Digital Pedagogy for Religious Cultural Heritage Preservation Using AI Tools

Thesis Abstract

The rapid digitization of religious cultural heritage (RCH) and the parallel rise of artificial intelligence (AI) present both opportunities and challenges for pedagogy in preservation and public engagement. This study addresses the gap between AI-enabled preservation tools and effective pedagogical strategies that cultivate critical understanding, ethical engagement, and participatory learning among diverse stakeholders, including students, religious communities, conservators, and educators. The aim is to develop and evaluate a technology-mediated pedagogical framework that leverages AI for RCH preservation while aligning with intercultural, ethical, and doctrinal considerations. Specific objectives are (1) to identify AI-based pedagogical interventions that enhance RCH literacy and preservation skills among postgraduate students; (2) to assess how AI-driven tools (e.g., computer vision for artifact cataloguing, natural language processing for digitized scriptures, and generative models for interactive exhibits) influence learner engagement, empathy, and critical reflection; (3) to examine ethical, theological, and cultural implications of AI in sacred contexts; (4) to assemble a validated pedagogical model integrating AI workflows with inquiry-based and collaborative learning approaches; and (5) to generate recommendations for curriculum design and policy guidelines for institutions engaged in RCH preservation. The study adopts a mixed-methods design anchored in the Communities of Practice theory and the Technological Pedagogical Content Knowledge (TPACK) framework to explore how AI tools mediate knowledge formation in RCH contexts. A sequential explanatory approach comprises two phases a quantitative phase with 180 postgraduate learners across three universities, using pre- and post-tests, engagement scales, and ethical attitudes inventories; and a qualitative phase involving 36 semi-structured interviews and 12 focus groups with educators, curators, and religious scholars to elucidate contextual dynamics. Data collection employs standardized instruments, classroom observations, artifact collections from AI-enabled labs, and platform analytics from an AI-assisted learning environment. Validity and reliability are ensured through pilot testing (n=30), back-translation of instruments where applicable, triangulation across data sources, and inter-rater reliability checks (Cohen’s kappa > 0.80) for coding. Quantitative data will be analyzed using multiple regression to identify predictors of learning outcomes and structural equation modeling to test the proposed pedagogical model. Qualitative data will undergo thematic analysis guided by Braun and Clarke, complemented by narrative inquiry to capture stakeholder experiences, with triangulation to corroborate findings. The expected results indicate that AI-assisted pedagogy enhances RCH literacy, interdisciplinary collaboration, and critical reflection when embedded within structured inquiry-based activities, with differential effects moderated by prior domain knowledge and cultural context. The study anticipates that computer vision-enabled cataloguing, NLP-driven search and translation of sacred texts, and adaptive feedback mechanisms will correlate with higher engagement scores, improved preservation competencies, and more robust ethical reasoning among learners. The contribution to knowledge lies in (a) articulating a comprehensive, empirically validated AI-enhanced pedagogy for RCH preservation compatible with theological sensitivities, (b) delineating a scalable framework that integrates AI workflows into postgraduate curricula and field-based learning, and (c) offering policy recommendations for governance, data stewardship, and community co-creation in AI-enabled RCH pedagogy. The study concludes that responsible AI integration, underpinned by intercultural ethics, participatory design, and transparent data practices, can transform preservation pedagogy and broader public understanding of religious cultural heritage. Practical recommendations include the adoption of modular AI curricula, guidelines for ethical AI in sacred contexts, professional development programs for educators, and a framework for ongoing assessment of pedagogical efficacy across diverse cultural settings. Long-term implications suggest that universities and religious institutions collaborate to establish shared repositories, governance models, and community-engaged learning laboratories to sustain AI-driven RCH education beyond the duration of the study.