Instructional Design • 2026
AI-Supported Learning Experience

Developing Student Feedback Literacy with AI

An AI-supported learning experience that helps students move from receiving feedback to acting on it — grounded in Carless & Boud's (2018) Feedback Literacy Framework.

Try ActiOn Live
Framework Carless & Boud (2018)
Role Instructional Designer
Topic Feedback Literacy / SRL

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.

Instructional Design Approach

ADDIE

1

Analyze

Identified that students often understand what feedback says but struggle to translate it into future action.

2

Design

Defined the learning outcome of creating an actionable improvement plan and designed scaffolded activities around feedback literacy.

3

Develop

Built four learning modules and AI-supported interactions.

4

Implement

Learners completed the prototype using authentic feedback scenarios and their own feedback.

5

Evaluate

Conducted five rounds of usability testing and iteratively redesigned the experience.

Backward Design

1

Desired Outcome

Students can independently turn instructor feedback into an actionable improvement plan.

2

Evidence of Learning

Students analyze feedback, prioritize areas for improvement, and create an actionable improvement plan.

3

Learning Experience

Learn Assess Practice Apply

How AI Supports Learning

AI as an Instructional Scaffold

In the Practice module, AI guides learners in developing an evidence-based revision plan. Rather than generating the plan for them, the AI asks structured questions, evaluates responses against a rubric, and provides additional guidance when needed.

Interaction Flow Design

1

Student Response

2

AI Evaluates Against Rubric

3

Adaptive Question / Feedback

4

Student Revises Thinking

AI-Assisted Development

Claude Code was used to support platform development, debugging, and implementation while I defined the instructional architecture, learning interactions, and design requirements.

Iteration & Evaluation

After conducting five rounds of user testing with college students, I identified recurring usability challenges and iteratively refined the learning experience based on learner feedback and observations.

User Testing Finding

Learners found the prototype text-heavy.

Design Change

Added relevant visuals and reorganized content to improve information hierarchy.

User Testing Finding

Learners lacked context when entering some modules.

Design Change

Added module introductions explaining their purpose and learning objectives.

User Testing Finding

Learners faced unnecessary complexity in some activities.

Design Change

Simplified and reduced activities while maintaining the targeted learning outcomes.

Final Outcome

The final learning experience provides students with a scaffolded pathway from understanding feedback to creating an actionable improvement plan. By combining feedback literacy research, adaptive AI support, and iterative user-centered design, ActiOn demonstrates how learning science can be translated into a practical digital learning experience.

← Back to All Projects