Volume IV: Society, Culture, and Local Ecological Wisdom

Toward Human-Centric AI Education: Integrating Islamic Ethical Values and Western AI Literacy to Foster Responsible AI Engagement among University Students in Thailand

  • Mohamed Soliman Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand; Faculty of Education, Universitas Negeri Malang, Indonesia; School of Business Management, Universiti Utara Malaysia, Malaysia; College of Entrepreneurship and Innovation, Midocean University, Comoros.
  • Tawat Noipom Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand
  • Mahmood Hayeemad Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand
  • Aris Hassama Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand
  • Ruslee Nuh Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand
  • Chutikarn Petkaew Business Innovation Department, Faculty of Islamic Sciences, Prince of Songkla University, Thailand
  • Ibtisam Hama Faculty of Sciences and Technology, Fatoni University, Thailand
  • Md Shamimul Islam UBD School of Business and Economics, Universiti Brunei Darussalam, Brunei

Keywords

Human-centric AI education; Islamic ethical epistemology; AI literacy; Responsible AI engagement; Digital religious literacy; Higher education.

Abstract

Discussion of artificial intelligence (AI) in universities has run almost entirely along Western cognitive and regulatory lines, and religious moral reasoning has been left out. This study builds and tests the first model of responsible AI engagement to join Islamic ethical epistemology with Western AI-literacy thinking, focusing on Muslim university students in Thailand’s southern border provinces. Undergraduate and postgraduate students at Islamic and public universities completed a cross-sectional survey. The data were analysed with partial least squares structural equation modelling (PLS-SEM), a complementary artificial neural network (ANN), and fuzzy- set qualitative comparative analysis (fsQCA). Islamic ethical awareness, AI knowledge competency, and digital religious literacy each shaped a responsible AI attitude, which in turn drove the intention to use AI ethically (β = 0.565). Perceived usefulness did not. Institutional support strengthened the link between attitude and intention, and the fsQCA results put ethical awareness and a responsible attitude at the core of every high-engagement configuration. The study introduces religiously grounded constructs into AI-literacy research and triangulates symmetric, predictive, and configurational evidence. The results give curriculum designers, university leaders, and AI-governance bodies a tested basis for ethically anchored, human-centred AI education.

References

  1. Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of
  2. generative AI usage among university students. International Journal of Educational Technology in Higher
  3. Education, 21(1), 10. https://doi.org/10.1186/s41239-024-00444-7
  4. Abror, M., & Hakim, L. (2025). The role of Islamic education in building digital wellbeing and social media ethics
  5. among Muslim youth. West Science Islamic Studies, 3(1), 1–12.
  6. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2),
  7. 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  8. Ali, F., Hassan, M., & Rahman, S. (2025). Islamic ethics and AI: An evaluation of existing approaches to AI using
  9. trusteeship ethics. Philosophy & Technology, 38(2), 34. https://doi.org/10.1007/s13347-025-00845-0
  10. Ali, R. A., Soliman, M., Weahama, M. R., Assalihee, M., & Mahmud, I. (2025). Investigating continuous intention
  11. to use metaverse in higher education institutions: A dual-staged structural equation modeling–artificial
  12. neural network approach. Smart Learning Environments, 12(1), 3. https://doi.org/10.1186/s40561-024-
  13. 00357-y
  14. Alshamy, A., Al-Harthi, A. S., & Abdullah, S. (2025). Perceptions of generative AI tools in higher education:
  15. Insights from students and academics at Sultan Qaboos University. Education Sciences, 15(2), 145.
  16. https://doi.org/10.3390/educsci15020145
  17. An, X., Chai, C. S., Li, Y., Zhou, Y., & Yang, B. (2023). Modeling students’ perceptions of artificial intelligence
  18. assisted language learning. Computer Assisted Language Learning , 36(7), 1–22.
