Institutional Responses to AI Adoption in Universities Worldwide: the Wxploratory study of a Large Text-based Data Source - University Websites

• In Event: Artificial Intelligence, Digitalization, and Educational Equity

Proposal

This study aims to map institutional responses to AI adoption in universities worldwide by analyzing a large text-based data source: university websites. As a key communication tool, websites reflect an institution’s culture and organizational norms (Fowle & Vassaux, 2017), shaping how universities engage with internal and external audiences.

We employ web scraping techniques to collect AI-related content from the websites of 2,000 universities included in THE rankings. Our analysis focuses on the scope and thematic dimensions of AI-related discussions in university communications. In the first stage, we apply text mining techniques, including keyword extraction and topic modeling using NLP approaches. We then classify and describe university profiles based on AI engagement, incorporating institutional characteristics from databases such as ETER.

The advent of widely available technologies such as electricity, the internet and search engines, has already reshaped higher education many times. Since 2023, generative AI tools have become widely accessible, attracting significant attention across society and fostering the contribution of universities to social innovation. We assume that the nature of genAI-driven changes differs from previous technological challenges faced by the higher education sector. For this study we focus on three assumptions of institutional context related to new technology in higher education.

Firstly, there is the rapid and widespread organic adoption of genAI technologies by users. In the words of technology acceptance models (David, 1987; Teo and Noyes 2011) perceived ease of use, perceived usefulness and perceived enjoyment might positively contribute to genAI technologies usage among students (some empirical evidence Cano and Nanez 2024). This encourages an 'organic' spread across the higher education sector (Frumin, 2024), which institutions must deal with.

Secondly, the market for genAI-integrated applications is thriving - the development of numerous general-purpose tools (like ChatGPT and Claude) as well as specialized applications (e.g. GitHub Copilot, IBM Watson Health); also targeting specific needs in higher education, from teaching and learning tools (like Cramly) to university service management (such as Ivy.ai). Universities are also stimulating the development of new AI models. Although the variety of tools provide a choice, it also forms the chaos of on-going changes, experimenting with and adoption of different new and better AI tools can generate the burden (e.g. in decision-making and all related transactional costs) for organisations.

Thirdly, universities are navigating the complex dynamics between multiple actors - students, faculty, university management and national policy. For national policies AI becomes a strategic issue that calls for varied intervention approaches across different countries, often framed as a “new space race” (Ulnicane et al 2021). Universities are not only shaped by public policy - through regulations, funding mandates, and accountability measures - but also act as active contributors to policy development, especially among elite institutions.

Previous research on universities’ responses to AI highlights key takeaways that provide a foundation for our large-scale data exploration. Korsberg and Elken (2024) show, based on interviews with university leaders, that many institutions remain in a state of hesitation, reluctant to adapt despite pressure from the media, students, and emerging technological trends. Then, universities are actively engaged in the race to regulate AI, responding to shifting national policies (see Smuha, 2021) by developing institutional strategies and guidelines. Recent research on AI-related university policies across the Global North highlights three dominant themes: guidelines for ethical generative AI use, the design of assessments, and training programs for faculty and students to enhance AI literacy (Jin et al., 2025). These themes - ethics and teaching and learning - also emerge as the most prevalent in research publications on AI in higher education (Frumin et al., forthcoming). Moreover, at the institutional level, universities prioritize responding to labor market demands and employer expectations. Analytical reports indicate a global increase in the availability of AI-related educational programs and the number of their graduates (Maslej et al., 2024).

Taking all above mentioned into account and our approach driven by exploratory inquiry, we attempt to address the following questions:
What types of universities fall into the "wait-and-see" category in response to rapidly advancing AI technologies?
To what extent do universities address AI-related topics beyond ethics and teaching and learning? What is the mix of topics that appear on the websites? What typical university profiles do they form?
Which stakeholders do universities prioritize in their AI-related communications through their websites - prospective students, labor market partners, the knowledge and innovation industry, or policymakers?
Given that our sample consists of universities striving for world-class status and the multiversity model, their engagement with AI on institutional websites may reveal multiple social roles - if the adoption of new technologies is framed as a tool for social innovation, knowledge production, or public engagement. This could reflect broader institutional priorities, such as experimenting with learning technologies, collaborating with industry and government, addressing societal challenges, or positioning themselves as key actors in emerging technological landscapes.

Authors

• Daria Platonova, Constructor university

• Isak Frumin, Constructor University Bremen