Automated Diagnostic Feedback vs. Self-Assessment: Rethinking Feedback Mechanisms on Academic Writing Development

Abstract

As international students increasingly pursue higher education in English-dominant countries, developing their academic writing skills is crucial. However, limited access to individualized feedback remains a challenge. AI-driven tools and self-assessment offer promising solutions, making feedback more accessible. This study involved 50 international graduate students who spoke English as an additional language, randomly assigned to two groups: one received BERT-generated automated diagnostic scores and feedback, while the other engaged in self-assessment. Using a sequential explanatory mixed-methods design, this study investigated the effects of automated diagnostic feedback and self-assessment on students' academic writing performance and self-perceived writing abilities. It also explored how cognitive, metacognitive, behavioral, and affective engagement with feedback varied across the two groups. Results indicated that the machine feedback group significantly outperformed the self-assessment group in task fulfillment, organization, and total writing scores, while no significant differences were observed for vocabulary and grammar. Additionally, the machine feedback group demonstrated deeper metacognitive, behavioral, and affective feedback engagement. However, they also reported a decline in academic writing self-confidence. While automated diagnostic feedback proved more effective than self-assessment in enhancing academic writing skills, its potential negative impact on students' confidence highlighted the need for future research to balance precision with emotional support.