Dynamic Feedback Mechanisms in Technology-Enhanced Language Learning: Examining Learner Engagement and Second Language Writing Outcomes

This 8-week randomized controlled experiment tested the different effects of adaptive artificial intelligence-based feedback versus static rule-based feedback on learner engagement and the acquisition of second language writing in 216 undergraduate students of English as a foreign language. The respondents were randomly divided into a control group that used standard automated feedback and an artificial intelligence-adaptive feedback group in which the responses were customized according to individual proficiency levels and error patterns. Validated analytical rubrics (intraclass correlation coefficient = 0.89) were used to evaluate the writing quality, and the involvement was assessed through self-reports and system logs weekly. The multilevel analysis of the growth curve revealed that there were high treatment effects, in that the artificial intelligence-adaptive group was more responsive to writing improvements ( b = 0.42, p < .001), and maintained a high level of engagement in the study. The connection between the type of feedback and writing gains was partially mediated by cognitive and behavioral engagement, with 34% of the overall effect. The strength of the findings was ensured by using propensity score matching after removing baseline differences. The results indicate that more dynamic adaptive artificial intelligence feedback systems that dynamically adjust to the requirements of the learner are more interesting and bring deeper development in writing than non-adaptive automated systems do. The technological implications of language teaching and the future of intelligent tutoring systems for language teaching writing in languages other than English are discussed in this paper.