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20242026
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cs.CY2026

AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice

Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt +2

There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven syste…

cs.CY2026

Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs

Ashish Gurung, Ge Gao, Jordan Gutterman +6

Hybrid human-AI tutoring, where technology and humans jointly facilitate student learning, can be more beneficial than AI-only tutoring. However, preliminary evidence suggests that…

cs.CY2026

Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork

Ashish Gurung, Jordan Gutterman, Danielle R. Thomas +3

Measures of disengagement provide insights into unproductive use of learning opportunities. Although measures of active disengagement, such as gaming the system and mind-wandering,…

cs.CY2026

Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education

Danielle R. Thomas, Conrad Borchers, Kirk P. Vanacore +2

Generative Artificial Intelligence (GenAI) is now widespread in education, yet the efficacy of GenAI systems remains constrained by the quality and interpretation of the labeled da…

cs.CY2026

Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning

Conrad Borchers, Ashish Gurung, Qinyi Liu +3

Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they recei…