Role: Assistant Professor, Strategic Design & Management
Affiliation: Parsons School of Design
Conference: DEL Conference 2024, Nanyang Academy of Fine Arts, Singapore

Objective

Project Rubrik explored AI-driven coaching to personalize design education by providing structured insights and adaptive feedback. The goal was to enhance learning experiences for both individual students and cohorts while maintaining human-centered pedagogy.

Process

Problem Definition

• Investigated existing feedback models and gaps in design education
• Identified challenges in scaling personalized critique and classroom dynamics by cohort while preserving meaningful instructor-student engagement

Coaching Framework

• Developed a system where AI provides structured, objective design assessments to complement human mentorship and relationship-building
• Focused on technical feedback, trend-based suggestions, and actionable next steps to complement instructor insights

Challenges

• Inability to hold and integrate multiple evaluations at once well (i.e. student's intentions with the project and project rubric)
• Difficulty challenging and pushing students incrementally based on where they are, providing practical/actionable vs. broad/vague recommendations

Output

A key innovation of Project Rubrik was its ability to generate AI-assisted student reports and class agenda recommendations, helping instructors better manage time and tailor discussions by individual goals, strengths and team dynamics.

Individual Reports

• AI aggregated individual and cohort-wide performance insights, identifying recurring strengths and areas for improvement
• Reports provided objective feedback on technical skills, design principles, and conceptual clarity
• Included trend-based insights, comparing work to industry benchmarks and emerging design trends
• Helped instructors pinpoint struggling individuals while offering customized learning paths for accelerated growth

Agenda & Group Dynamic Recommendations

• AI analyzed ongoing coursework, recent feedback, and student progress to propose enhancements, exercises and questions to balance group dynamics and encourage participation
• Suggested exercises for critique sessions, ensuring that feedback was equally distributed across students
• Recommended targeted workshops or exercises based on collective challenges (e.g., color theory refinement, UI consistency)
• Assisted instructors in balancing individual coaching with group learning dynamics

Weekly Progress Tracking System

• Set up a system to intake student weekly progress and work
• Developed recommendation channel to track development areas for each student against course learning outcomes

Pilot

• Designed and tested AI-driven critique flows with students to measure positive impact and additive value in my Fall 2024 Creative Team Dynamics course
• Iterated on how AI should balance prescriptive vs. exploratory feedback in creative learning

Peer Review

• Findings were presented at the peer-reviewed DEL Conference 2024, hosted at Nanyang Academy of Fine Arts, Singapore.
• Sparked discussions on AI’s role in design education and its potential to support and amplify, not replace, human mentorship and instructional design

Next Steps

✔ Further develop adaptive AI feedback models to refine critique personalization

✔ Explore and discuss with communities on shaping the future of personalized design education

✔ Expand research on AI’s role in creative learning beyond design education