Diversity in AI Education: Tailoring students’ learning paths for inclusive learning about facial recognition

As emerging technologies are rapidly advancing as part of our societies and everyday life, it is crucial to include and empower all students in learning about computing and advanced technologies. These include technical capabilities of algorithms, such as the use of artificial intelligence, that enable novel interactions between humans and their environment and give rise to societal and ethical concerns. In this article, we investigate how to engage students through diverse learning paths in computing education. We emphasise students who are generally less motivated to learn about technology and their ethical and societal implications, focusing on primary students (13-14 years) in a culturally diverse Danish public school. Based on design ethnographic studies and nexus analysis the study explored learning activities and social interactions in relation to students’ individual learning paths. The work extends Smith’s and Iversen’s (2018) framework on student archetypes in learning about digital technology to include aspects of diversity and inclusive education. The findings highlight, first, the impact of students’ cultural backgrounds in their learning paths even within the same archetype; second, opportunities for embodied learning strategies to engage “not (yet) motivated” students; third, benefits for the interplay between different student archetypes in classroom dynamics and fourth, how students may shift between archetypes depending on the learning context and theme.