Abstract

The rapid advancement of AI-based writing assistants has transformed language learning, yet gaps remain in understanding how learners interact with these tools and perceive their feedback. This mixed-methods study explores the dynamics between English as a Foreign Language (EFL) learners and an AI writing assistant (Type), focusing on interaction patterns, prompt types, and learner perceptions. Data was collected from 27 Saudi male university students who used Type to complete writing tasks, with their interactions logged and analyzed. Pre- and post-surveys assessed their experiences and attitudes toward AI-assisted writing. Findings reveal how learners engage with AI-generated feedback, the nature of their prompts, and their overall perceptions of AI tools in writing development. The study contributes to the literature on AI in education by examining the intersection of automated feedback, learner motivation, and instructional design. Results suggest implications for language educators in integrating AI tools effectively and highlight areas for developers to enhance AI writing assistants. By bridging theoretical frameworks such as the Community of Inquiry (CoI) and Students’ Approaches to Learning (SAL), this research provides insights into optimizing AI’s role in language education while addressing limitations such as over-reliance and feedback quality.

This work is licensed under a Creative Commons Attribution 4.0 License.