Practical Applications of Generative AI in Educational Support

Zhihua Zhang

doi:doi:10.11648/j.edu.20251403.14

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Practical Applications of Generative AI in Educational Support

Zhihua Zhang^*

Published in Education Journal (Volume 14, Issue 3)

Received: 25 March 2025 Accepted: 2 April 2025 Published: 26 May 2025

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Abstract

This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.

Published in	Education Journal (Volume 14, Issue 3)
DOI	10.11648/j.edu.20251403.14
Page(s)	111-125
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

LLMs, Generative AI, GPTs, Educational Applications of GenAI, Data Science Education

References

[1]	Zhang, Z., et al. Development of Education Curriculum in the Data Science Area for a Liberal Arts University. Towards a Collaborative Society Through Creative Learning, In Springer Nature, 2023, Keane, T. Lewin, C., Brinda, T., & Bottino, R. (Eds.). https://doi.org/10.1007/978-3-031-43393-1_32
[2]	Misa Tei. (2023). How should generative AI be used in education? Possibilities and challenges considered from various guidelines, Compass for SDGs & Society 5.0, Institute Business Environment Report, pp. 1-11. Available from: https://www.dlri.co.jp/files/ld/285589.pdf [Accessed 19 August 2024].
[3]	UNESCO，「AI and education: guidance for policy-makers」, Published in 2021 by the United Nations Educational, Scientific and Cultural Organization, 2021/12/26, https://doi.org/10.54675/PCSP7350 [Accessed 2 May 2024].
[4]	The U.S. Department of Education Office of Educational Technology, Artificial Intelligence and the Future of Teaching and Learning. Insights and Recommendations. Available from: https://www2.ed.gov/documents/ai-report/ai-report.pdf [Accessed 15 August 2024].
[5]	The U.S. Department of Education Office of Educational Technology, Designing for Education with Artificial Intelligence: An Essential Guide for Developers. Available from: https://tech.ed.gov/files/2024/07/Designing-for-Education-with-Artificial-Intelligence-An-Essential-Guide-for-Developers.pdf [Accessed 12 December 2024].
[6]	Ministry of Education, Culture, Sports, Science and Technology of Japan, Provisional Guidelines for the Use of Generative AI in Primary and Secondary Education. Available from: https://www.mext.go.jp/content/20230718-mtx_syoto02-000031167_011.pdf 1-24, 2023. [Accessed 26 December 2024].
[7]	Zhang, Z. Practical application of on-demand lessons in data science education for liberal arts students at private universities, Proceedings of the 85th National Conference of the Information Processing Society of Japan, Vol. 4, 97-112, 2023.
[8]	Zhang, Z. et al. The Course Design of Basic Data Science Taking into Account Both Face-To-Face and On-Demand Teaching and Effect Analysis, the 2023 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC). https://doi.org/10.23919/PNC58718.2023.10314978
[9]	Genshiro Kitagawa, Akimichi Takemura. Data Science as Liberal Arts, 2nd. Tokyo: Kodansha; 2022, 1-229.
[10]	OpenAI. GPT-4 Technical Report, Available from: https://cdn.openai.com/papers/gpt-4.pdf 2023, 1-100. [Accessed 20 April 2024].
[11]	A Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, 2022, 1-43, https://arxiv.org/pdf/2201.11903
[12]	AI achieves silver-medal standard solving International Mathematical Olympiad problems, Available from: https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ 2024. [Accessed 19 October 2024].
[13]	Japan Inter-University Consortium for Mathematics, Data Science and AI Education, Mathematics, Data Science, AI (Literacy Level) Model Curriculum - Cultivating Data Thinking - (Revised February 22, 2024), http://www.mi.u-tokyo.ac.jp/consortium/pdf/model_literacy_20240222.pdf, 2024 [Accessed 20 September 2024].
[14]	Japan Inter-University Consortium for Mathematics, Data Science and AI Education, Mathematics, Data Science, AI (Applied Basic Level) Model Curriculum - Cultivating Data Thinking - (Revised February 22, 2024), http://www.mi.u-tokyo.ac.jp/consortium/pdf/model_ouyoukiso_20240222.pdf 2024. [Accessed 25 December 2024].
[15]	Vaswani et al., Attention Is All You Need [Submitted on 12 Jun 2017 (v1), last revised 2 Aug 2023], arXiv:1706.03762v7, https://doi.org/10.48550/arXiv.1706.03762 [Accessed 15 April 2024].

