Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines

Zhihua Zhang

doi:doi:10.11648/j.edu.20251403.12

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Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines

Zhihua Zhang^*

Published in Education Journal (Volume 14, Issue 3)

Received: 25 March 2025 Accepted: 30 April 2025 Published: 14 May 2025

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Abstract

The rapid advancement of Generative AI (GenAI) presents both opportunities and challenges for higher education. Traditional educational methods often struggle with processing large datasets and facilitating real-time interactions, areas where AI offers potential solutions. This paper examines the development, application, and evaluation of a custom GPTs-based AI teaching assistant, the DS-ASST app, specifically designed for Data Science education within a university liberal arts context. Leveraging Retrieval-Augmented Generation (RAG) technology integrated with course-specific materials, the system aims to enhance both instructor efficiency and student learning experience. The study evaluates the AI assistant's impact across four key areas: teaching preparation efficiency, active learning support, data analysis process enhancement, and the promotion of advanced learning activities. Findings indicate significant improvements in instructor workflow, allowing for more focus on pedagogical refinement rather than routine content creation. For students, the tool provided on-demand concept clarification, guided problem-solving, and personalized learning path suggestions, fostering self-directed learning and engagement, particularly with complex data analysis tasks. The system also aided in developing practical data analysis skills through workflow guidance and interpretation support. Technical challenges inherent in using Large Language Models (LLMs), such as optimizing prompt design for educational relevance and mitigating the risk of AI "hallucinations," were addressed through systematic experimentation and the integration of RAG with a verified knowledge base. Furthermore, the paper discusses the broader implications of GenAI in education, including the redefinition of instructor roles and the evolution of assessment methods. At the same time, based on the implementation experience and existing frameworks, the study proposes practical institutional guidelines and checklists for the ethical and effective use of GenAI by both faculty and students in university settings, emphasizing academic integrity, critical thinking, and AI literacy.

Published in	Education Journal (Volume 14, Issue 3)
DOI	10.11648/j.edu.20251403.12
Page(s)	88-102
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

LLM, Generative AI, GPTs, Guidelines for Generative AI in Education, 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, pp1-11. 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
[4]	Japan Inter-University Consortium for Mathematics, Data Science and AI Education, Model curriculum for mathematics, data science, and AI (literacy level) - Cultivating data thinking - (revised February 22, 2024), http://www.mi.u-tokyo.ac.jp/consortium/pdf/model_literacy_20240222.pdf [Accessed 25 May 2024]
[5]	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]
[6]	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]
[7]	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]
[8]	Genshiro Kitagawa, Akimichi Takemura. Data Science as Liberal Arts, 2nd. Tokyo: Kodansha; 1-229, 2022.
[9]	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.
[10]	Hiromi Takahashi, Kazuhiko Sekita. An attempt to compare the characteristics of representative AI engines using reflection analysis of first-year university students; Japan Education Conference, Committee 11, First-year education (1), 189-190, 2024.
[11]	OpenAI. GPT-4 Technical Report, https://cdn.openai.com/papers/gpt-4.pdf 2023 1-100.
[12]	AI achieves silver-medal standard solving International Mathematical Olympiad problems, https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ 2024. [Accessed 19 October 2024]
[13]	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
[14]	Japan Inter-University Consortium for Mathematics, Data Science and AI Education, Model curriculum for mathematics, data science, and AI (applied basic level) - AI x data utilization practice - (revised February 22, 2024), http://www.mi.u-tokyo.ac.jp/consortium/pdf/model_ouyoukiso_20240222.pdf [Accessed 25 December 2024]

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APA Style

Zhang, Z. (2025). Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Education Journal, 14(3), 88-102. https://doi.org/10.11648/j.edu.20251403.12

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ACS Style

Zhang, Z. Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Educ. J. 2025, 14(3), 88-102. doi: 10.11648/j.edu.20251403.12

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AMA Style

Zhang Z. Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Educ J. 2025;14(3):88-102. doi: 10.11648/j.edu.20251403.12

