📄 Research Article
Instructor-Designed AI Tutors in University Foreign Language Education: A Mixed-Methods Study of Learner Motivation and Reflective Learning Experience Based on Self-Determination Theory
Synthesis: Lee and Kwon (2026) report a mixed-methods study of an instructor-designed, customized Japanese-language GPT tutor in a South Korean university general-education course, drawing on self-determination theory (SDT) and the noticing hypothesis. With 74 undergraduates, they find that continuous use of the instructor-built AI tutor was associated with high satisfaction of autonomy and relatedness needs, three distinct cognitive-noticing experiences, and learner perceptions of the tool as a structured learning environment rather than a convenience tool. The central claim is that the educational effectiveness of generative AI in foreign language learning depends less on frequency of use than on the quality of pedagogical design underlying its deployment.
Key Findings
- Autonomy-supportive design. Learners reported high perceived helpfulness for self-directed learning (M = 4.39, SD = 0.70) and a positive change in learning motivation (M = 3.95). Autonomy was realized within an instructor-designed system aligned to course objectives, not through general-purpose ChatGPT use alone.
- Production-oriented competence gains. Competence satisfaction was comparatively lower overall (early-stage learners did not yet perceive performance gains), but domains tied to production — writing, vocabulary, sentence generation — showed relatively higher means, and autonomy perceptions correlated most strongly with production-oriented activities.
- Relatedness through affective safety. Immediate feedback (78.4%) and affective safety (60.8%) were the most-endorsed relatedness indicators. The instructor-designed GPTs functioned as nonjudgmental, immediately responsive practice spaces for learners anxious about making errors in class.
- Three noticing experiences (RQ2). Qualitative analysis identified (1) AI-supported clarification of linguistic form, (2) noticing through intentional error generation plus AI feedback, and (3) metacognitive regulation of learning strategies.
- Design over frequency. Out-of-class usage frequency showed no significant correlation with SDT variables — how the tool is used and under what design conditions matters more than how often.
- Reconfigured learning mode (RQ3). Learners shifted from passive memorization/answer-confirmation toward active, interaction-based, output-oriented learning, and attributed the psychological safety they felt to the instructor's pedagogical and relational design (e.g., one learner contrasted "a rational type" general GPT with the instructor's "very bright" customized GPT).
Design Implications
- Instructor-designed AI tutors (customized GPTs scoped to course objectives, learner proficiency, and a curated knowledge base) can structure AI-mediated practice more consistently than unstructured general-purpose chatbot use.
- Intentional error generation + AI feedback (a three-stage sequence of knowledge activation, deliberate error production, and context-based sentence production) operationalizes the noticing hypothesis in AI-mediated tasks.
- Affective safety is a product of instructional design, not of the technology itself — nonjudgmental, immediately responsive interaction supports relatedness for error-anxious learners.
- Complementarity over substitution: AI tutors work best alongside existing methods; learners noted usage limits and individual learning-style preferences (e.g., handwriting) as constraints.
constraints.
Connected Concepts
- Language Learning
- Self Regulated Learning
- Generative AI
- Intelligent Tutoring
- Motivation
- Agency
- Metacognition
- Personalized Learning
- Feedback
- Student Experience
- Higher Ed
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Citation
Lee, H., & Kwon, H. (2026). Instructor-Designed AI Tutors in University Foreign Language Education: A Mixed-Methods Study of Learner Motivation and Reflective Learning Experience Based on Self-Determination Theory. Trends in Higher Education, 5(3), 78.