The Clash Between AI-Driven Education and Traditional Learning in Bangladesh: A Mixed-Methods Study of Academic Integrity and Educational Security in Sherpur Sadar Upazila
Abstract
This study examines the clash between AI-driven and traditional education systems and its implications for inclusive educational outcomes in Bangladesh, using evidence from Sherpur Sadar Upazila. A convergent mixed-methods design was applied, integrating a student survey (N = 209) conducted across seven institutions with qualitative data collected from 32 stakeholders through semi-structured interviews and focus group discussions. The empirical findings capture a critical period of educational transition, revealing significant tension between emerging AI-based learning practices and established pedagogical frameworks. Notably, 85.7% of the sampled rural institutions, or six out of seven, lacked student-level internet access, indicating a major infrastructural barrier to inclusive AI integration. The findings suggest that while AI-driven tools offer potential for personalized learning, their implementation is constrained by structural inequalities, institutional resistance, and uneven digital access. Data collected between February and October, 2025, indicate that this clash is rooted in divergent perceptions of instructional authority, technological readiness, and digital equity. By synthesizing stakeholder perspectives with quantitative survey results, the study provides a localized yet analytically scalable account of how AI-driven disruption challenges traditional educational delivery in developing contexts. The study concludes that achieving inclusive educational outcomes requires not only technological adoption but also targeted infrastructural investment, institutional adaptation, and policy support.
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