Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study

Kazım Timuçin Utkan; Başar Öztayşi

doi:10.1007/978-3-031-97992-7_70

Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study

Kazım Timuçin Utkan^*, Başar Öztayşi

^*Corresponding author for this work

Department of Industrial Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

It is widely acknowledged that timely, personalized feedback is necessary for effective and rapid learning, yet providing such feedback at scale remains a challenge in higher education. In this study, we investigate the use of Large Language Models (LLMs) to automate feedback provision within the context of a Database Management Systems (DBMS) course, and we propose a system designed for this purpose. We introduce a feedback mechanism that utilizes GPT-4o to evaluate the SQL queries students write in response to exam questions, generating detailed, individualized critiques of their answers along with confidence indicators. To assess the feasibility of this LLM-centric approach, we conducted a preliminary experimental study using real student exam data. The results show that the GPT-4o-based system can produce feedback closely aligned with instructor evaluations in many cases, offering a promising solution for scaling formative assessment. We outline the design of the system, present observations from the case study, and discuss the potential benefits and challenges of employing LLMs for educational feedback in real-world classroom settings.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Basar Oztaysi, Sezi Cevik Onar, Cagri Tolga, Irem Ucal Sari, Irem Otay
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	632-638
Number of pages	7
ISBN (Print)	9783031979910
DOIs	https://doi.org/10.1007/978-3-031-97992-7_70
Publication status	Published - 2025
Event	7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 - Istanbul, Turkey Duration: 29 Jul 2025 → 31 Jul 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1529 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025
Country/Territory	Turkey
City	Istanbul
Period	29/07/25 → 31/07/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Automated Assessment
GPT-4o
Large Language Models
Personalized Feedback
SQL Education

Access to Document

10.1007/978-3-031-97992-7_70

Cite this

Utkan, K. T., & Öztayşi, B. (2025). Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study. In C. Kahraman, S. Cebi, B. Oztaysi, S. Cevik Onar, C. Tolga, I. Ucal Sari, & I. Otay (Eds.), Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference (pp. 632-638). (Lecture Notes in Networks and Systems; Vol. 1529 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-97992-7_70

Utkan, Kazım Timuçin ; Öztayşi, Başar. / Large Language Models for Automated Personalized Feedback in SQL Education : A Preliminary Study. Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Basar Oztaysi ; Sezi Cevik Onar ; Cagri Tolga ; Irem Ucal Sari ; Irem Otay. Springer Science and Business Media Deutschland GmbH, 2025. pp. 632-638 (Lecture Notes in Networks and Systems).

@inproceedings{4983ab4c51ef457cb813b65ccd168e1d,

title = "Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study",

abstract = "It is widely acknowledged that timely, personalized feedback is necessary for effective and rapid learning, yet providing such feedback at scale remains a challenge in higher education. In this study, we investigate the use of Large Language Models (LLMs) to automate feedback provision within the context of a Database Management Systems (DBMS) course, and we propose a system designed for this purpose. We introduce a feedback mechanism that utilizes GPT-4o to evaluate the SQL queries students write in response to exam questions, generating detailed, individualized critiques of their answers along with confidence indicators. To assess the feasibility of this LLM-centric approach, we conducted a preliminary experimental study using real student exam data. The results show that the GPT-4o-based system can produce feedback closely aligned with instructor evaluations in many cases, offering a promising solution for scaling formative assessment. We outline the design of the system, present observations from the case study, and discuss the potential benefits and challenges of employing LLMs for educational feedback in real-world classroom settings.",

keywords = "Automated Assessment, GPT-4o, Large Language Models, Personalized Feedback, SQL Education",

author = "Utkan, \{Kazım Timu{\c c}in\} and Ba{\c s}ar {\"O}ztay{\c s}i",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025 ; Conference date: 29-07-2025 Through 31-07-2025",

