335 citations · 535 across the 88 of their papers we have counts for
22 papers · 1 filter
Nearly sharp stability of polynomial spectral Ritz projections
Yongxing Guo, Yuwen Li
We establish nearly sharp stability estimates for the Legendre spectral Ritz projection associated with Laplacian on a square domain. Its norm grows proportionally t…
Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Traces
Shuyan Huang, Kai Du, Andrew Lan
As human developers increasingly collaborate with large language model (LLM)-powered coding agents, adapting to individual developer preferences, potentially via agent skills, may…
Principal Trait Analysis: Identifying User Behavioral Traits in Human-AI Collaboration
Hunter McNichols, Kai Du, Fareya Ikram +1
Human-artificial intelligence (AI) collaboration takes many forms and occur in both professional and educational scenarios. It is important to identify how human users interact wit…
Generative AI May Reinforce Social Biases in Software Engineering Education
Erfan Entezami, Andrew Lan, Madeline Endres
Generative artificial intelligence (GenAI) is increasingly being deployed across a wide range of real-world applications. Without careful evaluation, reliance on these systems can…
When Rubrics Change: Cross-Rubric Generalization for Critical Thinking Essay Scoring
Nischal Ashok Kumar, Payu Wittawatolarn, Sana Kang +7
Automated essay scoring (AES) research has largely focused on cross-prompt generalization, where essays from unseen prompts are scored while the scoring criteria are typically held…
GRASP: GRanularity-Aware Search Policy for Agentic RAG
Varun Gandhi, Jaewook Lee, Shantanu Todmal +4
Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers.…
Empowering Student Debugging in Parallel Programming with Execution Traces and Large Language Models
God'salvation F. Oguibe, Vinodh Kumaran Jayakumar, Tongping Liu +2
Concurrent programming is a core component of Computer Science curricula, yet remains notoriously difficult for students to master due to its inherent complexity and the nondetermi…
Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components
Muntasir Hoq, Griffin Pitts, Zhangqi Duan +5
Introductory programming instruction relies on hands-on practice and short learning activities to support mastery of foundational concepts. Although many such learning resources ex…
A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming
Hasnain Heickal, Andrew Lan
Large language models (LLMs) are increasingly used to provide automated feedback in introductory programming courses, yet empirical evidence from authentic classroom deployments co…
Gumbel Machine: Counterfactual Student Writing Generation via Gumbel Noise Steering
Hunter McNichols, Alexander Scarlatos, Mihai Dascalu +2
An effective method of teaching across disciplines is to provide examples of high-quality work. However, an example may be significantly different from a student's current work, ma…
Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues
Zhangqi Duan, Shuyan Huang, Alexander Scarlatos +3
A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students, which can facilitate tutor m…
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
Shuyan Huang, Alexander Scarlatos, Jaewook Lee +1
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring s…
Cognitive Offloading in Agile Teams: How Artificial Intelligence Reshapes Risk Assessment and Planning Quality
Adriana Caraeni, Alexander Shick, Andrew Lan
Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cognition remains underexplored.…
Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation
Candace Walkington, Theodora Beauchamp, Fareya Ikram +4
Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personaliz…
A Multi-Agent Approach to Validate and Refine LLM-Generated Personalized Math Problems
Fareya Ikram, Nischal Ashok Kumar, Junyang Lu +4
Students benefit from math problems contextualized to their interests. Large language models (LLMs) offer promise for efficient personalization at scale. However, LLM-generated per…
Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
Zhangqi Duan, Arnav Kankaria, Dhruv Kartik +1
Fine-grained skill representations, commonly referred to as knowledge components (KCs), are fundamental to many approaches in student modeling and learning analytics. However, KC-l…
Should There be a Teacher In-the-Loop? A Study of Generative AI Personalized Tasks Middle School
Candace Walkington, Mingyu Feng, Itffini Pruitt-Britton +2
Adapting instruction to the fine-grained needs of individual students is a powerful application of recent advances in large language models. These generative AI models can create t…
CATTO: Balancing Preferences and Confidence in Language Models
Nisarg Parikh, Ananya Sai, Pannaga Shivaswamy +2
Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequent…
CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems
Wanyong Feng, Alexander Scarlatos, Ruochen Sun +1
Existing Computerized Adaptive Testing (CAT) frameworks typically select questions based on the predicted likelihood that the student will answer correctly. This design ignores inf…
Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization
Jaewook Lee, Alexander Scarlatos, Simon Woodhead +1
With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior wo…
KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks
Zhangqi Duan, Nigel Fernandez, Andrew Lan
Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (…
Simulated Students in Tutoring Dialogues: Substance or Illusion?
Alexander Scarlatos, Jaewook Lee, Simon Woodhead +1
Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires real students, which is time-con…