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CURI AI Tutor Voice Agent

Exploring how a voice-based adaptive tutoring agent can provide one-to-one instruction
KN
Kaci Nguyen
Role: UX Research
Team: Gen Marconette & Grace Kim (Instructional design), Roza Atarod (UX design)
Duration: 3 months (Jan - May 2021)
Tools: Figma and Voiceflow (for prototyping), Zoom (to conduct usability study)
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Brief

This research aimed to explore how a voice-based adaptive tutoring system may close the equity gap for access to human tutors by providing one-to-one instruction. This was a class project from the Human-AI Interaction course in the School of Information at UT Austin taught by Dr. Min Lee.

Problem

Personalized 1-on-1 learning is inaccessible.
A lot of accessible, cost-friendly learning platforms right now are 1-to-many, such as videos on Khan Academy. Meanwhile, human tutors that provide 1-on-1 guidance can be very expensive and geographically inaccessible.
To address the weaknesses of both methods of learning, we looked to AI as a way that can provide 1-on-1 help anywhere at anytime.

Goal

Improve a learner's motivation, self-efficacy, and metacognition of learning through an AI tutor

Report

Read the final report:
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AI Tutor Report.pdf
1.9 MB
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