An SQL Query Description Problem with AI Assistance for an SQL Programming Learning Assistant System.
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| Title: | An SQL Query Description Problem with AI Assistance for an SQL Programming Learning Assistant System. |
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| Authors: | Wardani, Ni Wayan, Funabiki, Nobuo, Kyaw, Htoo Htoo Sandi, Zhu, Zihao, Kotama, I Nyoman Darma, Sugiartawan, Putu, Putra, I Nyoman Agus Suarya |
| Source: | Information; Jan2026, Vol. 17 Issue 1, p65, 33p |
| Subject Terms: | SQL, ARTIFICIAL intelligence, EDUCATIONAL technology, GENERATIVE artificial intelligence, COGNITIVE ability, RELATIONAL databases, TUTORS & tutoring |
| Abstract: | Today, relational databases are widely used in information systems. SQL (structured query language) is taught extensively in universities and professional schools across the globe as a programming language for its data management and accesses. Previously, we have studied a web-based programming learning assistant system (PLAS) to help novice students learn popular programming languages by themselves through solving various types of exercises. For SQL programming, we have implemented the grammar-concept understanding problem (GUP) and the comment insertion problem (CIP) for its initial studies. In this paper, we propose an SQL Query Description Problem (SDP) as a new exercise type for describing the SQL query to a specified request in a MySQL database system. To reduce teachers' preparation workloads, we integrate a generative AI-assisted SQL query generator to automatically generate a new SDP instance with a given dataset. An SDP instance consists of a table, a set of questions and corresponding queries. Answer correctness is determined by enhanced string matching against an answer module that includes multiple semantically equivalent canonical queries. For evaluation, we generated 11 SDP instances on basic topics using the generator, where we found that Gemini 3.0 Pro exhibited higher pedagogical consistency compared to ChatGPT-5.0, achieving perfect scores in Sensibleness, Topicality, and Readiness metrics. Then, we assigned the generated instances to 32 undergraduate students at the Indonesian Institute of Business and Technology (INSTIKI). The results showed an average correct answer rate of 95.2% and a mean SUS score of 78, which demonstrates strong initial student performance and system acceptance. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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