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Submitted
December 13, 2024
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March 11, 2025
Published
May 12, 2025
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Abstract

Artificial Intelligence is advancing rapidly, promising efficient and effective learning methodologies in the educational sector, including the higher education sector. In particular, AI has facilitated the customization and enhancement of students’ learning experiences. Apart from technological factors, personality traits play a role in the extent to which students adopt AI. This study investigated how students’ personality traits from a postgraduate institution influence their attitude toward AI and their willingness to adopt new technology. This study employed a thematic qualitative methodology. In-depth interviews and focus group discussions were conducted to collect data. The findings suggest that the relationship between personality traits and attitudes toward adopting new AI technology is complex. On one hand, personality traits seem to influence the adoption of AI technology. On the other hand, the perceived usefulness of AI technology also appears to trigger personality traits and encourage them to adopt new technology. Furthermore, self-efficacy and positive experiences indirectly influence the adoption of the technology, whereas negative experiences enhance caution in using the technology without necessarily discouraging its use.

Keywords

Artificial Intelligence higher education personality traits qualitative study

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Copyright (c) 2025 Marie-Anne Razafiarivony , Janet N. Odhiambo

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biographies

Marie-Anne Razafiarivony , Adventist University of Africa, Kenya

Marie-Anne Razafiarivony is an associate professor of Management, currently working at the Adventist University of Africa in the Business Administration program. She holds a PhD in Commerce from the University of Santo Tomas, Philippines.  Her research interests are in career and employment issues in organizations and business education.

Janet N. Odhiambo, Adventist University of Africa, Kenya

Dr. Janet Odhiambo is a public health specialist with a focus on behavioral and mental health wellness. She is currently a senior lecturer at the Adventist University of Africa, where she coordinates the Master of Public Health program in the School of Postgraduate Studies in the Department of Applied Sciences. Dr. Odhiambo holds a Doctorate in Public Health with a major in Preventive Care and Lifestyle Medicine from the Adventist University of the Philippines (AUP). She also earned a Master of Psychology with a major in School Counseling from the same institution, as well as a Master of Public Health with a major in Health Promotion and Nutrition from the Adventist International Institute of Advanced Studies. Her research interests center on young adults and women.

How to Cite
Razafiarivony, M.-A., & Odhiambo, J. N. (2025). Exploring Students’ Personality Traits and Attitudes toward Artificial Intelligence and its Adoption in Higher Education Learning Experience. Pan-African Journal of Education and Social Sciences, 6(1), 14–29. https://doi.org/10.56893/pajes2025v06i01.02
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References

