Critical_Thinking

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. ...

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.” ...

Notebook LM is Google’s Note-taking and Reseach Assistant tool based on the Gemini 2.0 platform (as of the time of writing, pi-day (March 14th) 2025). DTU have no licenses to the tool and have not evaluated it’s risk and potential applicability. Some other services, like ChatGPT, Copilot or Le Chat are general tools that have been trained on vast amounts of data, and therefore embed a lot of general knowledge, although they may additionally be able to search for, and include, more recent information from “the internet” in the replies. ...

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]. ...