AI Literacy and Professional Development

A published guide addressed to students positions generative AI as a producer of drafts rather than an authority. It teaches prompt construction from a minimal three-part form up to a five-part template, maps use onto the levels of Bloom's taxonomy, and closes with four practices: withhold identifying data, verify critical claims against at least two sources, treat the course instruction as binding, and state the purpose for which the tool was used.

A module on critical AI literacy for learning was offered to students from autumn 2025. Placing the subject inside a module rather than a workshop makes participation part of study rather than an optional addition to it.

Respondents in every group attribute improved results to their own learning: prompts have become more specific and goal-directed, extended exchanges are sustained more deliberately, and use has moved from a single tool to a set of tools selected for particular purposes. Explaining a task to the model is described as constituting a learning experience in itself.

Where formal training is absent or limited, competence is acquired through colleagues, peer networks, self-paced material and practical use. The channels differ by role: staff describe internal knowledge sharing and consultation with colleagues, while for students public video and social media are the most common route. The report finds that informal learning acts as a complement to formal provision rather than as a substitute for it.

The staff counterpart of the student guide addresses academics and researchers. On the teaching side it covers learning outcomes, operations inside the learning management system, assessment and rubrics, accessibility and inclusion. On the research side it covers preparation for literature review, structured reading notes, translation, method and reporting checklists, and reference verification. Data minimisation, disclosure and institutional policy compliance are treated as obligations rather than recommendations.

Resources developed for educators and learners include a course on AI in teaching hosted in the learning management system, an experimentation environment, and a regional hub whose materials teachers can integrate into their own courses. Delivering staff development inside the same system staff already teach in removes one barrier to participation.

A workspace of materials on the basics of artificial intelligence in university work was published for teachers, researchers and support staff in 2025 and is updated as new material appears. A living workspace answers a difficulty that fixed guidance does not, since the tools change faster than documents are revised.

The vice-rectorate for educational innovation runs a training and awareness programme built on several pillars, accompanied by guides for generalists, teachers and students and by courses for teaching, administrative and service staff. Locating the programme in a vice-rectorate gives it a budget line and a permanent owner.

Germany funds specific projects to expand research and knowledge in AI, among them the AI Campus platform. The partnership records it first as a national practice and later as one of four federal initiatives, with a regional hub bundling transfer and networking in North Rhine-Westphalia.