Co-Advisor: Learning Programming Strategies in Context

Programming instruction often focuses on syntax and algorithms. However, mastering programming also requires the building strategic knowledge of skills such as debugging, problem solving, and program design. These critical skills are difficult to teach explicitly because they often involve tacit knowledge, context-specific understanding, and adaptive decision-making. Large Language Models (LLMs) can be effective in helping with syntactic and algorithmic questions but can fail to provide strategic knowledge. This is partly because strategic knowledge involves nuanced contexts that span code and runtime states, requires subjective judgments, and dynamically evolves based on the outcomes of users’ actions. We introduce Co-Advisor, a context-aware strategy recommendation tool that leverages LLM to evaluate problem context and monitor the programmer’s actions to provide personalized constructive feedback. Unlike prior work, Co-Advisor can dynamically align expert strategies with real-time programmer actions and code context, offering actionable, personalized strategic knowledge. In a formative evaluation with 14 programmers involved in two debugging tasks, we found that those using Co-Advisor to receive context-related feedback alongside expert strategies were significantly more successful than those without context-related feedback. They demonstrated greater engagement and had an improved learning experience, gaining insight into the reasons behind their mistakes, correcting them, and understanding the rationale behind their actions. Thus, Co-Advisor enhances conceptual understanding and strategic problem-solving.