Religion teachers welcome AI as a helper but insist it cannot replace the sacred

When a chatbot was asked to simulate a conversation with St. Basil the Great and then critique its own output, the answer it produced became a teaching tool in its own right. The exchange, reproduced in a Greek teacher-training module on artificial intelligence, acknowledged that the machine could not convey the spiritual depth of the saint, risked creating an “illusion of authentic encounter,” and could never replace the study of the original patristic texts. That case study now sits at the center of the first empirical look at how religious education teachers in an Orthodox national context perceive AI — and the teachers’ answers, gathered in a new survey, echo it with striking consistency.

The study, published in the Journal of Religious Education, reports survey data from 54 in-service religious education teachers in Greece who completed a national professional development program known as SUB8. Run by the Institute of Educational Policy under the country’s recovery plan and co-funded by the European Union, the program trained a slice of the country’s roughly 2,700 theology-graduate RE teachers in the use of AI and digital tools, with a dedicated session on large language models such as ChatGPT. The 54 respondents represent about 19 percent of the 284 RE teachers who took the module nationally, and they answered a questionnaire covering AI literacy, digital self-efficacy, technological pedagogical content knowledge, classroom use of AI, pedagogical change, theological concerns, and professional collaboration.

The headline numbers describe a cautiously positive cohort. Self-reported AI literacy averaged 3.91 on a five-point scale, digital self-efficacy 3.82, and TPACK — the integrated measure of technological, pedagogical, and content knowledge — 4.09, the latter anchored by strong scores for selecting tools on the basis of lesson content and for designing AI-assisted micro-teaching units. Reported classroom use of AI averaged 3.84 but showed the widest spread of any section, with differentiation and material production at the top and AI-assisted assessment at the bottom, a pattern the author reads as a cohort spanning eager early adopters and more hesitant practitioners. Notably, the literacy items were bimodal: a confident majority clustered at the high end while a distinct minority sat two points down the scale, even inside a self-selected, freshly trained group.

The most distinctive section, however, was the one no standard AI-in-education instrument contains: theological concerns. The strongest consensus in the entire questionnaire outside the collaboration items went to the statement that AI requires critical pedagogical management, endorsed at 4 or 5 by 46 of the 54 teachers — 85 percent — for a mean of 4.35. Agreement was also high that AI fails to capture the depth of religious experience, while the claim that AI necessarily produces shallow learning fell just below the scale midpoint, the only item in the instrument to do so. In other words, these teachers worry about AI’s epistemological reach without conceding that it automatically cheapens what students learn.

The open-ended answers sharpen that picture into three recurring themes. The first is practical: teachers repeatedly credited AI with cutting preparation time, improving lesson quality, and enabling genuine differentiation for students of varying needs and backgrounds. The second is a mix of the epistemic and the mundane — concerns about accuracy, reliability, and the “lack of understanding of depth” in AI outputs, alongside absent interactive whiteboards, unreliable school internet, and tools that exist almost exclusively in English. The third theme, and the most consistent across responses, is a firm professional boundary: AI must be a supplementary tool, subordinate to the teacher’s judgment and to the relational, experiential, and theological dimensions of the subject. One respondent put it plainly: AI “does not replace the role of the teacher, nor the word of God.”

Why religious education poses a qualitatively different AI problem than algebra or geography is the study’s conceptual core. Greek RE is a constitutionally anchored subject taught by university-trained theologians, and its curricula since 2016 have shifted from catechetical instruction toward learner-centered religious literacy. Within the Orthodox framing the study draws on, knowledge of God is understood as personal, relational, and experiential — classically expressed in the patristic distinction between nous, the noetic faculty of direct spiritual apprehension, and dianoia, discursive reason. AI systems operate entirely within the register of dianoia. A language model can simulate a dialogue with a saint, the training material argues, but it cannot embody the uniqueness of a person on a spiritual path, which is precisely why its output in this domain risks simulating encounter without delivering it.

The theoretical scaffolding comes from Shulman’s pedagogical content knowledge, extended through Mishra and Koehler’s TPACK framework and its recent AI-informed variants, which add ethical awareness and critical judgment about algorithmic systems to the classic triad. The survey’s own numbers illustrate where the seams are: teachers scored highest on integrating theological content into digital environments and lowest on designing student-centered activities with AI, and their confidence in using technology far outstripped their confidence in resolving technical problems, which at 3.24 was the weakest single item in the instrument. That gap between fluent use and critical, student-centered deployment mirrors a broader finding in the literature that teachers’ ethical and evaluative AI knowledge lags behind their technical familiarity.

Two findings stand out for anyone designing teacher training. The first is the near-unanimous endorsement of professional development itself — a mean of 4.89 for the statement that training is essential, with 50 of 54 respondents giving it the top score, and 4.81 for the value of exchanging good practices. Even teachers with more than two decades of classroom experience endorsed structured support as strongly as their younger colleagues. The second is subtler: the teachers’ theological framing of AI’s limits converges almost verbatim with the framing the SUB8 material itself taught, including the St. Basil chatbot exercise. The author is careful about what that convergence means. Because the survey was administered in the weeks immediately after training, it cannot show whether these convictions arise independently; the defensible reading is that an explicitly theological account of AI’s limits can be deliberately cultivated through professional development, and that participants internalized and reproduced it.

That caution is one of several the study applies to itself, and they matter for how the results should be quoted. The sample is small and self-selected — teachers who voluntarily enrolled in an AI-focused course are plausibly more digitally engaged and more favorably disposed than the roughly 2,700 Greek RE teachers as a whole. All data are self-reported and unverified by classroom observation, and the timing of measurement, at peak post-training enthusiasm, may inflate the competence scores. The study therefore claims to describe this specific professional development context, not Greek religious education teachers in general, and it makes no causal claims; it is a descriptive survey, not an experiment.

What the study can legitimately claim is a place in a very small comparative literature. The only prior empirical work on AI and religious education surveyed teachers in Germany and Poland, finding a similar ambivalence: appreciation of AI’s affordances for preparation and differentiation paired with insistence on the teacher’s irreplaceability. The recurrence of that conditional-acceptance profile across Catholic, Protestant, and now Orthodox settings hints — on still thin and methodologically heterogeneous evidence — that it may be a transnational feature of RE professionals rather than a local artifact, a hypothesis the author says would need harmonized comparative instruments to test properly. The practical upshot for program designers is concrete: combine technical skills with explicit attention to AI’s epistemological limits in spiritually sensitive subjects, build sustained collegial exchange into the program, and address the infrastructure and language barriers that even an enthusiastic cohort still reports. The deeper question the study leaves open is whether the teachers’ principled stance — AI as tool, never as encounter — would hold among colleagues who never sat in the training room, and whether it survives contact with the classroom once the post-training glow fades.

Subject of Research: Teacher perceptions of artificial intelligence in Greek religious education

Article Title: Artificial intelligence in religious education: teacher perspectives from a national professional development context

Article References: Komninou, I. (2026). Artificial intelligence in religious education: teacher perspectives from a national professional development context. Journal of Religious Education. https://doi.org/10.1007/s40839-026-00308-4

Image Credits: AI Generated

DOI: 10.1007/s40839-026-00308-4

Keywords: artificial intelligence, religious education, teacher survey, TPACK, professional development, Greece, Orthodox education, AI literacy, theology of education, Artificial, intelligence, religious