Authors: Junsoo Park, Youssef Medhat, Htet Phyo Wai, Ploy Thajchayapong, Ashok K. Goel (2026) — Georgia Tech

Surfacing Isolated Learners

What It Is

An interpretable decision layer that ranks course topics needing instructor attention without using grades or post-hoc outcome labels. The system combines three process-level signals to identify which topics (and which students) need intervention before formal assessments.

How It Works

Three signals feed into a topic priority score:

1. Gap prevalence (Rₜ): Fraction of students showing difficulty with topic t, detected from Jill Watson interaction traces

2. Survey disagreement (Dₜ): Difference between observed difficulty and student self-reports — captures blind spots

3. Teacher friction (F): Unresolved instructor concerns coded from semi-structured interviews

Topic priority: Pₜ = 0.70·Rₜ + 0.20·Dₜ + 0.10·F

Output is a ranked set of topic priorities with per-topic decision records explaining each ranking.

Key Results

Instructor alignment: Top-5 mediated topics overlapped with instructor concerns on 3/5 topics; Spearman ρ = 0.80

Student difficulty alignment: ρ = 0.46 (p = .048) — captures something beyond simple difficulty surveys

Isolated learner detection: Multi-signal integration AUC = 0.96 vs. 0.91 for gap prevalence alone — identified 2 isolated learners not detected by any single signal

Construct validity: Reflective thinking, help-seeking, and self-efficacy aligned with topic understanding scores

Why It Matters

This is one of the first systems to operationalize human-AI co-agency in classroom settings. The interpretable outputs help teachers trust and act on AI-provided priorities when grades are not yet available. By combining multiple weak signals, the system surfaces students who would otherwise be invisible — a critical capability for equitable instruction.