5 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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,…
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…
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…
Automatic Large Language Models Creation of Interactive Learning Lessons
Jionghao Lin, Jiarui Rao, Yiyang Zhao +6
We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engine…
Starting Seatwork Earlier as a Valid Measure of Student Engagement
Ashish Gurung, Jionghao Lin, Zhongtian Huang +4
Prior work has developed a range of automated measures ("detectors") of student self-regulation and engagement from student log data. These measures have been successfully used to…