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The human touch in AI: optimizing language learning through self-determination theory and teacher scaffolding
1School of Humanities, Foshan University, Foshan, China.
Frontiers in Psychology
|July 18, 2025
Summary
Artificial intelligence (AI) in language learning shows best results when combined with teacher support, enhancing motivation and English proficiency for Chinese learners. This human-centered approach optimizes AI
Area of Science:
- Educational Technology
- Applied Linguistics
- Artificial Intelligence in Education
Background:
- Artificial intelligence (AI) is increasingly used in language education, but its long-term effects on motivation and proficiency, especially concerning AI gamification and teacher scaffolding in diverse cultural contexts, are not well understood.
- The interaction between AI-driven gamification and teacher scaffolding in English as a Foreign Language (EFL) settings requires further investigation to understand its impact on learner engagement and outcomes.
Purpose of the Study:
- To investigate the sustained influence of AI-powered language games on the motivation, engagement, and English proficiency of Chinese EFL learners.
- To examine the interplay between AI gamification, teacher scaffolding, and cultural factors in an EFL context.
Main Methods:
- A 16-week mixed-methods, longitudinal quasi-experimental study involving 150 intermediate Chinese EFL learners.
- Participants were divided into three groups: AI with teacher scaffolding, AI only, and a control group using non-AI gamified platforms.
- Data collection included IELTS Indicator tests, motivation and technology acceptance surveys, interviews, observations, and reflective journals.
Main Results:
- The AI with Scaffolding group demonstrated significantly greater and more sustained improvements in English proficiency compared to the AI Only and Control groups.
- Learner motivation was significantly mediated by the satisfaction of Self-Determination Theory needs.
- Qualitative data revealed that teacher scaffolding was crucial for contextualizing AI feedback, mitigating algorithmic limitations, and promoting self-regulated learning, with cultural factors influencing technology acceptance.
Conclusions:
- Integrating AI with human pedagogical expertise is essential to maximize its potential in language learning, addressing limitations such as cultural insensitivity and trust issues.
- A balanced, human-centered approach to AI integration is recommended for diverse EFL contexts to foster learner motivation and proficiency effectively.
- Teacher scaffolding plays a vital role in guiding learners from novice to self-regulated levels when using AI tools.
Keywords:
artificial intelligencegamificationlanguage educationlanguage proficiencymotivationself-determination theoryteacher scaffoldingMore Related Videos
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