Speaker
Description
Sponsored by Nagoya University Graduate Student Showcase: This study proposes and validates an AI-driven approach to interactional competence (IC) pedagogy that integrates practice and feedback. AI serves as an interlocutor during interaction tasks and provides immediate learning-oriented diagnostic feedback on learners’ strengths and weaknesses with actionable suggestions. After configuring AI through prompt engineering, ten learners complete the same role-play dyadic tasks with AI and a trained human teacher, then receive feedback from them.
Abstract section 1: Relevance
Although IC is central to face-to-face communication, its systematic integration into foreign language pedagogy remains limited, particularly in terms of how learners can practice IC and receive feedback in instructional contexts (May et al., 2020).
Abstract section 2: Contribution/research questions
This study aims to propose and validate an AI-driven approach to IC learning that integrates interaction practice and feedback. This approach is validated by comparing their task-performance measures quantitatively and learners’ perceptions of feedback efficacy qualitatively.
Abstract section 3: Content/method
AI was trained through text prompts, and a human teacher was trained to follow the same interaction and feedback framework. Ten learners will complete the same tasks with both and receive feedback from each.
Abstract section 4: Outcomes/results
The expected outcomes include: (a) a detailed IC feedback framework with learner-oriented descriptors, and (b) AI prompt templates for interaction practice and feedback provision.
| Title | Validating AI-Driven Voice Chatbot |
|---|---|
| Teaching Context | General |