Practice Papers

Document Type

Conference Paper

Abstract

This practice paper describes an ongoing insider action research within the EIT InnoEnergy ecosystem. Its goal is to inspire teaching staff from the seven EIT InnoEnergy double degree Master of Science programmes to integrate Artificial Intelligence (AI) tools and knowledge into their courses based on joint learning. This insider action research runs from 2023 to the end of 2024. In late 2022, a problem statement of ‘AI tools for Education’ was identified by EIT InnoEnergy teachers as being crucial for their future learning and teaching processes. To align the needs of teaching staff with the complexity of emerging AI tools, a decision was made to plan a hybrid insider action research method. The outcome of this research will be twofold: one resulting in an AI toolkit covering three teaching staff needs, and two getting a better understanding of the processes involved in taking up a learning innovation at different engineering partner universities spread across Europe within the EIT InnoEnergy ecosystem. This paper shares the first phases of the insider action research and an overview of the individual AI initiatives taken by teaching staff at different partner universities that is the result of a first qualitative data analysis coming from initiatives shared by the insiders (i.e., teaching staff). Action research methodology was chosen to inspire teaching staff to take an investigative and experimental attitude to the new AI technologies while allowing all actors to support each other and grow towards an AI integration in courses and curricula.

DOI

https://doi.org/10.21427/9VCN-QW52

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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