Tell Me Why I Should (Not) Follow Your Advice: The Role of Explainable AI in Learner and Educator Decision-Making

Description
Explainable artificial intelligence (XAI) has proven its influence on learner’s acceptance and adoption of learning recommendations. The goal of XAI in education, however, is not to « justify » the learning recommendation that an AI-model is generating, but rather to clarify to the learner and educator the model’s inner workings, in a way that allows them to make an informed decision about accepting, modifying, or even rejecting the recommendation. In this webinar, we will dive into the collaborative decision-making process between education stakeholders (namely educators and learners) and AI, focusing on how explainable recommendations can empower learners to have more agency over the learning decisions, and more control over the parameters that AI-models use to personalize a learning recommendation for them. An example implementation of these concepts will be presented from our recent research in the field of vocational education and training.

Guest expert
Hasan Abu-rasheed is a research associate and doctoral researcher in the University of Siegen, Germany. With his teaching experience, and his background in computer science and mechatronics engineering, he positioned his research within the fields of technology enhanced learning (TEL) and AI in education (AIED). His main focus is developing explainable AI (XAI) approaches for supporting education stakeholders with more agency over the decision making process. For this purpose, he utilises a set of technologies, including knowledge graphs, recommender systems and conversational agents (Chatbots), to develop explainable decision-support systems for learners and educators.

Outline
10.45 – 11.00 : Presenter is settling in while participants are starting to connect
11.00 – 11.05 : Welcome and introduction speech
11.05 – 11.45 : Presentation by Hasan
11.45 – 11.55 : Questions and exchanges
11.55 – 12.00 : Conclusion and feedback

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