The Problem
Students regularly receive feedback on their work, yet many struggle to use it to improve future performance. Feedback often becomes a justification for a grade rather than a tool for learning. While institutions invest significant effort in improving feedback quality, research suggests that the larger challenge lies in students' ability to interpret, evaluate, and act upon feedback.
Carless and Boud (2018) describe this challenge as a feedback literacy problem. Students need support not only in understanding feedback, but also in managing their emotional responses, recognizing feedback's purpose, evaluating its importance, and developing plans for improvement. Without these skills, feedback rarely translates into meaningful action.
Research-Based Design
To address this challenge, I designed an AI-supported learning experience grounded in the Feedback Literacy Framework (Carless & Boud, 2018). The learning experience focuses on four dimensions of feedback literacy:
- 1. Managing Affect
- 2. Appreciating Feedback
- 3. Making Judgements
- 4. Taking Action
Rather than simply referencing these theories, I analyzed the literature to identify the core instructional principles underlying each dimension and translated those principles into learning activities and AI-supported interactions.
Translating Theory into Practice
The Appreciating Feedback module was informed by Winstone and Boud's (2021) work on helping students understand the purpose and value of feedback. The Making Judgements module drew on Panadero and Broadbent's (2018) research on evaluative judgement, encouraging learners to prioritize and evaluate feedback rather than treating all comments equally. The Taking Action module incorporated self-regulated learning principles from Zimmerman (2002), guiding students to develop concrete plans for future improvement.
Instructional Design Approach
The learning experience was designed around students' own feedback rather than hypothetical examples. Learners upload or review feedback they have received and complete a sequence of structured reflection activities aligned with the four dimensions of feedback literacy.
Within Module 3, I designed AI-supported interactions that scaffold students' thinking and reflection processes. The AI does not provide answers or corrections. Instead, it uses prompts and guided questioning to help students interpret feedback, evaluate its significance, and identify meaningful next steps.
By integrating learning science principles directly into both assessment activities and AI interactions, the design supports deeper engagement with feedback while encouraging learner ownership of the improvement process.
Final Learning Experience
The final product is an AI-supported feedback literacy intervention that helps students move from receiving feedback to acting on it.
Through structured reflection activities, evidence-based scaffolding, and guided AI interactions, students develop the skills needed to understand feedback, evaluate its importance, and create actionable improvement plans. The project demonstrates my ability to synthesize learning science research, design learner-centered instructional experiences, and translate theoretical frameworks into practical educational solutions.
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