Intrinsic and Extrinsic Motivation in AI-Supported EFL Learning

AI-assisted language-learning tools are shaping learners' experiences of motivation when they complete English-based coursework. Using a mixed-methods approach, this study investigated how 120 undergraduates at an English-medium private university in the United Arab Emirates perceived intrinsic and extrinsic motivation while using AI-supported drafting, revision, oral rehearsal, and tone-development tools. Survey results indicated associations between motivational orientations, patterns of AI use, perceived feedback usefulness, and gamified elements. Semi-structured interviews with 28 students explored autonomy, competence, relatedness, flow, pressure, and self-regulation. Quantitative results showed positive relationships between intrinsic motivation, perceived usefulness of feedback and chatbot use. Results also indicated positive relations between extrinsic motivation, perception of gamification, and streak length. Analysis of the interview data showed that most students considered AI-generated feedback as competency nurturing; however, some also described feeling pressure with regard to grades, family expectation, employability, and visual performance metrics. Students described strategies for retaining control over their learning, including taking breaks from metrics, reframing goals, and seeking teacher guidance on task criteria. Based on these findings, the authors propose the AI-Mediated Motivational Balance (AIMMB) Framework as a teacher-facing model for designing EFL courses that balance useful feedback, autonomy support, and carefully constrained rewards in AI-assisted task completion. Received: 23 February 2026 / Accepted: 24 June 2026 / Published: July 2026

Autonomy · Cognitive reframing · Feeling · Intrinsic motivation · Perception · AI in Service Interactions · Educational Games and Gamification · Technology-Enhanced Education Studies