  1. Aderibigbe, A. O., Ohenhen, P. E., Nwaobia, N. K., Gidiagba, J. O., & Ani, E. C. (2023). Artificial intelligence in developing countries: Bridging the gap between potential and implementation. Computer Science & IT Research Journal, 4(3), 185-199. https://doi.org/10.51594/csitrj.v4i3.629
  2. Ajibade, P. (2018). Technology Acceptance model limitations and criticisms: exploring the practical applications and use in technology-related studies, mixed-method, and qualitative research. Library Philosophy and Practice (e-journal). 1941. http://digitalcommons.unl.edu/libphilprac/1941
  3. Al-Kfairy, M. (2024). Factors impacting the adoption and acceptance of ChatGPT in educational settings: A narrative review of empirical studies. Applied System Innovation, 7(6), 110. https://doi.org/10.3390/asi7060110
  4. Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Thabit, S., El-Qirem, F. A., Lufti, A., Alrawad, M., Mulhem, A. A., Alkhdour, T., Awad, A. B. & Al-Maroof, R. S. (2022). Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: students’ perceptions at the university level. Electronics, 11(22), 3662, 1-122. https://doi.org/10.3390/electronics11223662
  5. Al Mesmari, S. (2023). Transforming data into actionable insights with cognitive computing and AI. Journal of Software Engineering and Applications, 16(6), 211-222. https://doi.org/10.4236/jsea.2023.166012
  6. Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S. & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
  7. Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374-390. https://doi.org/10.1057/ejis.2014.10
  8. Barrows, J., Dunn, S., & Lloyd, C. A. (2013). Anxiety, self-efficacy, and college exam grades. Universal Journal of Educational Research, 1(3), 204-208. https://doi.org/10.13189/ujer.2013.010310
  9. Bleidorn, W., Hopwood, C. J., & Lucas, R. E. (2018). Life events and personality trait change. Journal of Personality, 86(1), 83-96. https://doi.org/10.1111/jopy.12286
  10. Boguslawski, S., Deer, R., & Dawson, M. G. (2024). Programming education and learner motivation in the age of generative AI: student and educator perspectives. Information and Learning Sciences. https://dx.doi.org/10.1108/ILS-10-2023-0163
  11. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. https://doi.org/10.1191/1478088706qp063oa
  12. Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182. https://doi.org/10.1007/s10796-022-10291-4
  13. Chen, D., Liu, W., & Liu, X. (2024). What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model. Acta Psychologica, 249, 104442. 1- 9. https://doi.org/10.1016/j.actpsy.2024.104442
  14. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
  15. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, January-February, 108-116.
  16. Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note how does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93–105. https://doi.org/10.1287/isre.1070.0153
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  18. Duarte, R., Nobre, Â. L., & De Oliveira Pires, A. L. (2018). Mature Learners’ Participation in Higher Education and Flexible Learning Pathways: Lessons Learned from an Exploratory Experimental Research (pp. 33–53). Springer. https://doi.org/10.1007/978-981-10-8917-6_2
  19. Felix, C. V. (2020). The role of the teacher and AI in education. In International perspectives on the role of technology in humanizing higher education (pp. 33-48). http://dx.doi.org/10.1108/S2055-364120200000033003
  20. George, A. S. (2023). Preparing students for an AI-driven world: Rethinking curriculum and pedagogy in the age of artificial intelligence. Partners Universal Innovative Research Publication, 1(2), 112-136. https://doi.org/10.5281/zenodo.10245675
  21. Goodhue, D. (2007). Comment on Benbasat and Barki’s "Quo Vadis TAM" article. Journal of the Association for Information Systems, 8 (4), 219-222.
  22. Grönroos, C. (1998). Marketing services: the case of a missing product. Journal of business & industrial marketing, 13(4/5), 322-338. https://doi.org/10.1108/08858629810226645
  23. Hannan, E., & Liu, S. (2023). AI: new source of competitiveness in higher education. Competitiveness Review: An International Business Journal, 33(2), 265-279. https://doi.org/10.1108/CR-03-2021-0045sh
  24. Hassan, S., Akhtar, N., & Yılmaz, A. K. (2016). Impact of the conscientiousness as a personality trait on both job and organizational performance. Journal of Managerial Sciences, 10(1). https://qurtuba.edu.pk/jms/default_files/JMS/10_1/JMS_January_June2016_1-14.pdf
  25. Hong, J. C., Zhang, H. L., Ye, J. H., & Ye, J. N. (2021). The effects of academic self-efficacy on vocational students behavioral engagement at school and at firm internships: A model of engagement-value of achievement motivation. Education Sciences, 11(8), 387. https://doi.org/10.3390/educsci11080387
  26. Hutson, J., Jeevanjee T., Vander Graaf, V., Lively, J., Weber, J., Weir, G., Arnone, K., Carnes, G., Vosevich, K., Plate, D., Leary, M., & Edele, S. (2022). Artificial intelligence and the disruption of higher education: Strategies for integrations across disciplines. Creative Education, 13(12), 3953-3980. https://dx.doi.org/10.4236/ce.2022.1312253
  27. Ivanov, S. (2023). The dark side of artificial intelligence in higher education. The Service Industries Journal, 43, 15-16, 1055-1082, https://doi.org/10.1080/02642069.2023.2258799
  28. Jie, A. L. X., & Kamrozzaman, N. A. (2024). The challenges higher education students face in using artificial intelligence (AI) against their learning experiences. Open Journal of Social Sciences, 12, 362-387. https://doi.org/10.4236/jss.2024.1210025
