This page contains a number of resources that may be relevant to AI at DTU. Some items may contain a links to external resources; this does not mean that DTU endorse the resource as such but merely that it may be relevant to consider or be inspired by.
If you are aware of a resource that have wide relevance for others at DTU, please reach out to the AI Group to have it considered for inclusion.
Overview Critical thinking is a set of complex cognitive processes, see, e.g., the textbook by Moore and Parker for definitions, theory and analysis[1]. Here we provide a basic list of checkpoints to evaluate academic products with a specific focus on their external input. The need for systematic critical thinking is amplified by the advent of generative AI that allows massive generation of academic-like products. Potential use cases could be critical (self-)evaluation of student reports, scientific papers, or peer review reports. We thank the authors of the Calling Bullshit curriculum[2] for inspiration. ...
The EU AI Act When considering applications of Generative AI, it is important to consider the European Union AI Act, which is also provided as a high-level summary. Of particular importance is that the AI Act classifies AI according to its risk in four different categories: Unacceptable risk (which is prohibited) High-risk systems (which is strongly regulated) Limited risk systems (still subject to e.g. transparency obligations) Minimal risk (which is unregulated) With some exceptions, AI systems that determine admission to education, evaluate learning outcomes or monitor prohibited student behaviour under tests are all classified as high-risk systems and need to fulfill a list of requirements that include human oversight, proper design and quality management, risk assessment, data governance and more. High-risk AI systems must undergo rigorous testing, maintain comprehensive documentation, and prioritize user safety and transparency. ...
Modern Day Oracles or Bullshit Machines? is a resource developed by Biology Professor Carl T. Bergstrom and Information Science Professor Jevin D. West that are known from e.g. their prior work Calling Bullshit (Data Reasoning in a Digital Age). The site presents resources that can be helpful in teaching or in reflecting on own use of Generative AI tools. In their own words: “This is not a how-to course for using generative AI. It’s a when-to course, and perhaps more importantly a why-not-to course.” ...
Many of the ChatGPT-like Generative AI services are based on General Pre-trained Transformer (GPT) Large Language Models (LLM) whose main interaction method is prompting. There are many websites dedicated to “prompt engineering” and some contain helpful taxonomies. To get started, you may seek inspiration from the so-called prompt engineering guide (external link) to learn about the differences between zero-vs few-shot prompting, chain-of-thought prompting etc.
The paper “Teaching and testing in the era of text-generative AI: exploring the needs of students and teachers” by Jochim and Lenz-Kesekamp (2025) [1] offer a framework to characterize the use and domestication of Generative AI based on general Domestication Theory [2,3]. The framework may be helpful when assessing the use of Generative AI in courses, teaching, research, administration and related areas. The 4 identified levels are: Familiarisation or Appropriation : Learning about and experimenting with Generative AI in a number of different contexts Objectification: Integrating Generative AI routines into e.g. studying or teaching Incorporation: Use of Generative AI becomes systematic, structured and thought-out in academic work on a regular basis Conversion: Generative AI changes the academic culture These levels bear some resemblance with other models, like the Capability Maturity Model or the Temporality of Experience [4]. ...
An database of public sector use of AI has recently been published (in Danish) at offentlig-ai.dk. This may of course be helpful to understand the pick-up of Generative AI tools elsewhere in the public sector, but the resource also provides an overview of AI related research projects in Denmark, related news and report, podcasts, etc. It is possible to add new projects through a form.
viden.ai is a Danish website that focuses on use of Generative AI in primary school and high schools (gymansium) in Denmark. It presents a number of articles that address background, examples, tools, challenges and is currently updated approximately once a week. It is driven by a team of volunteers and authors, and provide some resources for free and others behind a paywall.