The Effect of Generative AI Usage on Academic Engagement: A Mixed-Methods Case Study at a College of Education in Ghana
Authors
Department of Integrated Science Education, University of Education (Winneba)
Department of Integrated Science Education, University of Education (Winneba)
Department of Science, St. Ambrose College of education, Dormaa Akwamu. (Winneba)
Department of Science, St. Teresa’s College of Education, Hohoe (Winneba)
Department of Integrated Science Education, University of Education (Winneba)
Article Information
DOI: 10.47772/IJRISS.2025.910000149
Subject Category: Physics
Volume/Issue: 9/10 | Page No: 1763-1775
Publication Timeline
Submitted: 2025-10-02
Accepted: 2025-10-10
Published: 2025-11-06
Abstract
The study was conducted to investigate the impact of the usage of generative AI on the academic engagement of students in a selected college of education in Ghana. The study seeks to explore and provide insights on the relationship between the use of generative AI in learning and students’ academic engagement. This approach was selected because it offers a more comprehensive understanding of the relationship between generative AI use and students’ academic engagement. A sequential-explanatory mixed-method research design is applied in the study to provide in-depth enlightenment, discussion, investigation, and thorough understanding of the generative AI frequently used by students and how it affects their academic engagement. This approach was selected because it offers a more comprehensive understanding of the relationship between generative AI use and students’ academic engagement. Ninety-eight (98) respondents served as participants for the quantitative phase of the study, and twelve (15) interviewees for the qualitative phase. The respondents were selected using convenience sampling, a non-probability sampling technique. The instrument for the quantitative data was a survey questionnaire to examine the type of generative AI mostly used by students and also assess the effect of generative AI on students’ academic engagement. The interview guide, on the other hand, was used to gather qualitative data to obtain rich data that could not be explored using only the quantitative data.
Keywords
The use of Artificial Intelligence (AI) in education
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References
1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. https://doi.org/10.2307/249577 [Google Scholar] [Crossref]
2. Al Shamsi, J. H., Al-Emran, M., & Shaalan, K. (2022). Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants. Education and Information Technologies, 27(6), 8071–8091. https://doi. org/10.1007/s10639-022-10947-3 [Google Scholar] [Crossref]
3. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative Artificial Intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484 [Google Scholar] [Crossref]
4. Brown, J. S., Collins, A., & Harris, G. (1978). Artificial intelligence and learning strategies. In F. O. Harold (Ed.), Learning strategies (pp. 107-139). Academic Press. https://doi.org/10.1016/B978-0-12 526650-5.50010-1 [Google Scholar] [Crossref]
5. Bryman, A. 2006. “Integrating Quantitative and Qualitative Research: How Is It Done?” Qualitative Inquiry 6 (1): 97–113. [Google Scholar] [Crossref]
6. Buchanan, R., Burridge, A., & Bowen, L. (2022). Generative artificial intelligence in education: Enhancing critical thinking through collaborative problem-solving. Journal of Educational Technology & Society, 25(3), 81-90. [Google Scholar] [Crossref]
7. Bulawan, A. A., Tilos, F. G., Bulawan, A., Samonte, J., Alejo, K., & Carasicas, M. (2023). The lived experiences of students in learning with technology: A descriptive phenomenological research study. International Journal of Advanced Multidisciplinary Research and Studies, 3(4), 438–441. https://www.multiresearchjournal.com/arclist/list3.4/id-1445 [Google Scholar] [Crossref]
8. Bureau, J. S., Howard, J. L., Chong, J. X., & Guay, F. (2022). Pathways to student motivation: A meta-analysis of antecedents of autonomous and controlled motivations. Review of Educational Research, 92(1), 46–72. https://doi. org/10.3102/00346543211042426 [Google Scholar] [Crossref]
9. Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems, 13(2), 185–204. https://doi.org/10.1080/07421222.1996.11518128 [Google Scholar] [Crossref]
10. Chiu, T. K. (2023a). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197 [Google Scholar] [Crossref]
