AI-Assisted Feedback in Online Learning: Students’ Experiences, Preferences, and Perceived Benefits - Adyt Anugrah; Yani Suryani (CV. FoundAE, 2025)
This study examined how students experience and interpret AI-assisted feedback in online learning, addressing the growing need to understand its cognitive, emotional, and developmental implications. Using a convergent mixed-methods design, data were collected from 212 undergraduate students through a structured questionnaire including Likert-scale items and open-ended responses. Quantitative analyses provided descriptive and inferential results on students’ experiences, preferences, and perceived benefits, while qualitative thematic analysis identified patterns related to clarity, explanatory value, confidence building, and concerns about accuracy. Integrated findings showed strong convergence across strands, indicating that students generally valued AI feedback for its immediacy and usefulness, yet remained cautious about its limitations. The study concludes that AI-assisted feedback can support learning processes when designed to provide explanatory depth and align with instructional expectations. These insights contribute to research on AI-enhanced education by clarifying how learners engage with automated feedback and by highlighting design considerations for future implementation.