Converging heutagogy, M-learning, and AI for sustainable language education: A fuzzy Delphi study - Kamrozzaman, Nurul Aisyah (Edelweiss Applied Science and Technology, 2025)
This study aimed to develop an expert-validated framework that integrates heutagogy, mobile learning (m-learning), and Artificial Intelligence to create sustainable and future-proof language education. Addressing a significant gap in the existing literature, this research provides the first expert-validated framework detailing how AI functionalities can strategically support each core process of self-determined language learning within a mobile context. The Fuzzy Delphi Method was employed, seeking consensus from a panel of 15 experts in language education, educational technology, and AI design. A three-round iterative process using a 5-point linguistic scale, converted to triangular fuzzy numbers, was used to evaluate proposed framework elements, achieving a consensus threshold of d≤0.2 and a 75% agreement rate. Findings confirmed strong expert consensus on all core elements, with AI-powered personalization (0.88) identified as crucial for facilitating heutagogy’s explore, create, and reflect cycles. Smartphones (0.89) were reaffirmed as the primary m-learning device. Significantly, AI-facilitated peer matching and recommendation systems for "Share" (0.92) received the highest ranking, underscoring AI’s value in enhancing human collaboration. The study concludes that this synergistic convergence transforms language education by promoting learner autonomy and equitable access, directly contributing to Sustainable Development Goal 4. The resulting framework provides a practical roadmap for educators and policymakers.