  19. https://doi.org/10.1080/09588221.2023.2246519
  20. Athoillah, S., Rahman, A., & Fauzi, A. (2023). Religious digital literacy of urban Muslim society in Indonesia: A
  21. systematic literature review. Akademika: Jurnal Pemikiran Islam, 28(2), 245–264.
  22. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological
  23. research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social
  24. Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
  25. Bouteraa, M., Bin-Nashwan, S. A., Al-Daihani, M., Dirie, K. A., Benlahcene, A., Sadallah, M., Zaki, H. O.,
  26. Mohamad, A., Ashaari, M. A., Hassan, S. A., & Ramli, M. (2024). Understanding the diffusion of AI-
  27. generative (ChatGPT) in higher education: Does students’ integrity matter? Computers in Human Behavior
  28. Reports, 14, 100402. https://doi.org/10.1016/j.chbr.2024.100402
  29. Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3),
  30. 185–216. https://doi.org/10.1177/135910457000100301
  31. Chee, H., Park, S., & Kim, J. (2024). A competency framework for AI literacy: Variations by different learner
  32. groups and an implied learning pathway. British Journal of Educational Technology, 55(6), 2419–2444.
  33. https://doi.org/10.1111/bjet.13442
  34. Chiu, T. K. F. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to
  35. support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
  36. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology.
  37. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  38. Du, L., Lv, X., & Wang, Y. (2024). Factors influencing students’ acceptance and use of generative artificial
  39. intelligence in elementary education: An expansion of the UTAUT model. Education and Information
  40. Technologies, 29(18), 24715–24739. https://doi.org/10.1007/s10639-024-12835-4
  41. Eke, D. O. (2023). ChatGPT and the rise of generative AI: Threat to academic integrity? Journal of Responsible
  42. Technology, 13, 100060. https://doi.org/10.1016/j.jrt.2023.100060
  43. Elmahjub, E. (2023). Artificial intelligence (AI) in Islamic ethics: Towards pluralist ethical benchmarking for AI.
  44. Philosophy & Technology, 36(4), 73. https://doi.org/10.1007/s13347-023-00668-x
  45. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using GPower 3.1: Tests
  46. for correlation and regression analyses. Behavior Research Methods, 41*(4), 1149–1160.
  47. 560 | ICON-IMAD XV Proceedings · Volume IV · 2026
  48. International Conference on Islam in the Malay World (ICON-IMAD XV), 2026
  49. https://doi.org/10.3758/BRM.41.4.1149
  50. Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research.
  51. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
  52. Gorian, E., & Korshunova, O. (2024). Digital ethics of artificial intelligence in Saudi Arabia and United Arab
  53. Emirates. Malaysian Journal of Syariah and Law, 12(2), 333–349.
  54. Gruenhagen, J. H., Sinclair, P. M., Carroll, J.-A., Baker, P. R. A., Wilson, A., & Demant, D. (2024). The rapid
  55. rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots
  56. for assistance with assessments. Computers and Education: Artificial Intelligence, 7, 100273.
  57. https://doi.org/10.1016/j.caeai.2024.100273
  58. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-
  59. SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  60. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-
  61. based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
  62. https://doi.org/10.1007/s11747-014-0403-8
  63. Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial
  64. intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial
  65. Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
  66. Jones, J. (2026). On the measurement of AI literacy among students in higher education: A scoping review.
  67. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 1(1), 1–23.
  68. Kannike, U. M. M. (2025). Exploring the ethical governance of artificial intelligence from an Islamic ethical
  69. perspective. Jurnal Fiqh, 22(1), 1–30.
  70. Karan, B. (2025). Influence of higher education students’ perceived behaviour on their artificial intelligence
  71. acceptance: An empirical investigation using the technology acceptance model. Journal of Applied
  72. Research in Higher Education. Advance online publication. https://doi.org/10.1108/JARHE-05-2024-0234
  73. Khalid, J., Mi, C., Soliman, M., Abbasi, G. A., & Ali, A. J. (2025). Augmenting humanity: Industry 5.0 and the
  74. rise of human-machine collaboration. Emerald Publishing.
