8 papers
Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content
Juho Leinonen, Paul Denny
As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can…
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
Qiming Bao, Juho Leinonen, Alex Yuxuan Peng +7
Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing e…
Oversight in Action: Experiences with Instructor-Moderated LLM Responses in an Online Discussion Forum
Shuying Qiao, Paul Denny, Nasser Giacaman
The integration of large language models (LLMs) into computing education offers many potential benefits to student learning, and several novel pedagogical approaches have been repo…
Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers
Neil C. C. Brown, Pierre Weill-Tessier, Juho Leinonen +2
Motivation: Students learning to program often reach states where they are stuck and can make no forward progress. An automatically generated next-step hint can help them make forw…
Evaluating Language Models for Generating and Judging Programming Feedback
Charles Koutcheme, Nicola Dainese, Arto Hellas +4
The emergence of large language models (LLMs) has transformed research and practice across a wide range of domains. Within the computing education research (CER) domain, LLMs have…
Teaching Program Decomposition in CS1: A Conceptual Framework for Improved Code Quality
Georgiana Haldeman, Judah Robbins Bernal, Alec Wydra +1
Program decomposition is essential for developing maintainable and efficient software, yet it remains a challenging skill to teach and learn in introductory programming courses. Wh…