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APA Style

Zhang, Z. (2025). Practical Applications of Generative AI in Educational Support. Education Journal, 14(3), 111-125. https://doi.org/10.11648/j.edu.20251403.14

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ACS Style

Zhang, Z. Practical Applications of Generative AI in Educational Support. Educ. J. 2025, 14(3), 111-125. doi: 10.11648/j.edu.20251403.14

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AMA Style

Zhang Z. Practical Applications of Generative AI in Educational Support. Educ J. 2025;14(3):111-125. doi: 10.11648/j.edu.20251403.14

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@article{10.11648/j.edu.20251403.14,
  author = {Zhihua Zhang},
  title = {Practical Applications of Generative AI in Educational Support
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {111-125},
  doi = {10.11648/j.edu.20251403.14},
  url = {https://doi.org/10.11648/j.edu.20251403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.14},
  abstract = {This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.
},
 year = {2025}
}

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TY  - JOUR
T1  - Practical Applications of Generative AI in Educational Support

AU  - Zhihua Zhang
Y1  - 2025/05/26
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.14
DO  - 10.11648/j.edu.20251403.14
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 111
EP  - 125
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.14
AB  - This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.

VL  - 14
IS  - 3
ER  -

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Author Information

Zhihua Zhang

Department of Data Science Education, Kansai University of International Studies, Kobe, Japan

Contact Email

http://orcid.org/0000-0003-3511-6073

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APA Style

Zhang, Z. (2025). Practical Applications of Generative AI in Educational Support. Education Journal, 14(3), 111-125. https://doi.org/10.11648/j.edu.20251403.14

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ACS Style

Zhang, Z. Practical Applications of Generative AI in Educational Support. Educ. J. 2025, 14(3), 111-125. doi: 10.11648/j.edu.20251403.14

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AMA Style

Zhang Z. Practical Applications of Generative AI in Educational Support. Educ J. 2025;14(3):111-125. doi: 10.11648/j.edu.20251403.14

Copy | Download

@article{10.11648/j.edu.20251403.14,
  author = {Zhihua Zhang},
  title = {Practical Applications of Generative AI in Educational Support
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {111-125},
  doi = {10.11648/j.edu.20251403.14},
  url = {https://doi.org/10.11648/j.edu.20251403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.14},
  abstract = {This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Practical Applications of Generative AI in Educational Support

AU  - Zhihua Zhang
Y1  - 2025/05/26
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.14
DO  - 10.11648/j.edu.20251403.14
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 111
EP  - 125
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.14
AB  - This paper investigates the practical application and effectiveness of a custom generative AI teaching assistant, developed as a GPTs application named DS-ASST, within a "Data Science" course at Kansai University of International Studies. As generative AI technologies like Large Language Models rapidly advance, their potential to transform educational paradigms becomes increasingly evident, offering solutions to challenges inherent in traditional methodologies, particularly in areas requiring personalized learning and large-scale data interaction. This research utilizes the 'Reconsidering Education in the AI Era' framework, focusing on educational AI alignment and redesigning practices to support both instructors and students. The DS-ASST system was developed using Retrieval-Augmented Generation technology to integrate course-specific materials, including lecture notes and textbook content, ensuring responses are contextually relevant and minimizing factual inaccuracies or "hallucinations". We detail the system architecture, iterative prompt design experiments aimed at optimizing educational value, and strategies employed to mitigate technical challenges like hallucination. The system's effectiveness was evaluated through formative assessment across four key dimensions: enhancing teaching preparation efficiency, supporting active student learning, improving data analysis processes, and promoting advanced learning activities. Key findings indicate significant improvements, including a notable reduction in instructor preparation time (approximately 42%) and increased student engagement in discussions (38%) compared to control groups. The AI assistant effectively provided on-demand concept clarification, guided problem-solving, facilitated interaction with complex data, and supported advanced activities like critical evaluation and ethical reasoning. While demonstrating substantial benefits over traditional methods in scalability and flexibility, limitations related to domain specificity, assessment capabilities, and technical requirements were noted. This research assessed the wider ramifications of generative AI for educational reform, based on its practical implementation in education. It specifically considered the changing roles of teachers, developments in assessment techniques, and the necessity of ethical literacy. The study also outlined potential future research, emphasizing hybrid teaching approaches and the formulation of sound ethical guidelines.

VL  - 14
IS  - 3
ER  -

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