Exploring the role of AI in higher education: a natural language processing analysis of emerging trends and discourses

Abstract

Purpose This study aims to analyze the most frequently discussed topics in the scientific discourse on artificial intelligence (AI) in higher education using Natural Language Processing (NLP) techniques. Design/methodology/approach This paper analyzes 52 peer-reviewed articles published between 2017 and 2024, utilizing NLP techniques to identify prevalent unigrams, bigrams and trigrams related to AI in higher education. Findings The analysis identifies an emerging concern with utilizing AI tools to enhance educational processes, with “Higher education,” “artificial intelligence” and “generative AI” becoming ubiquitous terms in use. LLM and ChatGPT represent types of technology that evoke potential for personalized learning and enhanced practice in instruction. Research limitations/implications In review studies, samples with a post-secondary educational background usually restrict generalizability to school environments. Future studies can examine the long-term consequences of AI technology in extended academic environments, longitudinal studies and educational environments. Practical implications The frequency patterns from our analysis offer essential insights for educators and administrators regarding curriculum development and teaching practices. The high occurrence of terms like “artificial intelligence” (1,193 times) and “higher education” (824 times) highlights the need for incorporating AI literacy into curricula. This integration should include guidelines for responsible AI use and training programs for faculty. The frequent mentions of “teaching learning” (226 times) and “AI education” (319 times) highlight important implications for teaching practices. Educational institutions must establish frameworks that blend traditional methods with AI-enhanced strategies, including assessment plans that consider AI tools while upholding academic integrity. Additionally, institutions should prioritize investment in AI infrastructure and support systems. Social implications Our findings highlight important societal implications beyond education. The frequency analysis reveals concerns about educational equity, including disparities in access to AI-enhanced education, digital literacy gaps and economic barriers to adopting AI tools. Addressing these issues is vital to prevent the worsening of social inequalities. Additionally, our results emphasize the need for workforce development. Educational institutions should focus on equipping students with the AI competencies that employers demand and bridging the gap between academic training and industry needs. The policy implications of our findings are equally significant. Our analysis suggests the need for educational policies that address AI integration while establishing clear guidelines for ethical AI use in academic settings. These policies should include standards for AI tool evaluation and implementation to guide institutions' adoption decisions. The economic impact of these developments is also noteworthy, as our results indicate the potential for enhanced workforce preparedness through AI-integrated education, improved educational efficiency through automation and new opportunities for educational technology development. Originality/value This study contributes to the field by providing an overview of prominent trends in AI within higher education, discussing the practical application, future research opportunities, and challenges associated with the responsible and effective use of AI in education.

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