year = "2025",

doi = "10.1007/978-3-031-97992-7\_70",

language = "English",

isbn = "9783031979910",

series = "Lecture Notes in Networks and Systems",

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pages = "632--638",

editor = "Cengiz Kahraman and Selcuk Cebi and Basar Oztaysi and \{Cevik Onar\}, Sezi and Cagri Tolga and \{Ucal Sari\}, Irem and Irem Otay",

booktitle = "Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference",

address = "Germany",

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Utkan, KT & Öztayşi, B 2025, Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study. in C Kahraman, S Cebi, B Oztaysi, S Cevik Onar, C Tolga, I Ucal Sari & I Otay (eds), Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. Lecture Notes in Networks and Systems, vol. 1529 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 632-638, 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025, Istanbul, Turkey, 29/07/25. https://doi.org/10.1007/978-3-031-97992-7_70

Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study. / Utkan, Kazım Timuçin; Öztayşi, Başar.
Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Basar Oztaysi; Sezi Cevik Onar; Cagri Tolga; Irem Ucal Sari; Irem Otay. Springer Science and Business Media Deutschland GmbH, 2025. p. 632-638 (Lecture Notes in Networks and Systems; Vol. 1529 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Large Language Models for Automated Personalized Feedback in SQL Education

T2 - 7th International Conference on Intelligent and Fuzzy Systems, INFUS 2025

AU - Utkan, Kazım Timuçin

AU - Öztayşi, Başar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - It is widely acknowledged that timely, personalized feedback is necessary for effective and rapid learning, yet providing such feedback at scale remains a challenge in higher education. In this study, we investigate the use of Large Language Models (LLMs) to automate feedback provision within the context of a Database Management Systems (DBMS) course, and we propose a system designed for this purpose. We introduce a feedback mechanism that utilizes GPT-4o to evaluate the SQL queries students write in response to exam questions, generating detailed, individualized critiques of their answers along with confidence indicators. To assess the feasibility of this LLM-centric approach, we conducted a preliminary experimental study using real student exam data. The results show that the GPT-4o-based system can produce feedback closely aligned with instructor evaluations in many cases, offering a promising solution for scaling formative assessment. We outline the design of the system, present observations from the case study, and discuss the potential benefits and challenges of employing LLMs for educational feedback in real-world classroom settings.

AB - It is widely acknowledged that timely, personalized feedback is necessary for effective and rapid learning, yet providing such feedback at scale remains a challenge in higher education. In this study, we investigate the use of Large Language Models (LLMs) to automate feedback provision within the context of a Database Management Systems (DBMS) course, and we propose a system designed for this purpose. We introduce a feedback mechanism that utilizes GPT-4o to evaluate the SQL queries students write in response to exam questions, generating detailed, individualized critiques of their answers along with confidence indicators. To assess the feasibility of this LLM-centric approach, we conducted a preliminary experimental study using real student exam data. The results show that the GPT-4o-based system can produce feedback closely aligned with instructor evaluations in many cases, offering a promising solution for scaling formative assessment. We outline the design of the system, present observations from the case study, and discuss the potential benefits and challenges of employing LLMs for educational feedback in real-world classroom settings.

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KW - Personalized Feedback

KW - SQL Education

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DO - 10.1007/978-3-031-97992-7_70

M3 - Conference contribution

AN - SCOPUS:105013061730

SN - 9783031979910

T3 - Lecture Notes in Networks and Systems

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A2 - Cebi, Selcuk

A2 - Oztaysi, Basar

A2 - Cevik Onar, Sezi

A2 - Tolga, Cagri

A2 - Ucal Sari, Irem

A2 - Otay, Irem

PB - Springer Science and Business Media Deutschland GmbH

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Utkan KT, Öztayşi B. Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study. In Kahraman C, Cebi S, Oztaysi B, Cevik Onar S, Tolga C, Ucal Sari I, Otay I, editors, Intelligent and Fuzzy Systems - Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference. Springer Science and Business Media Deutschland GmbH. 2025. p. 632-638. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-97992-7_70

Large Language Models for Automated Personalized Feedback in SQL Education: A Preliminary Study

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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