140 citations · 217 across the 41 of their papers we have counts for
6 papers · 1 filter
AI-Generated Slides: Are They Good? Can Students Tell?
Juho Leinonen, Lisa Zhang, Arto Hellas
As generative AI (GenAI) tools become easily accessible, there is promise in using such tools to support instructors. To that end, this paper examines using GenAI to help generate…
Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
Mragisha Jain, Tirth Bhatt, Griffin Pitts +6
Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instances. In this paper, we presen…
Teaching Language Models How to Code Like Learners: Conversational Serialization for Student Simulation
Charles Koutcheme, Juho Leinonen, Arto Hellas
Artificial students -- models that simulate how learners act and respond within educational systems -- are a promising tool for evaluating tutoring strategies and feedback mechanis…
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…
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
James Prather, Brent Reeves, Juho Leinonen +6
Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounte…
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…