235 citations · 245 across the 15 of their papers we have counts for
16 papers
Correctness, Convergence, and AI-Generated Code Detection: A Longitudinal Study of Student and Large Language Model Code in Introductory Programming
Runlong Ye, Jing Fan, Angela Zavaleta Bernuy +4
Large language models can generate plausible solutions to programming assignments, making it tempting to detect their use by matching student code against a reference bank of gener…
Who Bears the Cost of Honesty? A FAccT Workshop Synthesis and Research Agenda for Equitable AI Disclosure
Runlong Ye, Jessica He, Finola Finn +4
AI disclosure is increasingly promoted and sometimes required as a route to transparency, accountability, provenance, and trust. Yet disclosure can also expose AI users to suspicio…
Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models
Franco Ortiz, Runlong Ye, Michael Liut
Large Language Models (LLMs) have been widely applied to student-facing educational tools, this work explores their use in supporting instructors by presenting a practical adaptati…
Exploring Emerging Norms of AI Attribution and Disclosure in Programming Education
Runlong Ye, Oliver Huang, Jessica He +1
Generative AI blurs the lines of authorship in computing education, creating uncertainty around how students should attribute AI assistance. To examine these emerging norms, we con…
From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews
Runlong Ye, Naaz Sibia, Angela Zavaleta Bernuy +4
Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This fri…
Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course
Ruiwei Xiao, Runlong Ye, Xinying Hou +4
Despite universal GenAI adoption, students cannot distinguish task performance from actual learning and lack skills to leverage AI for learning, leading to worse exam performance w…
Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation
Runlong Ye, Oliver Huang, Patrick Yung Kang Lee +3
Reflexive Thematic Analysis (RTA) is a critical method for generating deep interpretive insights. Yet its core tenets, including researcher reflexivity, tangible analytical evoluti…
TreeWriter: AI-Assisted Hierarchical Planning and Writing for Long-Form Documents
Zijian Zhang, Fangshi Du, Xingjian Liu +5
Long documents pose many challenges to current intelligent writing systems. These include maintaining consistency across sections, sustaining efficient planning and writing as docu…
A Comparative Study of Student Perspectives on Technical Writing Feedback Quality: Evaluating LLMs, SLMs, and Humans in Computer Science Topics
Suqing Liu, Runlong Ye, Christopher Eaton +2
To address the scalability of feedback in computer science while mitigating the privacy and cost limitations of commercial Large Language Models (LLMs), this study evaluates a loca…
Exploring Student Choice and the Use of Multimodal Generative AI in Programming Learning
Xinying Hou, Ruiwei Xiao, Runlong Ye +2
The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programmi…
TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Zijian Zhang, Pan Chen, Fangshi Du +4
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a…
Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Education
Ruiwei Xiao, Xinying Hou, Runlong Ye +4
With the proliferation of large language model (LLM) applications since 2022, their use in education has sparked both excitement and concern. Recent studies consistently highlight…
Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
Runlong Ye, Zeling Zhang, Boushra Almazroua +1
AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without e…
ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking
Runlong Ye, Patrick Yung Kang Lee, Matthew Varona +2
Synthesizing knowledge from large document collections is a critical yet increasingly complex aspect of qualitative research and knowledge work. While AI offers automation potentia…
The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity
Runlong Ye, Matthew Varona, Oliver Huang +3
Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. While promising for augmenting cognition and…
CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs
Majeed Kazemitabaar, Runlong Ye, Xiaoning Wang +4
Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may h…