How assigned AI use before class shapes active student engagement in class

Dan J. Wang; Neelam Modi Jain; Vanessa Burbano; Jorge Guzman; Daniel Keum; Soomi Kim; Bruce Kogut; Nataliya Wright

cs.HCOct 7, 2026

How assigned AI use before class shapes active student engagement in class

Authors: Dan J. Wang, Neelam Modi Jain, Vanessa Burbano, Jorge Guzman, Daniel Keum, Soomi Kim, Bruce Kogut, Nataliya Wright

Organizations: Management Division, Columbia Business School, Columbia University, New York, NY 10027, USA.

Abstract

AI learning tools are rapidly entering classrooms, but evidence about whether they help students learn is mixed and rests mostly on test scores. Comparatively less research addresses whether the use of AI changes students' live learning behaviors in class. Here, we report the results of a preregistered field experiment with 759 MBA students enrolled in ten sections of a course, in which each student was randomly assigned two of ten class sessions to prepare for with a purpose-built voice-based AI discussion partner. After two uses of the AI discussion partner, students made about 31% more voluntary contributions in each later class session. Students who used the AI discussion partner more also reported greater comfort speaking up and greater perceived learning, but not greater focus or motivation. These findings suggest that repeated practice with a voice-based AI partner can meaningfully increase students' engagement in class discussion, enhancing a critical intermediate learning outcome.

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May 28, 2026, cs.AIJ/K move · Enter open · S save

Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI

Junsoo Park, Youssef Medhat, Htet Phyo Wai, Ploy Thajchayapong+1

Georgia Institute Of Technology, North Avenue, Atlanta, GA 30332, USA

Teacher Feedback Decisions Causal Mediation Analysis Ranking

Open paper AskPDF arXiv

AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to translate into timely instructional decisions. We propose an interpretable decision layer: a transparent mechanism that ranks course topics requiring attention without using grades or post-hoc outcome labels. The approach combines three signals: student learning difficulty prevalence, disagreement between learner self-reports and observed difficulties, and unresolved teacher concerns. The output is a ranked set of topic priorities with per-topic decision records explaining each ranking. In one graduate CS course offering (

n=5

instructor interviews;

n=279

survey responses), prioritized topics aligned with instructor concerns (top-5 overlap 3/5; Spearman

ρ=0.80

) and student-reported topic difficulty (

ρ=0.46

p=.048

). Multi-signal integration also surfaced learners not identified through individual signal sources alone (AUC

=0.96

vs.

0.91

for gap prevalence alone). Reflective thinking, help-seeking, and self-efficacy provided additional evidence that student behavioral signals align with learning-related constructs. While preliminary, these findings suggest that transparent coordination mechanisms may help support human-AI co-agency when feedback is incomplete.

Abstract

Explore similar work

Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI

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