Seminar is taught at the University of Vienna starting with summer semester 2024.
Aims, content and method of the course
Background
Artificial intelligence (AI) is currently the subject of much discussion. It can be assumed that AI will influence everyday school life. Be it that data collected by learning platforms is automatically analysed and any interventions are initiated or that available tools are used to prepare content or …
The field is dynamic and growing, the possibilities are great, but there are still limits and challenges. For example, the use of AI can also lead to biased results and discrimination. At the same time, there is an opportunity for the use of AI or AI-supported applications to facilitate individualised and inclusive teaching.
Content
Students explore the connection between AI and inclusive education under the guidance of the course instructor. Based on theoretical input (through texts and short presentations) on inclusive education and AI, students will work in groups to examine selected AI applications that can be used for lesson design.
Using the DORIT model (Dindler et al. 2023), they will analyse what the respective system was developed for and for whom, in order to then work out how the selected AI can – or cannot – be used in inclusive classroom design. The resources of the Computational Empowerment Lab (http://ce-lab.univie.ac.at/) are utilised for this purpose.
Objectives
After successfully completing the seminar …
… students will better understand the possibilities and limitations of AI for inclusive education.
… have tools in their hands to test future/other AI-using applications for their suitability for inclusive classroom design.
… have a better understanding of the interplay between educational inequalities and educational technologies as well as strategies for recognising and preventing possible negative effects.
… have expanded their knowledge and skills in inclusive classroom design.
Methods
- Literature review
- Analysis of existing/used AI applications (e.g. learning platforms, generative AIs, …)
- Documentation of the learning process and the results of the analyses
- Preparation of the results in a short guide for the use of the analysed technology
- Individual reflections
- Peer feedback