Exploring the Young Learners’ Interactions with AI-generated Multimodal Feedback in Collaborative Writing

Abstract

The rapid transformation of education by artificial intelligence (Al) has emerged as a pivotal solution to language learning. Recently, Al-enabled automated feedback learning systems signify a growing effort to support the teaching and learning in Chinese language. However, it can be found that recent scholars have primarily focused on the utilization of automated feedback for individual self-regulated learning and the application of unimodal feedback to augment students' learning outcomes. There is limited research on how Al-generated multimodal feedback can promote meaningful learning process and engaging students within collaborative language learning environments. Consequently, this research proposes employing a mixed methods approach to investigate how Al-generated multimodal feedback (comprising Al-generated image feedback, Al-enabled audio feedback, and automatic scoring) can promote vocabulary learning for young learners in collaborative Chinese language learning activities. This investigation will focus not only on students' learning outcomes but also on the learning process and learner enjoyment, as a way to explore the multiple dimensions of feedback in learning. This research seeks to gain insights into effective pedagogical strategies and their implications for Al-generated multimodal feedback in collaborative language learning.