Parker & Zavala-Cerna (2026) — Published in Education and Information Technologies.
Synthesis
This paper presents a systematic two-stage methodology for surfacing student misconceptions at scale. Drawing on 3,802 medical student enrollments across 5 biomedical science courses (9 course periods, 40-50 quizzes each), Parker and Zavala-Cerna first use quantitative quiz-level performance metrics to identify challenging topics, then deploy LLMs to analyze quiz questions, student response patterns, and lecture transcripts in combination — extracting the specific misconceptions underlying poor performance.
The quality of LLM-identified misconceptions was rated as excellent by subject matter experts, and faculty interviews confirmed that data-driven topic identification aligned with, and extended, instructors' own classroom observations. This is significant because it demonstrates that LLM-based analysis can surface insights invisible in performance data alone — bridging the gap between learning-analytics dashboards (which show what is going wrong) and qualitative pedagogical reasoning (which explains why).
The approach connects to several established themes in the wiki. It operationalizes formative-assessment by enabling targeted, misconception-specific interventions rather than generic remediation. It advances personalized-learning by providing the diagnostic foundation needed for adaptive systems to respond to individual conceptual gaps. And it extends knowledge tracing beyond binary correct/incorrect signals to the richer space of specific misconception categories.
For student-experience, the implications are direct: students struggling with the same topic for different conceptual reasons would receive different remediation. For ai-literacy, the paper models how instructors can leverage AI outputs as hypotheses to be validated by expert judgment, rather than as authoritative diagnoses — a responsible-use pattern relevant to faculty development.
Related Pages
- simulating-students-java-programming-errors-llms — Complementary approach generating synthetic student errors via LLMs- correct-answer-trap-ai-tutor — 8 of 8 papers in May 28 scan
- student-misconceptions-conditionals-loops-taxonomy — structured taxonomy for annotating student programming errors
- learning-analytics — Quantitative foundations that this work extends with qualitative LLM analysis
- formative-assessment — Assessment paradigm that misconception identification serves
- personalized-learning — The downstream goal of misconception-aware instruction
- knowledge-tracing-irt — Related student modeling approach; this adds misconception categorization
- student-experience — Student-facing implications of misconception-targeted remediation
- ai-literacy — Pattern of expert-validated AI use modeled by this methodology
- metacognition — Connection to student awareness of their own misconceptions
Citation
APA: Parker, M. J., & Zavala-Cerna, M. G. (2026). What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics. Education and Information Technologies. arXiv:2605.00294.