Automated written corrective feedback and ARCS-V model for enhancing writing and self-efficacy of EFL learners

Abstract

Grounded in the reinforcement principle of behaviorism and Keller's ARCS-V (Attention, Relevance, Confidence, Satisfaction, and Volition) motivational model, this study explored the effects of Grammarly as an Automated Written Corrective Feedback (AWCF) gadget, combined with teacher feedback and the ARCS-V motivational model, on enhancing the writing performance and self-efficacy of Saudi EFL university students. The study implemented a quasi-experimental design with equivalent groups. The participants included 84 first-year Saudi female EFL students at King Khalid University, divided into an experimental group (43 students) that was exposed to both Grammarly and teacher feedback via the ARCS-V model, and a control group (41 students) that received only teacher feedback. To collect data, pre- and post-writing tests evaluating six writing subskills and a self-efficacy scale covering four domains were utilized. Quantitative data were analyzed using independent and paired-samples t-tests. Results demonstrated that the experimental group significantly outperformed the control group in both writing skills and all four self-efficacy domains, reflecting the positive effects of integrating AWCF with teacher feedback via the strategic application of the ARCS-V model. It has been noted that Grammarly is effective at providing feedback on grammar, punctuation, spelling, and vocabulary. However, in some contexts, it could not provide relevant explanations on overall coherence and organization. Therefore, the teacher's feedback is crucial. This study recommends integrating AI tools, human feedback, and motivation models for writing instruction.