Nicola Dainese

Nicola Dainese is a Doctoral Researcher in the Department of Computer Science at the School of Science. His work focuses on the intersection of artificial intelligence and programming education, leveraging large language models (LLMs) to improve code generation, feedback mechanisms, and symbolic regression.

• Current projects include STN LITERACY (2023-2026) and FCAI Flagship (2020-2022, extended to 2026).
• Collaborations span institutions like the Finnish Center for Artificial Intelligence (FCAI) and international research teams.

Research interests include:

• Large Language Models (LLMs) for code generation and feedback
• Model-based reinforcement learning in programming education
• Symbolic regression and function discovery via AI
• Educational technology for student assessment
• Open-source language models and ethical AI applications

Recent publications highlight trends in leveraging LLMs for programming feedback, code repair, and symbolic regression. Key subfields include Monte Carlo Tree Search, prompt engineering, and vision-language models for AI planning.

• Active project member in DATALIT, addressing data literacy for responsible decision-making.
• Contributed to benchmarking studies on educational program repair and code world modeling.