Rethinking Language Instruction: Integrating AI Feedback to Develop Critical Writing Skills

Abstract

This study aimed (1) to develop a task-based language teaching model integrated with generative AI-powered feedback to enhance students’ critical writing skills and learning engagement, and (2) to examine the effects of implementing the developed model. Employing a research and development design, the model was constructed through a synthesis of relevant literature and validated by five experts. The developed model was then implemented with 25 Grade 11 students over a seven-week instructional period. Research instruments included an expert interview form, an instructional model evaluation form, lesson plans based on the developed model, and a performance-based critical writing assessment. Quantitative data were analyzed using descriptive statistics (mean and standard deviation) and repeated-measures analysis of variance, followed by post-hoc comparisons. The results indicated that the instructional model was evaluated by experts as highly appropriate and pedagogically coherent. Moreover, students’ critical writing skills showed statistically significant improvement across the intervention period, accompanied by increased levels of learning engagement. The findings suggest that integrating task-based instruction with generative AI-powered feedback and engagement-oriented design can effectively enhance critical writing development and learner engagement in secondary language education.