Facilitator or thinking inhibitor: understanding the role of ChatGPT-generated written corrective feedback in language learning
Xiaoshuang Guo
Abstract
Written corrective feedback (WCF) is crucial in foreign language learning, helping learners identify and rectify writing errors. However, manual WCF can be time-consuming for educators. Artificial general intelligence (AGI), exemplified by ChatGPT, offers a promising solution via automated WCF, addressing learners’ intricate linguistic demands. While effective in English learning, the suitability of AGI-powered automated WCF for other languages with complex grammar remains uncertain due to the English-centric training of many large language models. This study examines the reliability and acceptance of ChatGPT-generated WCF within a German as a foreign language (GFL) context. Through a 200-essay analysis, we explore two questions: (1) How does ChatGPT-generated automated WCF compare to teacher-provided WCF? (2) How do GFL learners respond and interact with WCF from both sources? Our findings revealed different behavioral, cognitive, and affective engagements between ChatGPT-provided WCF and teacher-provided WCF. ChatGPT has greater potential for providing diverse lexical, grammatical, structural, and content feedback, facilitating GFL learning. However, students’ emotional perceptions of ChatGPT-generated WCF vary, sometimes even negative, hindering language learning. We further explored the advantages and challenges of integrating ChatGPT-generated WCF into foreign language learning. Our findings underscore the potential of integrating AI-powered automated WCF into foreign language learning and writing development.