Klyshbekova, Maira, Cruz, Gisela Reyes, Bentley, Caitlin, Garasto, Stef, Brown, Amy Aisha, Aicardi, Christine, Ball, Brian, Naiseh, Mohammad and Andrei, Oana (2026) A UK perspective on responsible education for responsible AI: a multidisciplinary review and evaluation framework. Journal of Responsible Technology, 25:100147. ISSN 2666-6596 (Online) (doi:10.1016/j.jrt.2025.100147)

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Abstract

Responsible Artificial Intelligence (RAI) education has emerged as a way of approaching the field of AI to address a host of concerns (Bentley et al., 2023). Many education providers have been releasing new RAI-related online courses, programmes, or toolkits. When combined with the issues emerging from the development, deployment, and use of AI, the expansion of RAI education and the proliferation of resources raise two critical questions. First, what can we learn about RAI from examining both the content and structure of publicly available RAI educational resources? Second, how might we understand the quality and impact of these RAI resources? We conducted a systematic search of UK RAI educational resources found online. We first present a descriptive analysis of 211 resources collected, including their type, format, cost, sector, audience, and type of provider. Furthermore, we describe our collaborative approach to analysing four pre-selected resources in-depth, from which we outlined an evaluation framework that we then employed for assessing the content of a subset of 47 resources. The five crucial areas of our framework could guide both learners and developers when approaching RAI resources.

Item Type:	Article
Uncontrolled Keywords:	responsible AI, evaluation, resources, framework, multidisciplinary review
Subjects:	B Philosophy. Psychology. Religion > BJ Ethics L Education > L Education (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	13 Mar 2026 10:57
URI:	https://gala.gre.ac.uk/id/eprint/52037

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