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Despite the widespread use of SQL in both academic and professional contexts, students often struggle with writing correct queries due to a range of syntactic and semantic misconceptions. In this work, we propose a framework supporting SQL learning centered around errors as central to the learning process. The framework integrates generative AI and automated error categorization to foster metacognitive engagement and support personalization.

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Enhancing SQL Learning Through Generative AI and Student Error Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Davide Ponzini,
  • Barbara Catania,
  • Giovanna Guerrini

摘要

Despite the widespread use of SQL in both academic and professional contexts, students often struggle with writing correct queries due to a range of syntactic and semantic misconceptions. In this work, we propose a framework supporting SQL learning centered around errors as central to the learning process. The framework integrates generative AI and automated error categorization to foster metacognitive engagement and support personalization.