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@article{10.11648/j.edu.20251403.12,
  author = {Zhihua Zhang},
  title = {Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {88-102},
  doi = {10.11648/j.edu.20251403.12},
  url = {https://doi.org/10.11648/j.edu.20251403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.12},
  abstract = {The rapid advancement of Generative AI (GenAI) presents both opportunities and challenges for higher education. Traditional educational methods often struggle with processing large datasets and facilitating real-time interactions, areas where AI offers potential solutions. This paper examines the development, application, and evaluation of a custom GPTs-based AI teaching assistant, the DS-ASST app, specifically designed for Data Science education within a university liberal arts context. Leveraging Retrieval-Augmented Generation (RAG) technology integrated with course-specific materials, the system aims to enhance both instructor efficiency and student learning experience. The study evaluates the AI assistant's impact across four key areas: teaching preparation efficiency, active learning support, data analysis process enhancement, and the promotion of advanced learning activities. Findings indicate significant improvements in instructor workflow, allowing for more focus on pedagogical refinement rather than routine content creation. For students, the tool provided on-demand concept clarification, guided problem-solving, and personalized learning path suggestions, fostering self-directed learning and engagement, particularly with complex data analysis tasks. The system also aided in developing practical data analysis skills through workflow guidance and interpretation support. Technical challenges inherent in using Large Language Models (LLMs), such as optimizing prompt design for educational relevance and mitigating the risk of AI "hallucinations," were addressed through systematic experimentation and the integration of RAG with a verified knowledge base. Furthermore, the paper discusses the broader implications of GenAI in education, including the redefinition of instructor roles and the evolution of assessment methods. At the same time, based on the implementation experience and existing frameworks, the study proposes practical institutional guidelines and checklists for the ethical and effective use of GenAI by both faculty and students in university settings, emphasizing academic integrity, critical thinking, and AI literacy.
},
 year = {2025}
}

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TY  - JOUR
T1  - Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines

AU  - Zhihua Zhang
Y1  - 2025/05/14
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.12
DO  - 10.11648/j.edu.20251403.12
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 88
EP  - 102
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.12
AB  - The rapid advancement of Generative AI (GenAI) presents both opportunities and challenges for higher education. Traditional educational methods often struggle with processing large datasets and facilitating real-time interactions, areas where AI offers potential solutions. This paper examines the development, application, and evaluation of a custom GPTs-based AI teaching assistant, the DS-ASST app, specifically designed for Data Science education within a university liberal arts context. Leveraging Retrieval-Augmented Generation (RAG) technology integrated with course-specific materials, the system aims to enhance both instructor efficiency and student learning experience. The study evaluates the AI assistant's impact across four key areas: teaching preparation efficiency, active learning support, data analysis process enhancement, and the promotion of advanced learning activities. Findings indicate significant improvements in instructor workflow, allowing for more focus on pedagogical refinement rather than routine content creation. For students, the tool provided on-demand concept clarification, guided problem-solving, and personalized learning path suggestions, fostering self-directed learning and engagement, particularly with complex data analysis tasks. The system also aided in developing practical data analysis skills through workflow guidance and interpretation support. Technical challenges inherent in using Large Language Models (LLMs), such as optimizing prompt design for educational relevance and mitigating the risk of AI "hallucinations," were addressed through systematic experimentation and the integration of RAG with a verified knowledge base. Furthermore, the paper discusses the broader implications of GenAI in education, including the redefinition of instructor roles and the evolution of assessment methods. At the same time, based on the implementation experience and existing frameworks, the study proposes practical institutional guidelines and checklists for the ethical and effective use of GenAI by both faculty and students in university settings, emphasizing academic integrity, critical thinking, and AI literacy.