11. Chiu, T. K., Ismailov, M., Zhou, X., Xia, Q., Au, C. K., & Chai, C. S. (2023a). Using self-determination theory to explain how community-based learning fosters student interest and identity in integrated STEM education. International Journal of Science and Mathematics Education, 21(S1), 109–130. https://doi.org/10.1007/s10763-023-10382 [Google Scholar] [Crossref]
12. Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023b). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 1–17. https://doi.org/10.1 080/10494820.2023.2172044 [Google Scholar] [Crossref]
13. Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, developments, and future directions. Sprouts: Working Papers on Information Systems, 9(37), 290. https://aisel.aisnet.org/sprouts_all/290 [Google Scholar] [Crossref]
14. Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: SAGE Publications. [Google Scholar] [Crossref]
15. Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approach. Thousand Oaks, CA: SAGE Publications, Inc. [Google Scholar] [Crossref]
16. Creswell, J. W., and V. L. Plano Clark. 2011. Designing and Conducting Mixed Methods Research. Thousand Oaks, CA: Publications, Inc. [Google Scholar] [Crossref]
17. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the eld. International Journal of Educational Technology in Higher Education, 20(1), 1-22. Page 21/28 https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]
18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 [Google Scholar] [Crossref]
19. Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 19–43. https://doi.org/10.1146/annurev-orgpsych-032516-113108 [Google Scholar] [Crossref]
20. Esiyok, E., Gokcearslan, S., & Kucukergin, K. G. (2025). Acceptance of educational use of AI chatbots in the con text of self-directed learning with technology and ICT self-efficacy of undergraduate students. International Journal of Human–Computer Interaction, 41(1), 641–650. https://doi.org/10.1080/10447318.2024.2303557 [Google Scholar] [Crossref]
21. Estrellado, C. J. (2023). Artificial Intelligence in the Philippine educational context: Circumspection and future inquiries. International Journal of Scientific and Research Publications, 13(4), 16. https://doi.org/10.29322/IJSRP.13.04.2023.p13704 [Google Scholar] [Crossref]
22. Garito, M. A. (1991). Arti cial intelligence in education: evolution of the teaching—learning relationship. British Journal of Educational Technology, 22(1), 41-47. https:// doi.org/10.1111/j.1467-8535.1991.tb00050.x [Google Scholar] [Crossref]
23. Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(8), 1–30. https://doi.org/10.17705/1jais.00008 [Google Scholar] [Crossref]
24. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8 [Google Scholar] [Crossref]
25. Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology,15(2), 421. https://doi.org/10.30935/cedtech/13036 [Google Scholar] [Crossref]
26. Hashmi, N., & Bal, A. S. (2024). Generative AI in higher education and beyond. Business Horizons. https://doi.org/10.1016/j.bushor.2024.05.005 [Google Scholar] [Crossref]
27. Hidayat-ur-Rehman, I. (2024). Digital competence and students’ engagement: a comprehensive analysis of smartphone utilization, perceived autonomy and formal digital learning as mediators. Interactive Technology and Smart Education. https://doi.org/10.1108/itse-09-2023-0189 [Google Scholar] [Crossref]
28. Howard, J. L., Bureau, J. S., Guay, F., Chong, J. X., & Ryan, R. M. (2021). Student motivation and associated out comes: A meta-analysis from self-determination theory. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 16(6), 1300–1323. https://doi.org/10.1177/1745691620966789 [Google Scholar] [Crossref]
29. Igbaria, M., Parasuraman, S., & Baroudi, J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143. https://doi.org/10.1080/07421222.1996.11518115 [Google Scholar] [Crossref]
30. Johnson, B. (2023). NYC schools ban ChatGPT over academic dishonesty fears. New York Times. [Google Scholar] [Crossref]
31. Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/ j.tele.2022.101925 [Google Scholar] [Crossref]
32. Kumar, R. (2014). Research methodology: A step-by-step guide for beginners (3rd ed.). Thousand Oaks, CA: SAGE Publications. [Google Scholar] [Crossref]
33. Kurniati, E. Y., & Fithriani, R. (2022). Post graduate students’ perceptions of Quillbot utilization in English academic writing class. Journal of English Language Teaching and Linguistics, 7(3), 437. https://doi.org/10.21462/jeltl.v7i3.852 [Google Scholar] [Crossref]
34. Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178 [Google Scholar] [Crossref]
35. Lewin, S., C. Glenton, and A. D. Oxman. 2009. “Use of Qualitative Methods Alongside Randomized Controlled Trials of Complex Healthcare Interventions: Methodological Study.” British Medical Journal 339: b3496. [Google Scholar] [Crossref]
