The landscape of student help-seeking behaviour is undergoing a significant transformation with the rise of generative AI tools like Large Language Models (LLMs). Building on prior research that explores help-seeking tendencies among university students, this project aims to investigate and analyse large-scale student data on the effects of integrating LLM-powered assistants in programming courses, focusing on their influence on student behaviour, engagement, and learning outcomes. Ideally generating an approach for improved (predictive and prescriptive) decision making. The research will involve a comprehensive analysis of how the introduction of LLM-based conversational agents (e.g., ChatGPT) and other LLM-based educational tools, such as CodeAid and QuickTA, both developed at the University of Toronto, influence student approaches to seeking help. This will involve data mapping and analysis, but also the need to identify patterns in large conversational data. Traditional help-seeking behaviours have shown a reliance on informal support (e.g., peers) rather than formal educational resources (e.g., instructors), often due to perceived barriers like stigma or accessibility. We hypothesise that the availability of LLM tools may shift these dynamics, increasing students’ reliance on automated, real-time assistance and providing data-rich insights into evolving help-seeking patterns that could enhance predictive and prescriptive modelling for educational support strategies.