caeai.2024.100259
Von Garrel, J., & Mayer, J. (2023). Artificial intelligence in studies: Use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 10, Article 799. https://doi.org/10.1057/s41599-023-02304-7
