8 citations · 11 across the 13 of their papers we have counts for
16 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…
Teacher-Authored Prompts for Configuring Student-AI Dialogue: K-12 Classroom Implementation
Alex Liu, Min Sun, Lief Esbenshade +3
GenAI has rapidly entered instructional and learning settings as a teaching assistant or AI tutor. However, less is known about how pedagogical intent connects to the learning gene…
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…
Don't Use a Cannon to Kill a Fly: Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations
Guancheng Lin, Xiao Yu, Jacky Keung +3
Pre-trained or fine-tuned on large code corpora, Large Language Models (LLMs) have demonstrated strong performance in code completion tasks. However, their embedded knowledge is co…
Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data
Zi Liang, Qingqing Ye, Xuan Liu +3
Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large language models (LLMs) during trainin…
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…
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…
Improving the Distributional Alignment of LLMs using Supervision
Gauri Kambhatla, Sanjana Gautam, Angela Zhang +4
The ability to accurately align LLMs with diverse population groups on subjective questions would have great value. In this work, we show that adding simple supervision can more co…
Implementation Considerations for Automated AI Grading of Student Work
Zewei Tian, Alex Liu, Lief Esbenshade +4
This study explores the classroom implementation of an AI-powered grading platform in K-12 settings through a co-design pilot with 19 teachers. We combine platform usage logs, surv…
Connecting Feedback to Choice: Understanding Educator Preferences in GenAI vs. Human-Created Lesson Plans in K-12 Education -- A Comparative Analysis
Shawon Sarkar, Min Sun, Alex Liu +4
As generative AI (GenAI) models are increasingly explored for educational applications, understanding educator preferences for AI-generated lesson plans is critical for their effec…
Adapting to Educate: Conversational AI's Role in Mathematics Education Across Different Educational Contexts
Alex Liu, Lief Esbenshade, Min Sun +4
As educational settings increasingly integrate artificial intelligence (AI), understanding how AI tools identify -- and adapt their responses to -- varied educational contexts beco…
From Defects to Demands: A Unified, Iterative, and Heuristically Guided LLM-Based Framework for Automated Software Repair and Requirement Realization
Alex, Liu, Vivian +1
This manuscript signals a new era in the integration of artificial intelligence with software engineering, placing machines at the pinnacle of coding capability. We present a forma…
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
Zewei Tian, Min Sun, Alex Liu +2
This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We i…
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews
Alex Liu, Min Sun
Obtaining stakeholders' diverse experiences and opinions about current policy in a timely manner is crucial for policymakers to identify strengths and gaps in resource allocation,…