9 papers
Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth
Alex Liu, Lief Esbenshade, Michael Xiao +4
Evaluations of LLM-assisted qualitative coding almost universally measure model performance as agreement with human coders, a practice that presumes human coding is the standard to…
Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use
Alex Liu, Min Sun, Lief Esbenshade +4
Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently propose…
Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
Zewei Tian, Alex Liu, Lief Esbenshade +6
The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices. While automated scoring systems and ma…
How K-12 Educators Use AI: LLM-Assisted Qualitative Analysis at Scale
Alex Liu, Lief Esbenshade, Shawon Sarkar +4
This study investigates how K-12 educators use generative AI tools in real-world instructional contexts and how large language models (LLMs) can support scalable qualitative analys…
AI as a Teaching Partner: Early Lessons from Classroom Codesign with Secondary Teachers
Alex Liu, Lief Esbenshade, Shawon Sarkar +6
This report presents a comprehensive account of the Colleague AI Classroom pilot, a collaborative design (co-design) study that brought generative AI technology directly into real…
Emerging Patterns of GenAI Use in K-12 Science and Mathematics Education
Lief Esbenshade, Shawon Sarkar, Drew Nucci +10
In this report, we share findings from a nationally representative survey of US public school math and science teachers, examining current generative AI (GenAI) use, perceptions, c…