Vol 6 No 2 (2026)

Generative artificial intelligence (AI) is changing teaching, learning, evaluation, and feedback methods in educational settings. An increasing body of research indicates that while AI-supported technologies improve efficiency, customisation, and access to learning resources, they may also reduce evidence of meaningful learning while simultaneously improving observable academic performance. Using interrelated pedagogical, cognitive, ethical, assessment, and policy viewpoints, this study critically investigates AI in education. The study provides an integrative understanding of AI integration in education by synthesizing literature from empirical investigations (n = 21), conceptual papers (n = 19), policy reports (n = 5), and one review study using a critical narrative review design. The results show that although AI improves feedback, student engagement, instructional support, and chances for adaptive learning, it also creates issues with cognitive dependence, academic integrity, algorithmic bias, data privacy, inequality, and assessment validity. The review's main conclusion is that there is a growing conflict between meaningful learning and visible academic achievement since AI-mediated outputs might not always demonstrate prolonged cognitive engagement or conceptual grasp. The study contends that pedagogical design, teacher involvement, AI literacy, institutional governance, and technological competence all play a role in the educational usefulness of AI. The paper emphasizes the necessity of learning and assessment systems that support responsible, egalitarian, and learner-centered AI integration in educational settings while preserving learner agency and making thinking visible.