  75. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International
  76. Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  77. Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged
  78. structural equation modeling-artificial neural network approach. International Journal of Information
  79. Management, 51, 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
  80. Li, K. (2023). Determinants of college students’ actual use of AI-based systems: An extension of the technology
  81. acceptance model. Sustainability, 15(6), 5221. https://doi.org/10.3390/su15065221
  82. Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021).
  83. Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least
  84. squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
  85. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of
  86. the 2020 CHI Conference on Human Factors in Computing Systems , 1–16.
  87. https://doi.org/10.1145/3313831.3376727
  88. Ma, D., Akram, H., & Chen, I.-H. (2024). Artificial intelligence in higher education: A cross-cultural examination
  89. of students’ behavioral intentions and attitudes. The International Review of Research in Open and
  90. Distributed Learning, 25(3), 134–157. https://doi.org/10.19173/irrodl.v25i3.7703
  91. Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial
  92. intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94–111.
  93. https://doi.org/10.1080/02188791.2024.2305017
  94. Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey
  95. research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx.
  96. https://doi.org/10.47263/JASEM.4(2)01
  97. Missier, C. A. (2025). A qualitative study of digital religious influence: Perspectives from Christian, Hindu, and
  98. Muslim Gen Y and Gen Z in Mumbai, India. Religions, 16(3), 312. https://doi.org/10.3390/rel16030312
  99. Mohamed, M. G., Mahmoud, F. H., & Hassan, N. (2025). Generative artificial intelligence acceptance, anxiety,
  100. and behavioral intention in the Middle East: A TAM-based structural equation modelling approach. BMC
  101. Nursing, 24, 412. https://doi.org/10.1186/s12912-025-02934-z
  102. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory
  103. review. Computers and Education: Artificial Intelligence , 2, 100041.
  104. https://doi.org/10.1016/j.caeai.2021.100041
  105. O’Dea, X., & Ghori, S. (2024). Factors affecting university students’ generative AI literacy: Evidence and
  106. evaluation in the UK and Hong Kong contexts. Policy Futures in Education. Advance online publication.
  107. ICON-IMAD XV Proceedings · Volume IV · 2026| 561
  108. International Conference on Islam in the Malay World (ICON-IMAD XV), 2026
  109. https://doi.org/10.1177/14782103241296298
  110. Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set qualitative comparative analysis (fsQCA): Guidelines for
  111. research practice in information systems and marketing. International Journal of Information Management,
  112. 58, 102310. https://doi.org/10.1016/j.ijinfomgt.2021.102310
  113. Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2023). Detection of GPT-4 generated text in
  114. higher education: Combining academic judgement and software to identify generative AI tool misuse.
  115. Journal of Academic Ethics, 22, 89–113. https://doi.org/10.1007/s10805-023-09492-6
  116. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral
  117. research: A critical review of the literature and recommended remedies. Journal of Applied Psychology,
  118. 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  119. Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press.
  120. Raquib, A., Channa, B., Zubair, T., & Qadir, J. (2022). Islamic virtue-based ethics for artificial intelligence.
  121. Discover Artificial Intelligence, 2(1), 11. https://doi.org/10.1007/s44163-022-00028-2
  122. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. SmartPLS GmbH. https://www.smartpls.com
  123. Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and
  124. validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211.
  125. https://doi.org/10.1016/j.ausmj.2019.05.003
  126. Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative
  127. comparative analysis. Cambridge University Press. https://doi.org/10.1017/CBO9781139004244
  128. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive
  129. model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11),
  130. 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
  131. Smit, M., Bond-Barnard, T., & Steyn, H. (2025). Ambiguous regulations for dealing with AI in higher education
  132. can lead to moral hazards among students. Project Leadership and Society, 6, 100148.
