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Integrating Heutagogy and Generative AI in Asynchronous Learning: Personalizing Self-determined Learning for Lifelong Learners
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Abstract
This study explores the transformative potential of integrating heutagogy (self-determined learning) and generative AI in asynchronous learning environments, proposing a novel framework that combines selfdetermined learning principles with cutting-edge AI technologies. Grounded in Self-Determination Theory, heutagogy fosters learner autonomy, critical thinking, and self-directed education; however, its application in asynchronous settings remains inconsistent. Generative AI enhances this approach by providing real-time adaptability, personalized learning paths, and tailored feedback, addressing gaps in traditional asynchronous education. Despite its potential, a significant gap exists in systematically integrating these two approaches to optimize learning outcomes. Using a qualitative research design, this study conducted semi-structured interviews with ten educators experienced in asynchronous learning and heutagogical practices. Thematic analysis uncovered three key themes: positive reception of heutagogy and generative AI, challenges in implementation, and opportunities for enhanced personalization. Findings indicate that the integration of these approaches fosters learner engagement, autonomy, and personalization but also highlights barriers such as content alignment, educator readiness, and equitable access to AI technologies. These challenges underscore the need for robust institutional support and ethical frameworks. This study provides actionable insights for stakeholders to create scalable, inclusive, and learner-centred educational models that align with UNESCO’s Education 2030 Framework, promoting lifelong learning and equitable quality education.
PCF11 Plus: Beyond the Forum
Sub-Theme: Skills development through lifelong open education
Paper ID: 0668
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2025-09
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Commonwealth of Learning (COL)