Artificial Intelligence for Providing Feedback in the Graduate Online Classroom: Opportunities and Challenges

This study investigates the use of generative artificial intelligence in providing feedback within a graduate-level online course on instructional design, with a specific focus on ChatGPT. The overarching question guiding this study is: “How do online graduate education students perceive the learning experiences, benefits, and challenges associated with AI-generated feedback compared to professor feedback, and how does AI support understanding, evaluating, and revising assignments?” Prior research highlights AI’s potential in offering real-time, detailed feedback but has limitations such as lack of personalization and challenges in engaging higher-order thinking, which this study seeks to address through structured AI activities that follow the PROSE model (Kellam & Pérez Cortés, in review). Employing a convergent mixed-methods design, data was collected from 12 graduate students using Likert-scale surveys and open-ended questions across three activities: (1) comparing AI-generated with professor-provided feedback, (2) using AI-generated examples for assignments, and (3) seeking AI feedback on project drafts. Findings show that AI feedback was generally valued for its immediacy, structure, and actionable nature, though it often lacked the motivational and personalized aspects of professor feedback. These results suggest that AI feedback can aid students’ comprehension and engagement, especially in formative stages of coursework, but may require prompt refinements to support larger, complex assignments. This study highlights the potential of integrating AI into the feedback process, suggesting a complementary role for AI alongside human feedback to create a more comprehensive and balanced learning experience in online graduate education.