  133. https://doi.org/10.1016/j.plas.2025.100148
  134. Soliman, M., Ali, R. A., Khalid, J., Mahmud, I., & Ali, W. B. (2025). Modelling continuous intention to use
  135. generative artificial intelligence as an educational tool among university students: Findings from PLS-SEM
  136. and ANN. Journal of Computers in Education, 12(1), 897–928. https://doi.org/10.1007/s40692-024-00333-
  137. y
  138. Soliman, M., Ali, R. A., Khalid, J., Mahmud, I., & Assalihee, M. (2023). Modeling the continuous intention to
  139. use the metaverse as a learning platform: PLS-SEM and fsQCA approach. In M. A. Al-Sharafi, M. Al-
  140. Emran, G. W.-H. Tan, & K.-B. Ooi (Eds.), Current and future trends on intelligent technology adoption
  141. (Studies in Computational Intelligence, Vol. 1128, pp. 41–62). Springer. https://doi.org/10.1007/978-3-031-
  142. 48397-4_3
  143. Soliman, M., Ali, R. A., & Noipom, T. (2025). Unlocking AI-powered tools adoption among university students:
  144. A fuzzy-set approach. Journal of Information and Communication Technology, 24(1), 1–28.
  145. https://doi.org/10.32890/jict2025.24.1.1
  146. Soliman, M., Ali Abdelwahed, R. A., Mahmud, I., Hayeemad, M., & Khalid, J. (2025). Ethical and societal
  147. considerations of AI in business. In J. Khalid, C. Mi, M. Soliman, G. A. Abbasi, & A. J. Ali (Eds.),
  148. Augmenting humanity: Industry 5.0 and the rise of human-machine collaboration. Emerald Publishing.
  149. Soliman, M., Assalihee, M., Weahama, M. R., & Ali, R. A. (2024). Predicting continuous intention to use e-
  150. learning platforms among university students: An integrated model. International Journal of Information
  151. and Education Technology, 14(12). https://doi.org/10.18178/ijiet.2024.14.12.2203
  152. Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and
  153. Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159
  154. Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use
  155. of technology. Interactive Learning Environments , 32(9), 5142–5155.
  156. https://doi.org/10.1080/10494820.2023.2209881
  157. Sullivan, M., Kelly, A., & McLaughlin, P. (2023). ChatGPT in higher education: Considerations for academic
  158. integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 31–40.
  159. https://doi.org/10.37074/jalt.2023.6.1.17
  160. Wang, K., Sun, B., & Li, Y. (2025). Artificial intelligence in higher education: The impact of need satisfaction on
  161. artificial intelligence literacy mediated by self-regulated learning strategies. Behavioral Sciences, 15(1), 64.
  162. https://doi.org/10.3390/bs15010064
  163. Wang, W., Zhang, Y., & Chen, X. (2025). College students’ behavioural intentions of AI-assisted language
  164. learning: Based on the technology acceptance model. Journal of Computer Assisted Learning, 41(2),
  165. e13098. https://doi.org/10.1111/jcal.13098
  166. Waseem, U., & Ahmad, S. (2025). Islamic ethical perspectives on AI and digital transformation in the 21st
  167. 562 | ICON-IMAD XV Proceedings · Volume IV · 2026
  168. International Conference on Islam in the Malay World (ICON-IMAD XV), 2026
  169. century. Wah Academia Journal of Global Religions, 3(1), 45–68.
  170. Wiredu, J., Abdul-Rahaman, N., & Mensah, A. (2024). Impact of generative AI in academic integrity and learning
  171. outcomes: A case study in the Upper East Region. Asian Journal of Research in Computer Science, 17(6),
  172. 217–231. https://doi.org/10.9734/ajrcos/2024/v17i6471
  173. Zaid, B., Fedtke, J., Shin, D. D., El Kadoussi, A., & Ibahrine, M. (2022). Digital Islam and Muslim millennials:
  174. How social media influencers reimagine religious authority and Islamic practices. Religions, 13(4), 335.
  175. https://doi.org/10.3390/rel13040335
  176. Appendix: Questionnaire Items
  177. All items were measured on a seven-point Likert scale from 1 (strongly disagree) to 7 (strongly agree).
  178. Construct (source) Code Item
  179. Islamic Ethical Awareness
  180. (Raquib et al., 2022; Abror &
  181. Hakim, 2025)
  182. IEA1 I consider whether using an AI tool
  183. is consistent with Islamic
  184. ethical principles before
  185. relying on it.
