The human touch in AI: optimizing language learning through self-determination theory and teacher scaffolding - Yi Ma; Mingyang Chen (Frontiers in Psychology)
IntroductionArtificial intelligence (AI) is transforming language education, yet its long-term impact on motivation and proficiency, particularly how AI-driven gamification and teacher scaffolding interact in culturally distinct EFL contexts, remains underexplored. This study investigates the sustained influence of AI-powered language games on Chinese EFL learners’ motivation, engagement, and English proficiency.MethodsThis mixed-methods, longitudinal quasi-experimental study involved 150 intermediate Chinese EFL learners across three universities. Participants were stratified into three groups: AI with teacher scaffolding, AI only, and a control group using non-AI gamified platforms. Over 16 weeks, we collected quantitative data (IELTS Indicator tests, motivation and technology acceptance surveys) and qualitative data (interviews, observations, and reflective journals).ResultsQuantitative analyses revealed that the AI with Scaffolding group achieved significantly greater and more sustained proficiency gains than both the AI Only and Control groups. Motivation was significantly mediated by the satisfaction of Self-Determination Theory needs. Qualitative findings highlighted teacher scaffolding’s pivotal role in contextualizing AI feedback, mitigating algorithmic rigidity, and fostering a “novice-to-self-regulated learner” trajectory. Cultural factors significantly influenced technology acceptance.DiscussionFindings underscore that AI’s potential in language learning is maximized when strategically integrated with human pedagogical expertise, which addresses AI’s limitations related to cultural nuances, overcorrection, and trust. This study offers concrete practical implications for educators and institutions, advocating for a balanced, human-centered approach to AI integration in diverse EFL contexts.