  29. Kim, J., Kadkol, S., Solomon, I., Yeh, H., Soh, J. Y., Nguyen, T. M., Choi, J. Y., Lee, S., Srivatsa, A. V., Nahass, G. R., & Ajilore, O. A. (2023). AI anxiety: A comprehensive analysis of psychological factors and interventions. SSRN Electronic Journal 4573394. https://doi.org/10.2139/ssrn.4573394
  30. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & management 43(6): 740-755. https://doi.org/10.1016/j.im.2006.05.003
  31. Kshetri, N. (2020)."Artificial intelligence in developing countries” IEEE IT Professional, 22(4) 63 - 68. https://doi.org/10.1109/MITP.2019.2951851
  32. Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410. https://doi.org/10.1016/j.techsoc.2020.101410
  33. Lievano-Martínez, F. A., Fernández-Ledesma, J. D., Burgos, D., Branch-Bedoya, J. W., & Jimenez-Builes, J. A. (2022). Intelligent Process Automation: An Application in Manufacturing Industry. Sustainability, 14, 8804. https://doi.org/10.3390/su14148804
  34. Lucas, R. E., & Donnellan, M. B. (2011). Personality development across the life span: Longitudinal analyses with a national sample from Germany. Journal of Personality and Social Psychology, 101, 847–861. https://psycnet.apa.org/doi/10.1037/a0024298
  35. Malerbi, F. K., Nakayama, L. F., Dychiao, R. G., Ribeiro, L. Z., Villanueva, C., Celi, L. A., & Regatieri, C. V. (2023). Digital Education for the Deployment of Artificial Intelligence in Health Care. Journal of Medical Internet Research, 25, e43333. https://pmc.ncbi.nlm.nih.gov/articles/PMC10337407/
  36. Marangunic, N., & Granic, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95. https://doi.org/10.1007/s10209-014-0348-1
  37. Marikyan, D. & Papagiannidis, S. (2023). Technology Acceptance Model: A review. In S. Papagiannidis (Ed), Theory Hub Book. Available at https://open.ncl.ac.uk / ISBN: 9781739604400
  38. McCrae, R. R. (2009). The five-factor model of personality traits: Consensus and controversy. in P. J. Corr & G. Matthews (Eds.), The Cambridge handbook of personality psychology (pp. 148–161). Cambridge University Press. https://doi.org/10.1017/CBO9780511596544.012
  39. Mannuru N. R., Shahriar S., Teel, Z. A., Wang T., Lund B. D., Tijani S., Pohboon, C. O., Agbaji, D., Alhassan J., Galley J., Kousari R., Ogbadu-Oladapo L., Saurav S. K., Srivastava A., Tummuru S. P., Uppala S., Vaidya P. (2023). Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development. Information Development 1–19. https://dx.doi.org/10.1177/02666669231200628
  40. Matzler, K., Bidmon, S., & Grabner‐Kräuter, S. (2006). Individual determinants of brand affect: the role of the personality traits of extraversion and openness to experience. Journal of product & brand management, 15(7), 427-434. https://doi.org/10.1108/10610420610712801
  41. Nieß, C., & Zacher, H. (2015). Openness to experience as a predictor and outcome of upward job changes into managerial and professional positions. PLoS ONE, 10. https://doi.org/10.1371/journal.pone.0131115
  42. Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology, 156(1), 68–94. https://doi.org/10.1080/00223980.2021.2012109
  43. Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Working Papers on Education Policy,7. UNESCO https://www.gcedclearinghouse.org/sites/default/files/resources/190175eng.pdf
  44. Roberts, B. W., Lejuez, C., Krueger, R. F., Richards, J. M., & Hill, P. L. (2014). What is conscientiousness and how can it be assessed? Developmental psychology, 50(5), 1315. https://doi.org/10.1037/a0031109
  45. Samoili, S., Lopez Cobo, M., Gomez Gutierrez, E., De Prato, G., Martinez-Plumed, F. and Delipetrev, B. (2020). AI WATCH. Defining Artificial Intelligence, https://doi.org/10.2760/382730.
  46. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes toward Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
  47. Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial Intelligence: Definition and Background. In: Mission AI. Research for Policy. Springer. https://doi.org/10.1007/978-3-031-21448-6_2
  48. Singh, S. V., & Hiran, K. K. (2022). The impact of AI on teaching and learning in higher education technology. Journal of Higher Education Theory and Practice, 22(13). https://doi.org/10.33423/jhetp.v22i13.5514
  49. Soto, C. J., & John, O. P. (2014). Traits in transition: the structure of parent‐reported personality traits from early childhood to early adulthood. Journal of Personality, 82(3), 182-199. https://doi.org/10.1111/jopy.12044
  50. Tassone, D. (2023). Personality matters: predicting the acceptance of generative artificial intelligence through personality traits and job insecurity (Unpublished Master’s Dissertation, Universidade Católica Portugues, Portugal. https://repositorio.ucp.pt/bitstream/10400.14/42713/1/203327144.pdf
  51. Thomas, G. (2011). A typology for the case study in social science following a review of definition, discourse, and structure. Qualitative inquiry, 17(6), 511-521. https://doi.org/ 10.1177/1077800411409884
  52. Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of educational research, 78(4), 751-796. https://www.jstor.org/stable/40071145
  53. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
  54. Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39 (2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  55. Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/10.2307/27032860
  56. Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
  57. Zorlu, S., & Ünver, G. (2022). Predictive roles of self-regulatory learning strategies and self-efficacy beliefs on English language learning achievement. Turkish Journal of Education, 11(2), 74-92. https://doi.org/10.19128/turje.841709