  186. IEA2 I believe that knowledge gained
  187. through AI carries a moral
  188. trust (amanah) to use it
  189. honestly.
  190. IEA3 Fairness and justice (’adl) guide
  191. how I think AI should be
  192. used in my studies.
  193. IEA4 I avoid uses of AI that could harm
  194. others or spread falsehood.
  195. IEA5 Protecting my intellect and
  196. integrity (hifz al-’aql)
  197. matters more to me than the
  198. convenience AI offers.
  199. AI Knowledge Competency
  200. (Chiu, 2024; Hornberger et
  201. al., 2023)
  202. AIC1 I understand the basic principles of
  203. how AI systems generate
  204. their outputs.
  205. AIC2 I can recognise the limitations and
  206. possible errors of AI tools.
  207. AIC3 I am able to evaluate whether an
  208. AI-generated answer is
  209. accurate.
  210. AIC4 I understand how bias can arise in
  211. AI systems.
  212. AIC5 I can use AI tools effectively for
  213. academic tasks.
  214. Perceived Usefulness of AI
  215. (Davis, 1989; Li, 2023)
  216. PU1 Using AI tools improves my
  217. academic performance.
  218. PU2 AI tools help me complete
  219. academic tasks more quickly.
  220. PU3 AI tools are useful for my learning.
  221. PU4 Using AI enhances the quality of
  222. my work.
  223. Digital Religious Literacy
  224. (Athoillah et al., 2023; Zaid
  225. et al., 2022)
  226. DRL1 I can find reliable religious
  227. knowledge using digital
  228. sources.
  229. DRL2 I can judge whether online
  230. religious content is authentic
  231. and trustworthy.
  232. DRL3 I apply my faith values when
  233. evaluating digital
  234. information.
  235. ICON-IMAD XV Proceedings · Volume IV · 2026| 563
  236. International Conference on Islam in the Malay World (ICON-IMAD XV), 2026
  237. DRL4 I am able to identify religious
  238. misinformation online.
  239. DRL5 I use digital tools in ways
  240. consistent with my religious
  241. values.
  242. Responsible AI Attitude (Ajzen,
  243. 1991)
  244. RAA1 Using AI responsibly and ethically
  245. is important to me.
  246. RAA2 I believe AI should be used in ways
  247. that respect honesty and
  248. integrity.
  249. RAA3 A responsible attitude should guide
  250. every use of AI in education.
  251. RAA4 I value keeping human judgement
  252. central when using AI.
  253. RAA5 Being accountable for how I use AI
  254. is something I care about.
  255. Educational Institutional
  256. Support (Du et al., 2024)
  257. EIS1 My university provides clear
  258. guidelines for the ethical use
  259. of AI.
  260. EIS2 My university offers training and
  261. resources on responsible AI
  262. use.
  263. EIS3 My instructors support the
  264. responsible use of AI in
  265. coursework.
  266. EIS4 My university has policies that
  267. encourage ethical AI use.
  268. Intention to Use Ethical AI
  269. (Ajzen, 1991; Davis, 1989)
  270. INT1 I intend to use AI tools ethically in
  271. my studies.
  272. INT2 I will follow responsible practices
  273. whenever I use AI.
  274. INT3 I plan to use AI in ways consistent
  275. with academic integrity.
  276. INT4 I intend to avoid misusing AI for
  277. academic work.
  278. 564 | ICON-IMAD XV Proceedings · Volume IV · 2026
  279. International Conference on Islam in the Malay World (ICON-IMAD XV), 2026
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