Understanding Help-Seeking Behavior of Students Using LLMs vs. Web Search for Writing SQL Queries
This addresses the problem of optimizing help-seeking tools for students in programming education, but it is incremental as it builds on existing comparisons of assistance methods.
The study compared how students use web search versus LLMs (ChatGPT and an instructor-tuned LLM) for help in writing SQL queries, finding that the instructor-tuned LLM required more interactions but led to similar edit counts and no significant differences in query quality, though it reduced mental demand.
Growth in the use of large language models (LLMs) in programming education is altering how students write SQL queries. Traditionally, students relied heavily on web search for coding assistance, but this has shifted with the adoption of LLMs like ChatGPT. However, the comparative process and outcomes of using web search versus LLMs for coding help remain underexplored. To address this, we conducted a randomized interview study in a database classroom to compare web search and LLMs, including a publicly available LLM (ChatGPT) and an instructor-tuned LLM, for writing SQL queries. Our findings indicate that using an instructor-tuned LLM required significantly more interactions than both ChatGPT and web search, but resulted in a similar number of edits to the final SQL query. No significant differences were found in the quality of the final SQL queries between conditions, although the LLM conditions directionally showed higher query quality. Furthermore, students using instructor-tuned LLM reported a lower mental demand. These results have implications for learning and productivity in programming education.