140 citations · 155 across the 8 of their papers we have counts for
4 papers · 1 filter
Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools
Lorenzo Lee Solano, Charles Koutcheme, Juho Leinonen +2
Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over…
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks
Leonardo Pasquarelli, Charles Koutcheme, Arto Hellas
Providing sufficient support for students requires substantial resources, especially considering the growing enrollment numbers. Students need help in a variety of tasks, ranging f…
Let's Ask AI About Their Programs: Exploring ChatGPT's Answers To Program Comprehension Questions
Teemu Lehtinen, Charles Koutcheme, Arto Hellas
Recent research has explored the creation of questions from code submitted by students. These Questions about Learners' Code (QLCs) are created through program analysis, exploring…
Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
Arto Hellas, Juho Leinonen, Sami Sarsa +3
Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and…