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This paper presents a fundamental study on the application of Large Language Models (LLM), such as ChatGPT, for enhancing Institutional Research (IR) practices among new practitioners. Emphasizing the transition to low or no-code methodologies, this study explored the potential of semi-automated analysis and visualization methods to streamline the data handling processes in higher education. With the advancement of LLMs, traditional programming barriers are being dismantled, allowing for more efficient and accessible data analysis. This study particularly investigated the feasibility of utilizing ChatGPT in reducing the programming workload for IR practitioners, which could lead to a substantial transformation in the field. Through empirical experiments and the development of new visualization techniques, the research determined the benefits and practicalities of integrating LLMs into IR, promoting a future where sophisticated data analysis becomes more inclusive and user-friendly for all IR professionals. The findings suggest that embracing such technological tools not only broadens the capabilities of IR practitioners but also strengthens the strategic impact of their role within educational institutions.

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Fundamental Study of Empirical Experiments with New Institutional Research (IR) Practitioners Using Large Language Models (LLM) Such as ChatGPT

  • 会议论文

  • 发表日期：

    2025年

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  • Masayuki Seki,
  • Ikuhiro Noda,
  • Msato Omori,
  • Tomoyuki Sakai,
  • Katsuhiko Murakami,
  • Kunihiko Takamatsu

摘要

This paper presents a fundamental study on the application of Large Language Models (LLM), such as ChatGPT, for enhancing Institutional Research (IR) practices among new practitioners. Emphasizing the transition to low or no-code methodologies, this study explored the potential of semi-automated analysis and visualization methods to streamline the data handling processes in higher education. With the advancement of LLMs, traditional programming barriers are being dismantled, allowing for more efficient and accessible data analysis. This study particularly investigated the feasibility of utilizing ChatGPT in reducing the programming workload for IR practitioners, which could lead to a substantial transformation in the field. Through empirical experiments and the development of new visualization techniques, the research determined the benefits and practicalities of integrating LLMs into IR, promoting a future where sophisticated data analysis becomes more inclusive and user-friendly for all IR professionals. The findings suggest that embracing such technological tools not only broadens the capabilities of IR practitioners but also strengthens the strategic impact of their role within educational institutions.