Level of AI Dependency Vis-à-Vis Study Habits Among College Students at Quezon City University

This study examined the level of Artificial Intelligence (AI) dependency among college students at Quezon City University (QCU) and its relationship with their study habits. Using a descriptive-correlational research design, data were gathered from 372 respondents across five colleges through a validated survey questionnaire. The study assessed AI dependency across four dimensions, frequency and duration of AI tool usage, reliance on AI for academic tasks, compulsive or habitual use of AI tools, and interference of AI usage with independent learning, and study habits across four dimensions: time management and study scheduling, consistency and regularity of review, use of study strategies and techniques, and level of focus and concentration during study sessions. Findings revealed that students demonstrated a moderate level of AI dependency and a moderate-to-agreeable level of study habits. Significant differences in AI dependency were observed when respondents were grouped by demographic profile. Notably, a significant relationship was found between AI dependency and study habits, suggesting that increasing AI dependency is associated with changes in students' approach to academic work. The study recommends institutional policies and instructional strategies to promote responsible and balanced AI use in higher education

Artificial Intelligence, AI Dependency, Study Habits, College Students, Quezon City University

1. Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(1). [Google Scholar] [Crossref]

2. Acosta-Enriquez, B. G., Ballesteros, M. A. A., Guzman Valle, M. A., Morales Angaspilco, J. E., Aquino Lalupú, J. R., Jaico, J. L. B., Germán Reyes, N. C., Alarcón García, R. E., & Janampa Castillo, W. E. (2025). The mediating role of academic stress, critical thinking and performance expectations in the influence of academic self-efficacy on AI dependence: Case study in college students. Computers and Education: Artificial Intelligence, 8, 100381. https://www.sciencedirect.com/science/article/pii/S2666920X25000219 [Google Scholar] [Crossref]

3. Al-Natour, L. M., & Abuquteish, D. (2025). Multifactorial influences on academic performance among medical students: The role of study habits, artificial intelligence, and psychosocial factors. International Journal of Learning, Teaching and Educational Research, 24(6), 928–947. [Google Scholar] [Crossref]

4. Arce, C. M., Gavilanes, J. C., Arce, E. M., Haro, E. M., & Bonilla-Jurado, D. (2025). Artificial intelligence in higher education: Predictive analysis of attitudes and dependency among Ecuadorian university students. Sustainability, 17(17), 7741. https://www.mdpi.com/2071-1050/17/17/7741 [Google Scholar] [Crossref]

5. Asio, J. M. R., & Soriano, I. D. P. (2025). The state of artificial intelligence (AI) use in higher education institutions (HEIs) in the Philippines. IGI Global Scientific Publishing, 523–552. [Google Scholar] [Crossref]

6. Biswas, M., & Murray, J. (2024). The impact of education level on AI reliance, habit formation, and usage. ICAC 2024 – 29th International Conference on Automation and Computing. https://ieeexplore.ieee.org/abstract/document/10718860 [Google Scholar] [Crossref]

7. Blumberg, S., Chui, M., Yee, L., et al. (2024). What is AI (artificial intelligence)? McKinsey. https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-ai [Google Scholar] [Crossref]

8. Buniel, J., Intano, J., Cuartero, O., et al. (2025). Modeling the influence of AI dependence to research productivity among STEM undergraduate students: Case of a state university in the Philippines. Frontiers in Education. [Google Scholar] [Crossref]

9. Burkett, O. (2026). What is AI? SNHU. https://www.snhu.edu/about-us/newsroom/stem/what-is-ai [Google Scholar] [Crossref]

10. Calonia, J., Pagente, D., & Center, R. (2023). Time management and academic achievement: Examining the roles of prioritization, procrastination and socialization. International Journal of Innovative Science and Research Technology. www.ijisrt.com [Google Scholar] [Crossref]

11. Chakraborty, D., & Subramani, D. (2026). AI usage as a mediator between study habits, academic procrastination, and academic self-esteem. ResearchSquare. https://www.researchsquare.com/article/rs-9043305/latest [Google Scholar] [Crossref]

12. Chen, S., & Cheung, A. (2025). Effect of generative artificial intelligence on university students' learning outcomes: A systematic review and meta-analysis. Educational Research Review. https://www.sciencedirect.com/science/article/abs/pii/S1747938X25000740 [Google Scholar] [Crossref]

