Algorithmic Islam in Constructing Students’ Comprehension of Islamic History and Religious Identity in the Era of Generative AI
DOI:
https://doi.org/10.69552/mumtaz.v6i1.3874Keywords:
Algorithmic Islam, Generative AI, History of Islamic Culture, Religious Identity, Epistemic Authority, Interpretive AgencyAbstract
The findings show that while students utilize AI as an "instant tutor" to gain an initial overview of Islamic Cultural History, AI-generated answers also impose an algorithmic framing that tends to be succinct, neutral, moralistic, and occasionally insensitive to local contexts. Students negotiate these AI responses with instructors, Islamic texts, academic literature, and other sources, although some exhibit an epistemic dependence on answers that appear objective. This study proposes the concept of "algorithmic Islam" as a lens for understanding Islamic knowledge production, historical memory, and religious identity shaped by human-AI encounters. Consequently, the teaching of Islamic Cultural History must. The findings show that while students utilize AI as an "instant tutor" to gain an initial overview of Islamic Cultural History (ICH), AI-generated responses impose an algorithmic framing that tends to be succinct, neutral, moralistic, and occasionally insensitive to local subtleties. Students actively negotiate these AI outputs through cross-referencing them with lecturers, authoritative Islamic texts, academic literature, and other scholarly sources, although some exhibit an epistemic dependence on answers that project an impression of objectivity. Furthermore, this study proposes the concept of "algorithmic Islam" as a theoretical lens for understanding Islamic knowledge production, historical memory, and religious identity created through human-AI encounters. Consequently, the pedagogy of Islamic Cultural History has to critically integrate AI literacy, historical literacy, and religious literacy.
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Abid, A., Farooqi, M., & Zou, J. (2021). Persistent Anti-Muslim Bias in Large Language Models. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’21, 298–306. https://doi.org/10.1145/3461702.3462624
Amofa, B., Kamudyariwa, X. B., Fernandes, F. A. P., Osobajo, O. A., Jeremiah, F., & Oke, A. (2025). Navigating the Complexity of Generative Artificial Intelligence in Higher Education: A Systematic Literature Review. Education Sciences, 15(7), 826. https://doi.org/10.3390/educsci15070826
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
Bouncken, R. B., Czakon, W., & Schmitt, F. (2026). Purposeful sampling and saturation in qualitative research methodologies: Recommendations and review. Review of Managerial Science, 20(2), 579–615. https://doi.org/10.1007/s11846-025-00881-2
Cairns, R., & Garrard, K. A. (2024). ‘Learning from history is something that is important for the future’: Why Australian students think history matters. Policy Futures in Education, 22(3), 369–382. https://doi.org/10.1177/14782103231177615
Cotter, K., Ritchart, A., De, A., Foyle, K., Kanthawala, S., McAtee, H., & Watson, T. (2024). If you’re reading this, it’s meant for you: The reflexive ambivalence of algorithmic conspirituality. Convergence, 30(6), 1893–1918. https://doi.org/10.1177/13548565241258949
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
Kindenberg, B. (2024). ChatGPT-Generated and Student-Written Historical Narratives: A Comparative Analysis. Education Sciences, 14(5), 530. https://doi.org/10.3390/educsci14050530
Kizilcec, R. F, K., R. F. (n.d.). The life cycle of large language models in education: A framework for understanding sources of bias—Lee—2024—British Journal of Educational Technology—Wiley Online Library. Retrieved July 30, 2026, from https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13505
Lo, C. K. (2023). What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Mills, C., et al., Y., L. ,. Pammer-Schindler, V. ,. (n.d.). Beyond efficiency: Empirical insights on generative AI’s impact on cognition, metacognition and epistemic agency in learning. British Journal of Educational Technology. https://doi.org/https://doi.org/10.1111/bjet.70000
Rähme, B., & Prohl, I. (2025). Religious studies approaches to the intersection of artificial intelligence and religion: Formations analogous to religion. Religion, 55(3), 573–595. https://doi.org/10.1080/0048721X.2025.2506893
Tight, M. (2024). Saturation: An overworked and misunderstood concept? Qualitative Inquiry.
Tsai, C. C, W., J. Y. ,. Lee, Y. H. ,. Chai, C. S. ,. &. . (n.d.). Strengthening human epistemic agency in the symbiotic learning partnership with generative artificial intelligence. Educational Researcher. Tsai, C. C, 2025. https://doi.org/https://doi.org/10.3102/0013189X251333628
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