Scaling AI-Powered Tutor Authoring for Educators
Redesigning and engineering scalable bulk creation for intelligent tutoring content.
Scaling AI-Powered Tutor Authoring for Educators
Redesigning and engineering scalable bulk creation for intelligent tutoring content.
TEAM
Collaborated closely with Principal Programmer & CTAT Research Advisor at Carnegie Mellon University
Role
UX Engineer & Designer. Led design prototyping and user testing, and engineered early-phase implementation.
Timeline
8 Months | May 2025 - January 2026
Tools + Technologies
Figma, Vue.js, JavaScript, GitHub, Think-aloud testing
TEAM
Collaborated closely with Principal Programmer & CTAT Research Advisor at Carnegie Mellon University
Role
UX Engineer & Designer. Led design prototyping and user testing, and engineered early-phase implementation.
Timeline
8 Months | May 2025 - January 2026
Tools + Technologies
Figma, Vue.js, JavaScript, GitHub, Think-aloud testing
TEAM
Collaborated closely with Principal Programmer & CTAT Research Advisor at Carnegie Mellon University
Role
UX Engineer & Designer. Led design prototyping and user testing, and engineered early-phase implementation.
Timeline
8 Months | May 2025 - January 2026
Tools + Technologies
Figma, Vue.js, JavaScript, GitHub, Think-aloud testing
Context
Context
CTAT serves a diverse group of users, from learning science researchers to educators—who need to create and scale AI-powered tutoring systems.
The Learn Lab at Carnegie Mellon University develops AI-based learning technologies, including CTAT (Cognitive Tutor Authoring Tools), a platform that enables educators to author intelligent tutoring systems without deep programming expertise. Within CTAT, the Mass Production tool supports AI-assisted bulk generation of tutoring problems, enabling AI tutors to scale across classrooms, courses, and research studies.
Problem
Problem
Four authoring features that are faster, clearer, and more resilient.
Enhance Mass Production UI & Action Hierarchy
Reorganized primary actions and simplified inputs to make prompt authoring faster, clearer, and easier to navigate.
Guided Prompt-to-Table Workflow with Clear System Feedback
Improved reliability and trust during AI-assisted generation by clarifying system states, next steps, and progress.
Save & Load Prompt Authoring with Reusable Templates
Enabled authors to save, organize, and reload prompt templates to support iteration and reuse across sessions.
Auto-Sync
Designed a future-state syncing experience that reduces manual steps while preserving user control, clarity, and trust during AI-assisted content generation.
Four authoring features that are faster, clearer, and more resilient.
Enhance Mass Production UI & Action Hierarchy
Reorganized primary actions and simplified inputs to make prompt authoring faster, clearer, and easier to navigate.
Guided Prompt-to-Table Workflow with Clear System Feedback
Improved reliability and trust during AI-assisted generation by clarifying system states, next steps, and progress.
Save & Load Prompt Authoring with Reusable Templates
Enabled authors to save, organize, and reload prompt templates to support iteration and reuse across sessions.
Auto-Sync
Designed a future-state syncing experience that reduces manual steps while preserving user control, clarity, and trust during AI-assisted content generation.
Enable high-level refinement without manually editing each section
Designed to support quick iteration when users want to reshape the overall AI output.
Workflow is fragmented, manual, and easy to lose progress in, slowing authors down.
Workflow is fragmented, manual, and easy to lose progress in, slowing authors down.
Solution
Solution
