Teaching postsecondary students about the ethics of artificial intelligence: A scoping review protocol

• Published in: PloS one Vol. 20; no. 7; p. e0329020
• Main Authors: Hillis, Calvin, Bhattacharjee, Maushumi, AlMousawi, Batool, Eltanahy, Tarik, Ono, Sara, Hui, Marcus, Pham, Ba’, Swab, Michelle, Cormack, Gordon V., Grossman, Maura R., Bagheri, Ebrahim, Marshall, Zack
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.07.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Artificial Intelligence - ethics; Biology and Life Sciences; Chatbots; Collaboration; Computer and Information Sciences; Computer science; Core curriculum; Decision making; Education; Education, Higher; Educational objectives; Ethical aspects; Ethics; Humans; Machine learning; Medicine and Health Sciences; Methods; Pedagogy; Philosophy; Physical Sciences; Research and Analysis Methods; Role playing; Science Policy; Scoping Review as Topic; Search methods; Social Sciences; Students; Study and teaching; Study Protocol; Supervised learning; Teaching; Technology application; Canada
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0329020

The field of AI carries inherent risks such as algorithmic biases, security vulnerabilities, and ethical concerns related to privacy and data protection. Despite these risks, AI holds significant promise for social good, with applications ranging from improved healthcare diagnostics to enhanced education strategies. Teaching AI ethics in postsecondary settings has emerged as one of the strategies to mitigate AI-related harms. The objectives of this review are to (1) synthesize existing research related to teaching postsecondary students about the principles and practice of ethics and AI, and (2) identify how educators are evaluating changes in student knowledge, skills, attitudes, and behaviors. This scoping review will follow the first five steps articulated by Arksey and O’Malley. A structured search strategy developed by an academic librarian incorporates three primary concept groups related to education, AI, and ethics. Database search strategies emphasize sensitivity rather than precision, given that a supervised machine learning tool will be used to assist in the identification of relevant abstracts. Searches will be conducted in the following academic databases: PubMed, Embase, Scopus, ERIC, LISTA, IEEE Xplore, APA PsycInfo, and ProQuest Dissertations and Theses. Results will include an up-to-date synthesis of the current state of AI ethics education in postsecondary curricula, evaluated teaching strategies, and potential outcomes associated with AI ethics education. Search results will be reported according to the PRISMA-ScR checklist. Data charting will focus on AI ethics pedagogy. This review will inform future research, policy development, and teaching practices, offering valuable insights for educators, policymakers, and researchers working towards responsible AI integration. Findings will contribute to enhanced understandings of the complexities of AI ethics education and have the potential to shape the ways trainees in multiple disciplines learn about the ethical dimensions of AI in practice.