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This chapter explores the transformative role of Natural Language Processing (NLP) in reshaping higher education research (HER) through both methodological innovation and epistemological reconfiguration. It examines how NLP enables scalable, data-intensive analysis across humanities and social sciences (HSS), expanding capacities for literature synthesis, hypothesis generation, and thematic exploration. Using bibliometric analysis and case studies, the chapter demonstrates NLP’s growing application in HER, from longitudinal topic modelling of dissertation abstracts to AI-led experimental design and internationalisation studies. It outlines the epistemic shift from traditional, truth-based inquiry to probabilistic, pattern-based reasoning, highlighting tensions between prediction and understanding, interpretability, and algorithmic bias. Ethical challenges, including explainability, fairness, and human–machine collaboration, are critically examined. Ultimately, the chapter advocates for a pluralistic, interdisciplinary approach that integrates computational tools with humanistic values to responsibly navigate the evolving HER landscape.

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Reconfiguring Inquiry: The Epistemic and Methodological Impacts of NLP in Higher Education Research

  • 章节

  • 发表日期：

    2026年

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  • Catherine Yuan Gao,
  • Xuechun Wang,
  • Kexin Yu

摘要

This chapter explores the transformative role of Natural Language Processing (NLP) in reshaping higher education research (HER) through both methodological innovation and epistemological reconfiguration. It examines how NLP enables scalable, data-intensive analysis across humanities and social sciences (HSS), expanding capacities for literature synthesis, hypothesis generation, and thematic exploration. Using bibliometric analysis and case studies, the chapter demonstrates NLP’s growing application in HER, from longitudinal topic modelling of dissertation abstracts to AI-led experimental design and internationalisation studies. It outlines the epistemic shift from traditional, truth-based inquiry to probabilistic, pattern-based reasoning, highlighting tensions between prediction and understanding, interpretability, and algorithmic bias. Ethical challenges, including explainability, fairness, and human–machine collaboration, are critically examined. Ultimately, the chapter advocates for a pluralistic, interdisciplinary approach that integrates computational tools with humanistic values to responsibly navigate the evolving HER landscape.