13 papers
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…
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…