70 citations · 83 across the 31 of their papers we have counts for
31 papers
Why AI Detection Fails for Academic Integrity
Jonathan A. Karr, Grigorii Khvatskii, Ting Hua +1
Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled…
Do LLMs have core beliefs?
Anna Sokol, Marianna B. Ganapini, Nitesh V. Chawla
The rise of Large Language Models (LLMs) has sparked debate about whether these systems exhibit human-level cognition. In this debate, little attention has been paid to a structura…
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models
Han Bao, Penghao Zhang, Yue Huang +9
Large Language Models (LLMs) are increasingly integrated into real-world decision-making, including in the domain of public policy. Yet, their ability to comprehend and reason abou…
TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
Isabel Molnar, Peiyu Li, Si Chen +5
Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot adv…
AI4CAREER: Responsible AI for STEM Career Development at Scale in K-16 Education
Sugana Chawla, Si Chen, Julia Qian +5
Rapid advances in artificial intelligence (AI) are reshaping how students imagine, explore, and prepare for STEM careers across K-16 education. As AI systems increasingly influence…
Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction
Khiem Le, Sreejata Dey, Marcos Martínez Galindo +4
Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressi…
Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
Yijun Ma, Zehong Wang, Weixiang Sun +4
The opioid epidemic remains one of the most severe public health crises in the United States, yet evaluating policy interventions before implementation is difficult: multiple polic…
A Human-Centred AI System for Multi-Actor Planning and Collaboration in Family Learning
Si Chen, Jingyi Xie, Yao Li +8
Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Despite growing interest in AI tutors, most existing…
Automated Benchmark Generation from Domain Guidelines Informed by Bloom's Taxonomy
Si Chen, Le Huy Khiem, Annalisa Szymanski +3
Open-ended question answering (QA) evaluates a model's ability to perform contextualized reasoning beyond factual recall. This challenge is especially acute in practice-based domai…
Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models
Khiem Le, Phuc Nguyen, Youssef Mroueh +4
Group Relative Policy Optimization (GRPO) has become the dominant method for reinforcement learning with verifiable rewards in large language models, but it suffers from two critic…
Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks
Peiyu Li, Xiuxiu Tang, Si Chen +4
Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly impractical at scale. Existing evaluat…
Graph Diffusion Transformers are In-Context Molecular Designers
Gang Liu, Jie Chen, Yihan Zhu +4
In-context learning allows large models to adapt to new tasks from a few demonstrations, but it has shown limited success in molecular design. Existing databases such as ChEMBL con…
KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance
Kuangshi Ai, Jonathan A. Karr, Meng Jiang +2
We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in safety-critical contexts. Using t…
AI Academy: Building Generative AI Literacy in Higher Ed Instructors
Si Chen, Xiuxiu Tang, Alison Cheng +3
Generative AI is reshaping higher education, yet research has focused largely on students, while instructors remain understudied despite their central role in mediating adoption an…
Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
Si Chen, Isabel R. Molnar, Peiyu Li +5
Large language models (LLMs) typically generate direct answers, yet they are increasingly used as learning tools. Studying instructors' usage is critical, given their role in teach…
Racing to the Starting Line: Measuring Opportunity and Improvement in Cross Country
Jonathan A. Karr, Ryan M. Fryer, Nitesh V. Chawla
Collegiate cross country programs set race schedules without large comparable datasets across courses and conditions. Using the comprehensive-era National Running Club Database (NR…
LatentPrompt: Optimizing Promts in Latent Space
Mateusz Bystroński, Grzegorz Piotrowski, Nitesh V. Chawla +1
Recent advances have shown that optimizing prompts for Large Language Models (LLMs) can significantly improve task performance, yet many optimization techniques rely on heuristics…
Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation
Xiuxiu Tang, Si Chen, Ying Cheng +3
As generative AI (GenAI) tools like ChatGPT become more common in higher education, understanding student attitudes is essential for evaluating their educational impact and support…
"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness
Jonathan A. Karr, Benjamin F. Herbst, Matthew L. Sisk +5
Homelessness is a persistent social challenge, impacting millions worldwide. Over 876,000 people experiencing homelessness (PEH) were recorded in the U.S. in 2025. Social bias is a…
Building Scaffolding Dialogue Data with LLM-Simulated Novices
Si Chen, Izzy Molnar, Ting Hua +6
High-quality, multi-turn instructional dialogues between novices and experts are essential for developing AI systems that support teaching, learning, and decision-making. These dia…
FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
Khiem Le, Tuan Tran, Ting Hua +1
Existing resource-adaptive LoRA federated fine-tuning methods enable clients to fine-tune models using compressed versions of global LoRA matrices, in order to accommodate various…
Graph Foundation Models: A Comprehensive Survey
Zehong Wang, Zheyuan Liu, Tianyi Ma +16
Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural lang…
SMOTExT: SMOTE meets Large Language Models
Mateusz Bystroński, Mikołaj Hołysz, Grzegorz Piotrowski +2
Data scarcity and class imbalance are persistent challenges in training robust NLP models, especially in specialized domains or low-resource settings. We propose a novel technique,…
Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructors
Si Chen, Reid Metoyer, Khiem Le +6
Instructors play a pivotal role in integrating AI into education, yet their adoption of AI-powered tools remains inconsistent. Despite this, limited research explores how to design…