The proliferation of automated writing feedback tools over the past decade has been remarkable. While artificial intelligence (AI) tools like ChatGPT have gained prominence, automated writing evaluation tools, like Criterion ® , widely used to support formative assessment of second language (L2) writing, also remain relevant in educational settings due to their alignment with curriculum objectives. Previous studies have predominantly focused on learners’ engagement with automated written corrective feedback (AWCF) on errors in grammar, usage, and mechanics, with fewer examining engagement with text-level feedback (style, organisation, and development). This study set out to address this gap by investigating L2 learners’ engagement with all feedback categories generated by Criterion ® (AWCF and text-level) and by comparing it to engagement with teacher feedback. Our 10-week study was conducted with 38 ESL and EFL learners in authentic classroom contexts. Adopting a mixed-methods approach, we analysed the first and revised drafts of three writing tasks, think-aloud protocols, and stimulated-recall interviews. The findings revealed comparable learner engagement with teacher and Criterion corrective feedback, but higher engagement with teacher text-level feedback, which was attributed to the higher level of trust in teacher feedback. These findings have implications for writing instruction, including how automated tools might be best integrated with teacher feedback in L2 writing classes to optimise the benefits of teacher and of automated feedback.
Zohali et al. (Sun,) studied this question.