Learning, Teaching and Leadership

In this post, I’d like to give a shoutout to Beatriz Moya, who led a rapid review on academic integrity and artificial intelligence.

Here is the reference:

Moya, B. A., Eaton, S. E., Pethrick, H., Hayden, A. K., Brennan, R., Wiens, J., & McDermott, B. (2024). Academic integrity and artificial intelligence in higher education (HE) contexts: A rapid scoping review. Canadian Perspectives on Academic Integrity, 7(3). https://doi.org/10.55016/ojs/cpai.v7i3

Abstract

Artificial intelligence (AI) developments challenge higher education institutions’ teaching, learning, assessment, and research practices. To contribute evidence-based recommendations for upholding academic integrity, we conducted a rapid scoping review focusing on what is known about academic integrity and AI in higher education before the emergence of ChatGPT. We followed the Updated Reviewer Manual for Scoping Reviews from the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic reviews Meta-Analysis for Scoping Reviews (PRISMA-ScR) reporting standards. Five databases were searched, and the eligibility criteria included higher education stakeholders of any age and gender engaged with AI in the context of academic integrity from 2007 through November 2022 and available in English. The search retrieved 2,223 records, of which 14 publications with mixed methods, qualitative, quantitative, randomized controlled trials, and text and opinion studies met the inclusion criteria. The results showed bounded and unbounded ethical implications of AI. Perspectives included: AI for cheating; AI as legitimate support; an equity, diversity, and inclusion lens into AI; and emerging recommendations to tackle AI implications in higher education. The evidence from the sources provides guidance that can inform educational stakeholders in decision-making processes for AI integration, in the analysis of misconduct cases involving AI, and in the exploration of AI as legitimate assistance. Likewise, this rapid scoping review signals possibilities for future research, which we explore in our discussion.

Keywords

academic integrity, artificial intelligence, academic misconduct, higher education, rapid scoping review, large language models (LLM)

This is a fully open access article. You can download a copy of the full article here: https://doi.org/10.55016/ojs/cpai.v7i3

Related posts:

Exploring the Contemporary Intersections of Artificial Intelligence and Academic Integrity https://drsaraheaton.wordpress.com/2022/05/17/exploring-the-contemporary-intersections-of-artificial-intelligence-and-academic-integrity/

New project: Artificial Intelligence and Academic Integrity: The Ethics of Teaching and Learning with Algorithmic Writing Technologies – https://drsaraheaton.wordpress.com/2022/04/19/new-project-artificial-intelligence-and-academic-integrity-the-ethics-of-teaching-and-learning-with-algorithmic-writing-technologies/

The Use of AI-Detection Tools in the Assessment of Student Work – https://drsaraheaton.wordpress.com/2023/05/06/the-use-of-ai-detection-tools-in-the-assessment-of-student-work/

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Sarah Elaine Eaton, PhD, is a faculty member in the Werklund School of Education at the University of Calgary, Canada. Opinions are my own and do not represent those of my employer.

Sarah Elaine Eaton, PhD, Editor-in-Chief, International Journal for Educational Integrity