The Evolving Media Discourse on ChatGPT and Higher Education
For educators and policymakers in higher education, this paper reveals a media bias toward institutional narratives over long-term societal impacts, which may influence how AI is integrated into teaching and learning.
This study analyzes 198 U.S. news articles from November 2022 to October 2024 to trace how media narratives about ChatGPT in higher education evolved, finding that coverage focused on institutional responses with positive sentiment, while broader ethical and social implications were less consistently addressed.
This research full paper examines how news media have been instrumental in creating specific narratives about generative AI applications, especially ChatGPT, in higher education, and how these narratives have changed over time. The introduction of emerging technologies in higher education is driven not only by their technological affordances but also by the narratives built around their perceived value, risks, and possibilities. Therefore, understanding how news media narratives contribute to sociotechnical imaginaries - the imagined futures of technology use that institutions and educators inherit - is important for evaluating ChatGPT's role in teaching and learning, including engineering education. Through temporal and sentiment analyses of 198 U.S. news articles from November 2022 to October 2024, we traced the evolving narratives surrounding generative AI and the use of ChatGPT in higher education. We found that the media discourse largely centered on institutional responses, with policy changes and teaching practices showing the most consistent presence and positive sentiment over time. Conversely, coverage of topics such as human-centered learning, the job market, and skill development appeared more sporadic, with initially uncertain portrayals gradually shifting toward cautious optimism. Media sentiment toward ChatGPT's role in college admissions remained predominantly negative. Our findings suggest that media narratives prioritize institutional responses to generative AI over the long-term, broader ethical, social, and labor-related implications. This imbalance is especially relevant to engineering and computing education, where students must be prepared not only to use AI tools but also to critically evaluate the broader sociotechnical consequences of AI as future designers of AI-enabled technologies.