Procedures for designing and assessing responsible experiments with AI in education

AI-based interventions could enhance learning by personalization, improve teacher effectiveness, or optimize educational processes. However, they could also have unintended or unexpected side-effects, such as undermining learning by enabling procrastination, or reducing social interaction by individualizing learning processes. Responsible experiments are required to map both the potential benefits and the side-effects. Current procedures used to screen experiments by ethical review boards do not take the specific risks and dilemmas that AI poses into account. Previous studies identified sixteen conditions that can be used to judge whether trials with experimental technology are responsible. These conditions, however, were not yet translated into practical procedures, nor do they distinguish between the different types of AI applications and risk categories. In this brown-paper session, Izaak presents how those conditions could be further specified into procedures that could help facilitate and organize responsible experiments with AI, while differentiating for the different types of AI applications based on their level of automation. During the presentation four procedures will be proposed and discussed with the attendants: 1) A process of gradual testing 2) Risk- and side-effect detection 3) Explainability and severity, and 4) Democratic oversight. These procedures can be used by researchers, review boards, and research institutions to responsibly experiment with AI interventions in educational settings.