An Experiment on Leveraging ChatGPT for Online Teaching and Assessment of Database Students

Abstract

The task of generating SQL queries from natural language questions has long been a challenge for researchers, particularly in the context of teaching and tutoring database students. Existing efforts have yielded limited success, especially when it comes to assessing student assignments automatically. The discrepancy arises from the fact that students’ query solutions may differ significantly from those of instructors, despite producing identical results. The conventional approach to determining query correctness relies on establishing query equivalence, yet a satisfactory algorithmic solution remains elusive due to the lack of a solid theoretical foundation.In this paper, we explore an alternative approach by harnessing the capabilities of ChatGPT, an advanced language model with extensive knowledge. We conduct experiments to investigate if this "all-knowing" system can provide valuable assistance in generating SQL queries. Remarkably, our findings demonstrate that ChatGPT offers significantly more encouraging and superior results compared to existing solutions found in the literature. This research presents a promising avenue for leveraging ChatGPT in the realm of teaching and assessing database students. By capitalizing on the model’s extensive knowledge and language processing abilities, we open up new possibilities for improving the accuracy and efficiency of SQL query generation from natural language. These results contribute to the advancement of educational methodologies and provide valuable insights for researchers and educators seeking innovative approaches to enhance database instruction and assessment.

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