From Automation to Educational Value: A Conceptual Framework for Human–AI Co-Learning in Education
Abstract
The rapid expansion of artificial intelligence (AI) in education has increased the availability of personalized learning, intelligent tutoring, generative feedback, and automated instructional support. However, technological capability does not necessarily translate into educational value. This conceptual study addresses the question: Under what pedagogical, human, and institutional conditions does AI capability translate into educational value? A critical conceptual synthesis of research on AI-supported personalization, intelligent tutoring, generative AI, cognitive engagement, learner and teacher agency, reliability, equity, and learning authenticity was undertaken. The analysis distinguishes AI capability from educational value and identifies five dimensions through which AI-supported education can be evaluated: personalization, reliability, human agency, equity, and learning authenticity. Based on these dimensions, the study develops a Human–AI Co-Learning Framework (HACLF) comprising six interconnected layers- learner, AI assistant, teacher, learning environment, governance, and society- and proposes the AI Education Value Index (AEVI) as a conceptual evaluation framework. The conceptual synthesis indicates that the educational consequences of AI are conditional on how technological capabilities are pedagogically integrated and mediated by human judgment, verification practices, equitable access, and authentic learner participation. The study contributes an education-centred perspective that shifts evaluation from what AI can automate to the conditions under which AI can support meaningful learning. HACLF and AEVI provide a basis for future empirical research and validation of AI-supported educational practices.
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