Promoting AI Literacy at universities: How can the integration of AI learning opportunities succeed?

Generative AI is fundamentally transforming university teaching and presenting educators with new challenges. This article shows how universities can foster AI literacy and support educators on the path into an AI-shaped educational world through the targeted use of AI learning opportunities, curation and strategic anchoring.
 

It is a Tuesday morning, shortly before 9 o’clock. A professor of engineering is sitting in front of her computer and having a script for the lecture that is about to take place generated. One floor above, her colleague from computer science is currently preparing a new exam format, because a multiple-choice-based exam could be cracked in seconds with generative AI. In the library, a doctoral candidate is wondering how she is supposed to convey to her students in the seminar what “independent knowledge generation” actually means and why it is a skill worth learning when a language model can deliver print-ready summaries at the push of a button.

The three scenes point to a new reality of university teaching: challenges that would have been hardly conceivable just a few years ago. With impressive speed, new tools are changing how teaching can be conceived, designed and delivered. Generative AI not only calls independent writing and traditional forms of assessment into question, but also central principles of academic work at universities. And in parallel, it is fundamentally changing how knowledge comes into being at all, how it is evaluated and how it is communicated. Educators are right in the middle of this upheaval. The aim of offering students reliable, fair and future-proof teaching meets a day-to-day reality in which there is often neither time nor clear answers or structures available for this change.

Scenes like these are currently playing out at countless universities. They show what it is fundamentally about when we talk about AI competences in teaching: about specific and often complex situations in educators’ everyday work and the question of how necessary knowledge and future competences can be fostered and translated into confident action.
 

The new key competence “AI Literacy”

Whether generative language models in academic writing, learning analytics systems in student advisory services or AI-supported diagnostic tools in medicine: AI applications now shape almost every academic discipline. This also changes what universities teach and what competences they need to equip students with.

This is exactly where the term AI literacy comes in: the ability to understand AI systems, critically assess their possibilities and limitations, and use them responsibly in one’s own disciplinary and work context. The AI Campus engages extensively with the topic. You can find further information on our AI Literacy topic page.

However, AI literacy does not only describe individual competence requirements. AI literacy is also a foundation for the competence of educational institutions to create framework conditions for a reflective, responsible and learning-promoting use of AI. While students often use AI tools intuitively and with a willingness to experiment, educators face the task not only of mastering them but also of placing them in a didactic context: When does the use of AI really promote learning, and when does it undermine it? Where is the boundary between meaningful support and academic dishonesty? 

In addition, there is a structural challenge: AI literacy is not static knowledge that you acquire once and then possess. The underlying systems continue to develop at a pace that can hardly be kept up with by traditional continuing-education cycles. This results in the task for universities and continuing-education institutions to provide both learning opportunities that strengthen AI competences and the ability to navigate a dynamic field.
 

A consortium for broad-based strengthening of AI competences: the project “Strengthening AI competences at universities”

How such concerns can be structurally anchored is shown by the project “Strengthening AI competences at universities”. Funded by the Federal Ministry of Research, Technology and Space (BMFTR) as part of the Hightech Agenda Germany, the consortium project runs from January 2026 to March 2029. The project is coordinated by the Stifterverband and implemented together with eight university and research partners: RWTH Aachen University, Humboldt University of Berlin, the German Research Centre for Artificial Intelligence (DFKI), Baden-Württemberg Cooperative State University (DHBW), Heinrich Heine University Düsseldorf, the FernUniversität in Hagen, Technical University of Munich, and the University of Tübingen. The consortium is complemented by associated partner universities and networks.

With the EU AI Act, binding requirements have been created for the competent and responsible use of AI in higher education. The project responds with a clear aim: to promote nationally scalable, open educational resources and learning technologies that strategically and systemically anchor AI competences sustainably in study programmes, teaching and administration at universities. In this way, more than 500,000 people are to be reached with free learning opportunities.
 

From practice: How university partners reach educators and learners

What can the use and integration of digital teaching and learning content in a higher-education context look like in concrete terms? Not only do the contents used vary from one university to another, but so do the channels through which educators are reached. The experiences within the consortium show that the question is less about the “right” format and much more about suitable points of connection and transfer pathways within existing university structures.

At the DHBW (Baden-Württemberg Cooperative State University), the threads come together through personal exchange: In regular staff meetings and the annual lecturers’ conference, attention is drawn to the university-specific AI Campus offerings and AI-relevant topics are discussed – in future, short “nuggets” are also desired as conversation starters. So far, the AI Campus courses have been used mainly by first-semester students and students at the beginning of their studies, for example in the self-study course “Academic Work with AI 2.0”. At the same time, educators also use the platform themselves for continuing professional development and pass on their knowledge as multipliers.

At Humboldt University of Berlin, the approach is more curated: project staff select suitable AI Campus offerings, for example on AI fundamentals, prompting or the EU AI Act specifically for the Career Centre’s course selection and integrate them directly into Moodle courses via an LTI interface. In blended formats, they structure the time between on-campus sessions as self-study phases on the AI Campus. In future, the BMFTR project will also develop its own continuing-education formats specifically for educators and student teachers.

The FernUniversität in Hagen goes one step further when it comes to course integration and anchors learning opportunities and even entire learning paths in the curriculum: Over 30 AI Campus learning opportunities there make up the certificate module “Learning about AI in education” in the Bachelor’s programme in Educational Science. In addition, a separate module on “Didactics with AI” is being created for educators, accompanied by individual didactic advice and supplemented by mandatory AI training for all university staff.

At RWTH Aachen, finally, a pattern emerges that many project partners are familiar with: offerings such as the long-established “Prompt Lab” work best when they are not presented as an isolated add-on, but are embedded in the university’s existing qualification and continuing-education programmes – asynchronously, at one’s own pace, as preparation for or follow-up to face-to-face formats.

A common thread runs through all four exemplary approaches: what is decisive is careful curation of the content and embedding it in structures that are already familiar to educators. Increasingly, the portfolio is being supplemented by the tailored adaptation of openly licensed educational resources and, in some cases, by the complete new production of learning opportunities. The step-by-step integration of existing and future content remains a core task – a challenge that is easier to manage within a consortium. Here, the partners benefit from synergies ranging from regular exchanges of experience to joint pilot series. Always with the same goal: shaping the teaching of tomorrow today.
 

Why the topic is relevant now

The educators imagined as examples at the beginning of the text are not alone in their challenging situation. Many colleagues share precisely these uncertainties regarding future-proof teaching. This is precisely where the opportunity lies to jointly develop new ways of dealing with AI. The examples from Heilbronn, Berlin, Hagen and Aachen show: there is no single, correct way to reach educators. Depending on the context and situation, different approaches that draw on principles such as good curation, linking to established formats and structures, informal conversations and curriculum-embedded, mandatory certificate modules can, in their diversity, strategically unfold their impact.

Projects such as “Strengthening AI competences at universities” make it clear that this task is too complex to be tackled by individual chairs or universities alone. It requires consortia, open platforms and shared experiences to address the complexity of this transformation process. It also requires educators who are willing to continuously develop their practice. At universities across the country, they ask themselves anew every day how dealing with AI can be shaped today and tomorrow and how it can succeed at universities. The answers that emerge from this are not found once or understood as final solutions, but as a shared practice that is continually renegotiated, tested, reflected upon and further developed in the teaching and learning process. And via open learning opportunities on the AI Campus, it can also become visible to others.