Personalized Learning through AI and its Effects on Students’ Perception and Engagement

Authors

  • Saima Nawaz The University of Lahore, Sargodha Campus
  • Syeda Hoor Fatima Department of Education, The University of Lahore, Sargodha Campus, Pakistan
  • Faisal Saqalain Shah Department of Education, The University of Lahore, Sargodha Campus, Pakistan
  • Zeeshan Shoukat Department of Education, The University of Lahore, Sargodha Campus, Pakistan
  • Ghazanfar Nawaz Department of Education, The University of Lahore, Sargodha Campus, Pakistan
  • Atif Mahmood Department of Education, The University of Lahore, Sargodha Campus, Pakistan

Keywords:

Personalized Learning, Artificial Intelligence, Students’ perception and Engagement

Abstract

This study was exploring personalized learning through AI and its effect on students’ perception and engagement. The main objective of the study was to explore the relationship between students’ perception of AI integration and their academic engagement. The population of the study was all the universities of District Sargodha. To collect the sample, the multistage sampling technique was used. First of all, from all the universities of district Sargodha, one public university and one private university were selected. From each university 6 faculties were selected (social science, information technology, allied health sciences, management sciences, sciences, and languages). A stratum of 50 students was made from each faculty of both universities, and a total of 300 students were selected. This study was quantitative in nature, so a self-developed questionnaire was used to gather data from the respondents. The chi-square was used to find out the conclusions. The results of the study were that there was a significant association between students’ perception of academic engagement and AI-personalized learning content at the graduate level. On the basis of the conclusion, it was recommended that to optimize AI-powered learning for graduate students, prioritize engagement-driven strategies, simplify AI interactions for enhanced accessibility, and continuously refine AI models for high personalized accuracy.

References

Bell, C., Olukemi, A., & Broklyn, P. (2024). AI-Driven Personalization in Digital Marketing: Effectiveness and Ethical Considerations.

bin Salem, I. (2024). Integrating artificial intelligence in personalized learning: A future-oriented approach to enhance student engagement and achievement. International Journal of Post Axial: Futuristic Teaching and Learning, 111-119.

Broklyn, P., Olukemi, A., & Bell, C. (2024). Ai-driven personalization in digital marketing: Effectiveness and ethical considerations. Available at SSRN 4906214.

Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15(1), 1–10. https://doi.org/10.1186/s41239-018-0109-y

Hennekeuser, J., Meier, C., & Schmid, U. (2024). Adaptive learning environments and AI in higher education: The role of personalization for academic success. Computers & Education, 210, 104789. https://doi.org/10.1016/j.compedu.2023.104789

Hennekeuser, J., Meier, C., & Schmid, U. (2024). Adaptive learning environments and AI in higher education: The role of personalization for academic success. Computers & Education, 210, 104789.

Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Hwang, G. J., Sung, H. Y., & Chang, S. C. (2020). A review of artificial intelligence in education: Implications for future classroom learning. Educational Technology & Society, 23(4), 1–14.

Johnson, M., & Smith, T. (2019). Personalized learning and self-efficacy: The role of adaptive educational technologies. Journal of Learning Analytics, 6(3), 45–59. https://doi.org/10.18608/jla.2019.63.4

Nawaz, S., Fatima, S. H., Shah, F. S., Shoukat, Z., Nawaz, G., & Mahmood, A. (2025). Personalized learning through AI and its effects on students’ perception and engagement. Pakistan Journal of Positive Psychology, 2(3), 21–25.

Tapalova, O., & Zhiyenbayeva, A. (2022). Data-driven adaptive learning: AI approaches for identifying student learning gaps. Journal of Educational Computing Research, 60(7), 1723–1740. https://doi.org/10.1177/07356331221084721

Vieriu, R., & Petrea, S. (2025). AI in modern education: Opportunities and cognitive challenges. Journal of Educational Innovation, 12(1), 22–36.

Wu, T. (2023). Human cognition versus artificial intelligence: A comparative study on creativity and understanding. Cognitive Systems Research, 79, 100–115. https://doi.org/10.1016/j.cogsys.2023.07.005

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

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Published

2025-09-25

How to Cite

Saima Nawaz, Syeda Hoor Fatima, Faisal Saqalain Shah, Zeeshan Shoukat, Ghazanfar Nawaz, & Atif Mahmood. (2025). Personalized Learning through AI and its Effects on Students’ Perception and Engagement. Pakistan Journal of Positive Psychology, 2(3), 21–25. Retrieved from https://pjpp.org/index.php/pjpp/article/view/60

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