A Three-Step Approach to Shift from Assessment Integrity to Assurance of Learning in the Age of AI
Abstract
Maintaining academic integrity has become a challenge in engineering education with the increase in the use of Artificial Intelligence (AI) by students. Combined with the lack of self-regulation skills in students, which are essential for workplace success, this issue highlights the need to rethink assessment design across programs of study. In this study, a mapping exercise evaluating current assessment practices across an academic program was carried out. Based on the learnings from this exercise, this paper proposes a three-step approach that shifts the focus from preserving assessment integrity to ensuring assurance of learning, whilst also addressing the limited opportunities universities offer for developing self-regulation through assessment. By embedding Assessment for Learning (AfL) principles throughout the curriculum, this approach offers the potential to foster deep learning, reduce the risk of AI misuse, and ensure students graduate as well-rounded, competent professionals in an AI-driven world.
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Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31 · see citing works
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- Authors
- Thite, S. D., Danish, N., Florez, G. O., & Rougieux, F.
- Affiliations
- UNSW Sydney (AU)
- Venue
- 2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)
- Publisher
- IEEE
- Published
- 2024-12-02
- Pages
- 1-5
- OpenAlex
- W4406949715
- Cited by
- 3
- References
- 36
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Engineering › Media Technology › Engineering Education and Curriculum Development
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- assessment integrity, assurance of learning, academic integrity, artificial intelligence in education, AI misuse, assessment for learning, student self-regulation, engineering education, assessment design, curriculum mapping, deep learning
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