Authors : Lauristan Choy

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/337v2tbs

Scribd : https://tinyurl.com/3nnsbexp

DOI : https://doi.org/10.38124/ijisrt/26May1357

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid integration of artificial intelligence in higher education has reshaped teaching and learning practices, particularly in relation to self-directed learning. Self-directed learning has become a critical component of contemporary education, enabling students to design their own learning through autonomy, reflection, and critical thinking. However, while artificial intelligence tools offer unique opportunities to personalise learning experiences and support independent study, there are concerns regarding overreliance, ethical implications, and reduced human interaction. This study explores students' perspectives on artificial intelligence and self-directed learning among students enrolled in the Bachelor of Youth and Community Development Programme at the University of Guyana. The study was guided by the constructivist learning theory, which emphasises knowledge construction through experience and reflection. A qualitative research design was employed, utilising a purposive sampling to select 12 BYD students across years one to three. Data were collected through online semi-structured interviews conducted via the Zoom platform and analysed using thematic analysis supported by digital transcription and qualitative analysis tools. The findings reveal that students generally perceive artificial intelligence tools as a valuable support to self-directed learning, particularly in terms of efficiency, access to information, and motivation. Participants indicated that artificial intelligence tools assisted with summarising content, clarifying concepts, and extending learning beyond lectures. However, the study also identified notable challenges, including generic or inaccurate outputs, limited contextual relevance, reduced critical engagement, and ethical concerns related to data privacy and academic integrity. A key tension emerged between the convenience of artificial intelligence-supported learning and the perceived loss of interpersonal and deeper cognitive engagement associated with traditional learning methods. The study concludes that artificial intelligence has both positive and negative implications for self-directed learning; while it can enhance autonomy and learning efficiency, its effective use requires deliberate pedagogical guidance, ethical awareness, and critical evaluation skills. The study offers implications for educators and higher education institutions seeking to integrate artificial intelligence responsibly to support meaningful, ethical, student-centred learning experiences.

Keywords : Artificial Intelligence, Self-Directed Learning, Artificial Intelligence Tools, Higher Education, Qualitative Research.

References :

1. Al‑Azawei, A., & Al‑Hamad, A. (2024). Adaptive learning and AI in higher education. Education and Information Technologies, 29(1), 112–130.
2. Berger, R. (2015). Now I see it, now I don’t: Researcher’s position and reflexivity in qualitative research. Qualitative Research, 15(2), 219–234.
3. Brandt, B. (2020). Self-directed learning in higher education. Journal of Adult Learning, 33(2), 45–60.
4. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
5. Bruner, J. (1996). The culture of education. Harvard University Press.
6. Byrne, D. (2021). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 55(3), 1081–1099.
7. Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Nurse Researcher, 28(3), 36–44.
8. Candy, P. C. (1991). Self-direction for lifelong learning: A comprehensive guide to theory and practice. Jossey-Bass.
9. Chen, X., Zou, D., & Xie, H. (2023). AI-supported personalised learning. Computers & Education, 194, 104676.
10. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage.
11. DeJonckheere, M., & Vaughn, L. M. (2019). Semi-structured interviewing in primary care research. Family Medicine and Community Health, 7(2), 1–6.
12. Fosnot, C. T. (2013). Constructivism: Theory, perspectives, and practice (2nd ed.). Teachers College Press.
13. Hongke, L. (2020). The negative impact of artificial intelligence on college students’ learning. Journal of Educational Issues, 6(1), 45–58.
14. Ifenthaler, D., et al. (2024). Students’ ethical awareness of AI. British Journal of Educational Technology, 55(1), 45–62.
15. Jonassen, D. H. (1999). Designing constructivist learning environments. In C. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. II, pp. 215–239). Lawrence Erlbaum Associates.
16. Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965.
17. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
18. Loyens, S. M., Magda, J., & Rikers, R. M. (2008). Self-directed learning in problem-based learning and its relationships with self-regulated learning. Educational Psychology Review, 20(4), 411–427.
19. McGraw, A., & Bannister, J. (2020). AI-enabled learning and student autonomy. Journal of Learning Analytics, 7(3), 45–60.
20. Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
21. Namjoo, S., et al. (2023). Student perceptions of AI in learning. Education and Information Technologies, 28(5), 5671–5690.
22. Orb, A., Eisenhauer, L., & Wynaden, D. (2001). Ethics in qualitative research. Journal of Nursing Scholarship, 33(1), 93–96.
23. Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544.
24. Piaget, J. (1972). The psychology of the child. Basic Books.
25. Schunk, D. H. (2020). Learning theories: An educational perspective (8th ed.). Pearson.
26. Schwandt, T. A. (2015). The Sage dictionary of qualitative inquiry (4th ed.). Sage.
27. Slade, S., & Ungerer, L. (2022). Ethical considerations in AI-supported learning. Australasian Journal of Educational Technology, 38(2), 1–15.
28. Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837–851.
29. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
30. Yildirim, B., et al. (2023). Risks of AI in student learning. Journal of Computer Assisted Learning, 39(4), 1123–1138.