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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Plain Text BibTeX RIS

APA Style

Zhang, Z. (2025). Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Education Journal, 14(3), 88-102. https://doi.org/10.11648/j.edu.20251403.12

Copy | Download

ACS Style

Zhang, Z. Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Educ. J. 2025, 14(3), 88-102. doi: 10.11648/j.edu.20251403.12

Copy | Download

AMA Style

Zhang Z. Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines. Educ J. 2025;14(3):88-102. doi: 10.11648/j.edu.20251403.12

Copy | Download

@article{10.11648/j.edu.20251403.12,
  author = {Zhihua Zhang},
  title = {Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines
},
  journal = {Education Journal},
  volume = {14},
  number = {3},
  pages = {88-102},
  doi = {10.11648/j.edu.20251403.12},
  url = {https://doi.org/10.11648/j.edu.20251403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.edu.20251403.12},
  abstract = {The rapid advancement of Generative AI (GenAI) presents both opportunities and challenges for higher education. Traditional educational methods often struggle with processing large datasets and facilitating real-time interactions, areas where AI offers potential solutions. This paper examines the development, application, and evaluation of a custom GPTs-based AI teaching assistant, the DS-ASST app, specifically designed for Data Science education within a university liberal arts context. Leveraging Retrieval-Augmented Generation (RAG) technology integrated with course-specific materials, the system aims to enhance both instructor efficiency and student learning experience. The study evaluates the AI assistant's impact across four key areas: teaching preparation efficiency, active learning support, data analysis process enhancement, and the promotion of advanced learning activities. Findings indicate significant improvements in instructor workflow, allowing for more focus on pedagogical refinement rather than routine content creation. For students, the tool provided on-demand concept clarification, guided problem-solving, and personalized learning path suggestions, fostering self-directed learning and engagement, particularly with complex data analysis tasks. The system also aided in developing practical data analysis skills through workflow guidance and interpretation support. Technical challenges inherent in using Large Language Models (LLMs), such as optimizing prompt design for educational relevance and mitigating the risk of AI "hallucinations," were addressed through systematic experimentation and the integration of RAG with a verified knowledge base. Furthermore, the paper discusses the broader implications of GenAI in education, including the redefinition of instructor roles and the evolution of assessment methods. At the same time, based on the implementation experience and existing frameworks, the study proposes practical institutional guidelines and checklists for the ethical and effective use of GenAI by both faculty and students in university settings, emphasizing academic integrity, critical thinking, and AI literacy.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Integrating Generative AI in Higher Education: Practical Applications and Institutional Guidelines

AU  - Zhihua Zhang
Y1  - 2025/05/14
PY  - 2025
N1  - https://doi.org/10.11648/j.edu.20251403.12
DO  - 10.11648/j.edu.20251403.12
T2  - Education Journal
JF  - Education Journal
JO  - Education Journal
SP  - 88
EP  - 102
PB  - Science Publishing Group
SN  - 2327-2619
UR  - https://doi.org/10.11648/j.edu.20251403.12
AB  - The rapid advancement of Generative AI (GenAI) presents both opportunities and challenges for higher education. Traditional educational methods often struggle with processing large datasets and facilitating real-time interactions, areas where AI offers potential solutions. This paper examines the development, application, and evaluation of a custom GPTs-based AI teaching assistant, the DS-ASST app, specifically designed for Data Science education within a university liberal arts context. Leveraging Retrieval-Augmented Generation (RAG) technology integrated with course-specific materials, the system aims to enhance both instructor efficiency and student learning experience. The study evaluates the AI assistant's impact across four key areas: teaching preparation efficiency, active learning support, data analysis process enhancement, and the promotion of advanced learning activities. Findings indicate significant improvements in instructor workflow, allowing for more focus on pedagogical refinement rather than routine content creation. For students, the tool provided on-demand concept clarification, guided problem-solving, and personalized learning path suggestions, fostering self-directed learning and engagement, particularly with complex data analysis tasks. The system also aided in developing practical data analysis skills through workflow guidance and interpretation support. Technical challenges inherent in using Large Language Models (LLMs), such as optimizing prompt design for educational relevance and mitigating the risk of AI "hallucinations," were addressed through systematic experimentation and the integration of RAG with a verified knowledge base. Furthermore, the paper discusses the broader implications of GenAI in education, including the redefinition of instructor roles and the evolution of assessment methods. At the same time, based on the implementation experience and existing frameworks, the study proposes practical institutional guidelines and checklists for the ethical and effective use of GenAI by both faculty and students in university settings, emphasizing academic integrity, critical thinking, and AI literacy.

VL  - 14
IS  - 3
ER  -

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