Walk into almost any university department today and you’ll likely find colleagues already using GenAI for all kinds of purposes – drafting lesson plans, generating content, supporting students, even assisting with assessment. GenAI tools are opening up possibilities that would have seemed far-fetched only a few years ago. At the same time, important questions arise about ethics, academic integrity, transparency, data protection, and what it means to be an educator in an AI-enabled classroom.
TaLAI project consortium of European partners – FAU Erlangen-Nürnberg (Germany), Fachhochschule Südwestfalen (Germany), Media & Learning Association, and the University of Amsterdam (The Netherlands) – set out to give educators a space to work through these questions, through reflection and real examples. The result is A Hands-on Guide to Ethical AI in Higher Education, an open online course built specifically for those working in higher education. Through practical activities, real-world examples and reflective dialogue, it invites participants to explore both what GenAI makes possible in university teaching, and where its limits lie.
Guiding educators through the ADDIE Framework
The course follows the ADDIE instructional design model, consisting of five phases (Analyse, Design, Develop, Implement and Evaluate), providing a framework through which educators can examine how GenAI tools can be used across the full cycle of course development and delivery. Throughout the course, participants are accompanied by the TaLAI Guy, who helps guide the learning journey, while the Ethics Guy and Knowledge Guy prompt reflection on ethical, pedagogical and epistemological questions.
Analyse: understanding learners, contexts and policies
The Analyse phase is about understanding your learners, your institutional context, and the wider policy environment. Participants start by exploring how customised AI tools can help build learner personas — a way of reflecting on the different needs, expectations and challenges students bring to the classroom. From there, the section turns to the EU AI Act, examining what it means for higher education and prompting a closer look at questions of transparency, human oversight and risks. Finally, participants are invited to turn that lens on their own institution: reviewing local policies, data protection requirements, and existing guidance on GenAI.
Design: creating aligned learning experiences
Building on this foundation, the Design phase explores how learning outcomes, teaching activities and assessment can be aligned effectively. Drawing on the principle of constructive alignment, participants consider how courses can be designed to support meaningful learning while responding to the growing presence of AI technologies. A custom AI lesson-planning tool demonstrates how GenAI can assist in generating ideas for learning activities and assessments. However, equal emphasis is placed on critical evaluation and the continued importance of professional judgement. The section also examines contemporary debates about assessment, academic integrity, and the future of written assignments in an era when AI-generated content is readily available.
Develop: building learning materials responsibly
The Develop phase turns attention to the creation of learning materials. Examples show how GenAI can support tasks such as drafting content, generating visuals, producing multimedia resources and developing learning activities. At the same time, participants explore the limitations of large language models and reflect on the responsibilities that remain with educators. The section highlights the importance of ensuring that AI-generated materials are accurate, appropriate, and aligned with learning objectives, rather than being adopted simply because the technology is available.
Implement: using AI in teaching practice
The Implementation phase showcases practical examples of GenAI already at work in educational settings. Case studies show colleagues using AI in different ways – as a debating partner, project partner, personal learning assistant, research assistant, or laboratory assistant – illustrating the range of approaches to AI-enhanced learning already being tried. Across all these examples, one message comes through clearly: GenAI works well only when it’s used with a clear pedagogical purpose, critical engagement, and ongoing human supervision. With this in mind, participants are then invited to consider how similar approaches might be adapted to their own discipline and institution.
Evaluate: reflecting and improving
Finally, the Evaluation phase looks at how educators can reflect on and improve their teaching. Evaluation is treated not as a final step, but as an ongoing process shaped by feedback, reflection and evidence of student learning. The section offers structured steps for evaluating GenAI’s role across course design, teaching practice and the student experience – helping educators see where it can meaningfully support learning. It also introduces a Custom GPT that simulates feedback conversations with students, letting educators practise dialogic feedback and become more aware of their own feedback strategies. Through this, participants consider how their feedback shapes learning, student agency and the wider learning environment.
Building your own approach to GenAI use in teaching
As GenAI becomes increasingly embedded in higher education, the question is no longer whether educators will encounter AI in their teaching context, but how they will respond to it. While these technologies offer new possibilities for supporting learning, they also require careful consideration of different aspects such as academic integrity, transparency, assessment and professional responsibility.
A Hands-on Guide to Ethical AI in Higher Education does not offer simple answers to these complex questions. Instead, it provides a structured space for exploration, experimentation, and reflection. Through practical examples, critical discussion and opportunities for hands-on engagement, the course encourages educators to develop their own informed and context-sensitive approaches to GenAI use in teaching and learning.
Funded by the European Union. Views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the DAAD National Agency. Neither the European Union nor the DAAD National Agency can be held responsible for them. Project Number: 2023-1-DE01-KA220-HED-000153155