Negotiating Meaning: The Interplay of Human Tutoring and AI Feedback in Chinese Writing Revision

Abstract

This presentation investigates the pedagogical interplay between generative AI and human tutoring within Chinese writing assignments. As automated tools proliferate in language education, understanding the negotiation between machine efficiency and interpersonal engagement is essential. Drawing on qualitative end-of-semester reflections, this study elucidates how students perceive and utilize the distinct affordances of AI-assisted revision and synchronous tutoring with native speakers.

Analysis reveals that learners adopt a functional approach to these resources, utilizing AI as a low-stakes environment for iterative linguistic experimentation. While AI provides immediate feedback on syntax and lexicon, its inherent inconsistencies often serve as a catalyst for metacognitive scrutiny, prompting students to critically evaluate alternatives rather than accepting corrections passively. In contrast, human tutors offer dimensions of relationality and cultural context that AI cannot simulate. These synchronous interactions foster affective security and facilitate the real-time negotiation of meaning, reframing communicative dissonance as a productive site of acquisition.

The study shows that students do not view these modalities as competitive but as a synergistic ecosystem addressing divergent cognitive and social dimensions of second-language development. The findings offer practical implications for the design of human-centered AI models, suggesting that intentionally structured hybrid frameworks can optimize learner agency and deepen proficiency in Chinese writing.

Presenters