Juho Leinonen

This work examines reference integrity at key computing education venues using ACM Digital Library data, and manually identifies LLM-generated hallucinations containing verifiably false information, including impossible page ranges, invented titles, and misattributed authors.

Emerging AI tools are reshaping software engineering education, revealing significant curricular trends that could redefine how future developers are trained.

Students using text prompts outperformed their peers using voice input, highlighting the need for careful consideration of input modalities in programming education.

Deliberately injecting bugs into GenAI-generated code significantly enhances students' debugging skills and success rates in subsequent attempts.

Novice programmers often overlook crucial details in prompts, leading to a reliance on AI that can hinder their debugging skills and understanding of code generation.

LLMs can get up to 6x more logically consistent without human feedback, simply by fusing NLI scores into the DPO training loop.

Forget hand-crafted examples: this system automatically generates worked examples tailored to student errors by mining common code patterns.

Open-weight language models can now mimic student debugging processes with surprising fidelity by learning from conversational logs of student-environment interactions, offering a privacy-respecting alternative to prompting proprietary LLMs.

Fine-tuning smaller, open-source LLMs on a targeted dataset can rival the performance of larger, proprietary models for explaining compiler errors, offering a cost-effective and privacy-preserving solution for educational tools.