AI-enhanced feedback and its effects on student motivation, self-regulation, and learning outcomes in higher education
Bibliographic Data
| ID | 22420082 |
|---|---|
| Authors | Man Zhai (Jilin Business and Technology College), Muhammad Farhan (0009-0008-7295-0204, Department of Applied Psychology, Bahauddin Zakariya University Multan, corresponding author) |
| Year | 2026 |
| Pages | 1-21 |
| Publication date | 2026-07-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2026.2687874 |
| OpenAlex | W7167907488 |
| Language | EN |
| References cited | 37 |
The growing use of artificial intelligence (AI) in higher education has renewed interest in AI-enhanced feedback as a tool to support student motivation, self-regulated learning (SRL), and academic performance. This study reports a convergent mixed-methods investigation comparing AI-assisted formative feedback with conventional instructor feedback in undergraduate courses. Using a randomized two-group pretest-posttest design, 156 students participated over a 12-week academic term. Quantitative data included validated measures of academic motivation, SRL, engagement, academic performance, and computational analyses of academic writing quality using Coh-Metrix indices of syntactic complexity, lexical diversity, and textual cohesion. Qualitative data were collected through interviews, reflective journals, and think-aloud protocols. Compared with the control group, students receiving AI-enhanced feedback showed greater increases in intrinsic motivation, self-efficacy, metacognitive self-regulation, and academic performance. Textual analyses indicated larger improvements in writing quality in the AI condition, suggesting substantive development rather than surface-level revision. A parsimonious structural equation model indicated that the experimental feedback condition was associated with perceived feedback quality, motivation, and SRL, which functioned as complementary post-test process indicators linked to learning outcomes. These associations are considered process-consistent primarily given parallel measurement rather than causal mediation. Drawing from the quantitative results, the qualitative feedback captured the immediacy, specificity, and non-judgmental character of AI, while also reporting a wide range in student engagement and expressing concerns about possible AI overuse. Overall, the findings demonstrate that, with thoughtful pedagogical and metacognitive framings, AI-enhanced feedback can bolster students’ feedback motivation, self-regulation, and academic writing performance.
Academic achievement · Educational technology · Electronic learning · Experiential learning · Higher education · Learning analytics · Student engagement · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Technology-Enhanced Education Studies
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| Highly cited | No |
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