13. Chen, X., Xie, H., & Hwang, G. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence. [Google Scholar] [Crossref]

14. Dalisaymo, L. (2025). Assessing student dependence on artificial intelligence tools. The International Journal of Technologies in Learning, 32(2), 67–81. https://doi.org/10.18848/2327-0144/CGP/v32i02/67-81 [Google Scholar] [Crossref]

15. Eden, C., Chisom, O., & Adeniyi, I. (2024). Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Scientia Advanced Research and Reviews. [Google Scholar] [Crossref]

16. Estrada-Araoz, E., Mamani-Roque, M., Quispe-Aquise, J., et al. (2025). Academic self-efficacy and dependence on artificial intelligence in a sample of university students. Sapienza: International Journal of Interdisciplinary Studies. https://journals.sapienzaeditorial.com/index.php/SIJIS/article/view/916 [Google Scholar] [Crossref]

17. Ezebuola, B., & Chisoma Wodi, J. (2026). Time management strategies for academic performance of students in public universities in Rivers State. SEAHI Publications. www.seahipublications.org [Google Scholar] [Crossref]

18. Fernando, E., Tolentino, J., Cruz, M., et al. (2025). Profiles of AI dependency: A latent class analysis of Filipino students' academic competencies. Proceedings of the 2025 9th International Conference on Education and Multimedia Technology. https://dl.acm.org/doi/10.1145/3761843.3761871 [Google Scholar] [Crossref]

19. Garcia, M., Rosak-Szyrocka, J., & Bozkurt, A. (2025). Pitfalls of AI integration in education: Skill obsolescence, misuse, and bias. IGI Global. [Google Scholar] [Crossref]

20. Gurramkonda, B., & Pradhan, R. (2024). Exploring the impact of AI-based learning on students' study habits and motivation of university students. [Google Scholar] [Crossref]

21. Hernandez, H., Manalase, R., Dianelo, R., et al. (2025). Dependency on Meta AI chatbot in Messenger among STEM and Non-STEM students in higher education. International Journal of Computing Sciences Research. http://arxiv.org/abs/2507.21059 [Google Scholar] [Crossref]

22. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. [Google Scholar] [Crossref]

23. Holmes, W., Bialik, M., & Fadel, C. (2024). Praise for artificial intelligence in education from international organizations. [Google Scholar] [Crossref]

24. Khalkho, R., Singh, S., Gupta, N., et al. (2024). Impact of educational AI on students' studying habits and academic performance. 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications. https://ieeexplore.ieee.org/abstract/document/10882178 [Google Scholar] [Crossref]

25. Khanduri, V., & Teotia, D. (2023). Revolutionizing learning: An exploratory study on the impact of technology-enhanced learning using digital learning platforms and AI tools on the study habits of university students through focus group discussions. International Journal of Research Publication and Reviews. [Google Scholar] [Crossref]

26. Lim, W., Gunasekara, A., Pallant, J., et al. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education. https://www.sciencedirect.com/science/article/pii/S1472811723000289 [Google Scholar] [Crossref]

27. Louise, D., Bacallo, D., Bie, P., et al. (2024). Digital learning revolution: Identifying the influence of dependency on artificial intelligence tools towards the knowledge acquisition of students. Ignatian International Journal for Multidisciplinary Research. www.icceph.com [Google Scholar] [Crossref]

28. Marcelo, H., Borromeo, J., & Navarra, L. (2025). Quantifying the influence of artificial intelligence dependency on computer engineering students in Bulacan State University. International Journal of Research and Innovation in Social Science, 9(10). https://repository.gyaanarth.com/ijriss/9/10 [Google Scholar] [Crossref]

29. Lim, W., Gunasekara, A., Pallant, J., et al. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education. https://www.sciencedirect.com/science/article/pii/S1472811723000289 [Google Scholar] [Crossref]

30. Louise, D., Bacallo, D., Bie, P., et al. (2024). Digital learning revolution: Identifying the influence of dependency on artificial intelligence tools towards the knowledge acquisition of students. Ignatian International Journal for Multidisciplinary Research. www.icceph.com [Google Scholar] [Crossref]

31. Marcelo, H., Borromeo, J., & Navarra, L. (2025). Quantifying the influence of artificial intelligence dependency on computer engineering students in Bulacan State University. International Journal of Research and Innovation in Social Science, 9(10). https://repository.gyaanarth.com/ijriss/9/10 [Google Scholar] [Crossref]

32. Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). McGraw-Hill Education. [Google Scholar] [Crossref]

33. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. [Google Scholar] [Crossref]

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