Employing Generative AI Applications in Learning: A Factor Analysis of Motivations and Effects among Moroccan University Students

Authors

DOI:

https://doi.org/10.32674/vt55r634

Keywords:

Academic performance, Generative AI, Higher Education, memory impairment, procrastination, quality quest, reward quest, study pressure, time pressure

Abstract

This study seeks to understand the nature of the use of generative artificial intelligence (GenAI) applications among Moroccan university students, by investigating the motivations for use and the effects of this use, by adopting the structural equation modeling technique using the partial least squares method (PLS-SEM) using the SmartPLS version 4 program, to analyze the study data collected through a scale prepared for this study and applied to a random sample of (206) male and female students from different Moroccan universities. After verifying the reliability of the proposed model, the study reached a set of results: Study pressure is the main motivation for using generative artificial intelligence applications among students, while the effect of time pressure and the search for quality and rewards was less significant. In terms of effects, the study indicated that the use of applications contributes to improving academic performance, but it also leads to an increase in the tendency towards procrastination and poor ability to remember when used excessively. The study provides an explanatory model for the motivations and effects of using GenAI and recommendations to promote application users’ responsible and ethical use of AI in higher education.

Author Biographies

  • prof. Dr. Mohammed Zemrani, Higher School of Education and Training, Sultan Moulay Slimane University, Morocco

    Mohammed Zemrani is a lecturer professor at the Higher School of Education and Training, Sultan Moulay Slimane University, Morocco.

     

  • Prof. Dr. Brahim Ait Hammou, Education Sciences Faculty, Rabat, Morocco

    Brahim Ait Hammou is a lecturer professor at the Education Sciences Faculty, Rabat, Morocco.

  • Prof. Dr. Salah-Eddine Mansour, Hassan II University, Casablanca, Morocco

    Prof. Dr. Salah-Eddine Mansour is a lecturer professor at Hassan II University, Casablanca, Morocco.

References

Abbas, M., Jam, F.A. & Khan. (2024). T.I. Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. Int J Educ Technol High Educ 21, 10 (2024). https://doi.org/10.1186/s41239-024-00444-7

Amani, S., White, L. L. A., Balart, T., Arora, L., Shryock, K. J., Brumbelow, K., & Watson, K. (2023). Generative AI perceptions: A survey to measure the perceptions of faculty, staff, and students on generative AI tools in academia. arXiv.org. https://doi.org/10.48550/arxiv.2304.14415

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. https://doi.org/10.1186/s41239-023-00411-8

Denkin, R. (2024). On perception of prevalence of cheating and usage of generative AI. arXiv.org. https://doi.org/10.48550/arxiv.2405.18889

Deric, E., Frank, D., & Malenica, M. (2024). Comparison and quantification of GAI tools use among different academic population segments. International Convention on Information and Communication Technology, Electronics and Microelectronics. https://doi.org/10.1109/mipro60963.2024.10569253

Faruk, L. I. D., Rohan, R., Ninrutsirikun, U., & Pal, D. (2023). University students’ acceptance and usage of generative AI (ChatGPT) from a psycho-technical perspective. International Conference on Advances in Information Technology. https://doi.org/10.1145/3628454.3629552

Gonzalez, M., & Wright, R. (2020). Understanding the Role of Technology in Time Management among College Students. Educational Technology Research and Development, 68(3), 1473-1487.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Publications, SAGE.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis: A Global Perspective. Pearson Education.

Hayashi, Y., Russo, C. T., & Wirth, O. (2015). Texting while driving as impulsive choice: A behavioral economic analysis. Accident Analysis & Prevention, 83, 182–189. https://doi.org/10.1016/j.aap.2015.07.025

Henseler, J., Ringle, C.M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 43, (1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Nadif, B., & Bidari, S. (2023). Advanced tools in The EFL classroom: Friend or foe for novice teachers. The Journal of Quality in Education, 13(21), 12–20. https://doi.org/10.37870/joqie.v13i21.336

Masa'deh, R., Majali, S. A., Alkhaffaf, M., Thurasamy, R., Almajali, D., Altarawneh, K., Al-Sherideh, A., & Altarawni, I. (2024). Antecedents of adoption and usage of ChatGPT among Jordanian university students: Empirical study. International Journal of Data and Network Science. https://doi.org/10.5267/j.ijdns.2023.11.024

Moukhliss, G., Lahyani, K., & Diab, G. (2024). The impact of artificial intelligence on research and higher education in Morocco. Journal of Education and Learning, 18(4), 1292-1300. https://doi.org/10.11591/edulearn.v18i4.21511

Rkiki, A., Nadif, B., Sakale, S., & Bidari, S. (2025). Integrating Corrective Feedback in EFL Classrooms: Harmonizing Traditional Pedagogies with Digital Innovations. The Journal of Quality in Education, 15(25), 143–168. https://doi.org/10.37870/joqie.v15i25.472

Saito, N. (2024). Analysis of trends in the use of generative AI for learning by attributes and characteristics of university students. ACEID Official Conference Proceedings. https://doi.org/10.22492/issn.2189-101x.2024.83

Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023). Educator and student perspectives on the impact of generative AI on assessments in higher education. ACM Conference on Learning @ Scale. https://doi.org/10.1145/3573051.3596191

Storm, B. C., & Stone, S. M. (2015). Saving-enhanced memory: The benefits of saving on the learning and remembering of new information. Psychological Science, 26(2), 182-188.

Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13425

Styve, A., Virkki, O. T., & Naeem, U. (2024). Developing critical thinking practices interwoven with generative AI usage in an introductory programming course. IEEE Global Engineering Education Conference. https://doi.org/10.1109/educon60312.2024.10578746

Wecks, J. O., Voshaar, J., Plate, B. J., & Zimmermann, J. (2024). Generative AI usage and academic performance. Social Science Research Network. https://doi.org/10.2139/ssrn.4812513

Wu, S. T., Chiang, Y. T., & Lee, W. (2022). Artificial Intelligence in Education: A Double-edged Sword for Student Motivation and Performance. Computers in Human Behavior Reports, 6, 100187.

Yilmaz, R. M., & Yilmaz, F. G. K. (2023). ChatGPT: The elephant in the room—Opportunities, risks, and policy implications. Journal of Computers in Education. https://doi.org/10.1007/s40692-023-00180-9

Published

2025-11-28

Issue

2026: Online First : American Journal of STEM Education

Section

STEM Education (regular)

How to Cite

Nadif, B., Zemrani, M. Z., Hammou, B. ., & Mansour, S.-E. . (2025). Employing Generative AI Applications in Learning: A Factor Analysis of Motivations and Effects among Moroccan University Students. American Journal of STEM Education. https://doi.org/10.32674/vt55r634