AI Skills for University Teaching – Harnessing Generative AI Tools
The effective use of generative AI in education requires key skills: in this project, we are developing the necessary competencies.
Duration: January 2024 – December 2024
Status: Completed
Educational Level: Tertiary Level
Topic: Artificial Intelligence AI, Digital Skills & Literacy
Keywords: Artificial Intelligence AI, Digital Skills
Initial Situation
Tools such as ChatGPT are dramatically changing the way we learn and teach. These advanced technologies have the potential to significantly improve education, especially in text-based teaching and learning processes. It is therefore very important to integrate such AI tools into higher education in order to promote modern teaching methods, improve the learning experience and increase efficiency of teaching. However, the successful integration of these technologies faces challenges, as both teachers and learners must have the necessary skills, which have not yet been sufficiently defined.
Objectives
With our project, we want to find out exactly what skills are needed to use generative AI tools effectively for teaching and learning purposes. Our goal is to develop a clear competency model that serves as a guide for lecturers, students and educational institutions to specifically promote these new skills.
Method
After an initial literature search, we conduct a Delphi study to collect competencies and validate them with an expert panel.
Results
The result is a competency model which, for practical reasons, is an extension of an established competency model for (university) teachers (DigCompEdu, Redecker, 2017). We enrich it with specific aspects relating to generative AI – e.g. prompting (knowledge & skills), evaluative judgement (knowledge & skills) and competence development (attitude).
Implemented Translation
The results of the Delphi study phases 1-2 were presented at the ECTEL conference 2024 in an interactive workshop, with the result that the findings were verified by experts and concrete conclusions were drawn for teaching and continuing education practice. The course on AI in teaching offered as part of the university didactics course at BFH by Kerstin Denecke builds upon the identified competencies on the one hand and teach these competencies on the other. Finally, the findings will be incorporated into the doctoral project of Isabelle Geppert (doctoral candidate at the University of Bern) and, in the course of this, will also be used in the continuing education practice of the Learning and Development (LEAD) department (University of Bern) under the direction of André Klostermann. Impact was not explicitly measured. When applied, the competency model can help in developing tailored courses for teachers and students.