36. Liang J, Wang L, Luo J, Yan Y and Fan C (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: serial mediating roles of self-efficacy and cognitive engagement. Front. Psychol. 14:1285392. https://doi.org/10.3389/fpsyg.2023.1285392 [Google Scholar] [Crossref]
37. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management in Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790 [Google Scholar] [Crossref]
38. Liu, G. L., Darvin, R., & Ma, C. (2024). Exploring AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer Assisted Language Learning, 1–29. [Google Scholar] [Crossref]
39. Norman, E. W., & Fraenkel, J. R. (2000). How to design and evaluate research in education. New Jersey, NJ: McGraw-Hill. [Google Scholar] [Crossref]
40. O’Cathain, A., E. Murphy, and J. Nicholl. 2010. “Three Techniques for Integrating Data in Mixed-Methods Studies.” British Medical Journal 341: c4587. [Google Scholar] [Crossref]
41. Ocaña-Fernández, Y., Valenzuela-Fernández, L. A. & Garro-Aburto, L. L., 2019. Artificial Intelligence and Its Implications in Higher Education. Journal of Educational Psychology-Propositosy Representaciones, 7(2), pp. 553-568 [Google Scholar] [Crossref]
42. Okoye, M., & Mante, D. (2024). The nexus between artificial intelligence and STEM education: Research on AI applications in higher education. Educational Technology Research and Development, 68(4), 1851–1861. [Google Scholar] [Crossref]
43. Opoku, M. O., & Enu-Kwesi, F. (2019). Relevance of the technology acceptance model (TAM) in information management research: A review of selected empirical evidence. Research Journal of Business and Management, 6(1), 55–62. https://doi.org/10.17261/Pressacademia.2019.1028 [Google Scholar] [Crossref]
44. Oravec, J. (2023). Artificial intelligence implications for academic cheating: Expanding the dimensions of responsible human-AI collaboration with ChatGPT and Bard. Journal of Interactive Learning Research, 34(2), 213–237. [Google Scholar] [Crossref]
45. Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13. https://doi.org/10.1186/s41039-017-0062-8 [Google Scholar] [Crossref]
46. Rodrigues, M., Silva, R., Franco, M. A. P. B., & Oliveira, C. (2024). Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis. Kybernetes. https://doi.org/10.1108/k-09-2023-1666 [Google Scholar] [Crossref]
47. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020 [Google Scholar] [Crossref]
48. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860 [Google Scholar] [Crossref]
49. Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32. https://doi.org/10.1016/j.compedu.2019.04.011 [Google Scholar] [Crossref]
50. Sok, P., & Heng, C. (2023). ChatGPT and academic writing: Student perceptions in Cambodian universities. Asian Journal of Education, 14(3), 245–259. [Google Scholar] [Crossref]
51. Van den Broeck, A., Howard, J. L., Van Vaerenbergh, Y., Leroy, H., & Gagné, M. (2021). Beyond intrinsic and extrinsic motivation: A meta-analysis on self-determination theory’s multidimensional conceptualization of work motivation. Organizational Psychology Review, 11(3), 240–273. https://doi.org/10.1177/20413866211006173 [Google Scholar] [Crossref]
52. Vansteenkiste, M., Sierens, E., Soenens, B., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology, 101(3), 671–688. https://doi.org/10.1037/a0015083 [Google Scholar] [Crossref]
53. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (pp. 5998–6008). Long Beach, CA, USA. [Google Scholar] [Crossref]
54. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb01822.x [Google Scholar] [Crossref]
55. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 [Google Scholar] [Crossref]
56. Yang, Y., Luo, J., Yang, M., Yang, R., & Chen, J. (2024). From surface to deep learning approaches with generative AI in higher education: an analytical framework of student agency. Studies in Higher Education, 49, 817-830. [Google Scholar] [Crossref]
57. Yim, I. H. Y., & Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12, 93–131. https://doi.org/10.1007/s40692-023-00304-9 [Google Scholar] [Crossref]
58. Yim, I. H. Y., & Su, J. (2024). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12, 93–131. https://doi.org/10.1007/s40692-023-00304-9 [Google Scholar] [Crossref]
59. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007a). Technology acceptance: A meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. https://doi.org/10.1108/17465660710834453 [Google Scholar] [Crossref]
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