Applied AI / Teaching about AI across the curriculum
In 2026, we are focusing on how different disciplines teach AI technologies. Our experts from the arts, sciences, and humanities will give us valuable insights into how AI is changing their subjects, and how young people can be supported to develop the skills they need in the future. To learn more, watch the seminar recordings, read our summary blogs, and download speakers' slides.
Teaching youth to critically evaluate AI in healthcare while learning and applying data science and machine learning skills (14 April 2026)
Speaker: Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare)
In this seminar, Kathryn Jessen Eller shared how the Data Science, AI and You (DSAIY) in Healthcare programme combines a semester-long high school foundations course with current event datathons to help students understand how AI works and how it impacts society. She described how the course introduces essential ideas, graphing, statistics, correlation, and linear regression via free online programs such as CODAP as a foundation for interpreting Python-generated pair plots, scatterplots, boxplots, and histograms.
These visual tools and additional short engaging activities from sources like code.org help students learn to identify meaningful features, compare models, and understand supervised versus unsupervised learning. Students also learn to design, train, test, and refine a model. Kathryn also discussed how students explore bias at every stage of the machine learning process, from data collection to model interpretation. Student collaboration with teachers, data scientists, and clinicians during DSAIY’s intergenerational healthcare datathons reinforces foundational concepts learned during the course.
Kathryn Jessen Eller is Principal Investigator of the National Science Foundation-funded Data Science, AI and You (DSAIY) in Healthcare program. Her work focuses on designing and studying data science and AI learning experiences that help high school students build statistical reasoning, interpret real-world datasets, understand core machine learning concepts, and recognise ethical and societal impacts of AI in medicine. DSAIY ensures that students are aware of their responsibility in the ethical use of AI. She collaborates with Brown University, MIT Critical Data, and Rhode Island secondary schools to broaden participation in AI and support youth in developing human-centered, critical AI literacy.
Seminar materials
Teaching AI to creators (17 March 2026)
Speaker: Rebecca Fiebrink (University of the Arts London)
In this seminar, Rebecca Fiebrink explored the questions of how and why we might teach AI for creative practitioners, including children, students, and professionals. She described ways to understand the broad creative value of AI beyond the headline-grabbing systems that produce fully-formed media from text prompts. This includes the use of AI in making new types of creative works, and in making creative experiences more accessible. She demonstrated some of the tools she and her collaborators have built to scaffold AI learning and support AI use in contexts including interactive art, musical instrument design, and design ideation.
Rebecca Fiebrink is a Professor of Creative Computing at University of the Arts London. She has taught creative machine learning to university students, professional artists, members of the public, and children for over a decade. In 2016, she launched the world's first online course (MOOC) about creative machine learning. She and her collaborators have also developed numerous open-source software tools for creative machine learning (e.g., Wekinator for artists and musicians, InteractML for game and VR developers), and these are used by hundreds of thousands of creators and students worldwide.
Seminar materials
Links shared by Rebecca:
www.wekinator.org
mimicproject.com
interactml.com
Advancing AI for society via race-conscious algorithmic approaches (10 February 2026)
Speaker: Thema Monroe-White (George Mason University)
The increasing dependence on algorithmic systems across societal domains has underscored the need to address structural inequities encoded in scientific, and artificial intelligence (AI) systems. Thema Monroe-White presented her research on intersectional race and gender biases in large language models (LLMs) and scientific discourse with implications for educators and education research. She discussed why and how emancipatory data practices, empowering algorithmic design principles, and responsible innovation can help to mitigate systemic biases in scientific- and AI-driven decision-making. By utilising critical quantitative approaches, her research not only advances scientific discovery; it prioritises the needs and well-being of marginalised people. Her interdisciplinary approach highlights the value of centering lived experiences and personal identities in computational and quantitative methodologies to ensure the benefits of science and technology are equitably distributed.
Thema Monroe-White is an Associate Professor of Artificial Intelligence, and Innovation Policy at the Schar School of Policy and Government and the Department of Computer Science (joint) at George Mason University. Her interests include bias mitigation in artificial intelligence (AI), critical quantitative and computational methods, and racial equity in social and economic systems. She is particularly concerned with understanding the pathways to empowerment for minoritised groups via AI education and emancipatory data science practices. She serves as a senior advisor for multiple nonprofit, community, and philanthropic agencies on equitable pathways in data science education, race and gender equity in the AI workforce, and fostering diversity in STEM pathways.