Abstract

Generative Artificial Intelligence (GenAI), exemplified by tools such as ChatGPT, has posed significant challenges and opportunities in the realm of academic integrity, particularly in engineering education. This commentary critically examines alternative strategies that go beyond traditional assessment security, aiming to uphold academic integrity while embracing the transformative potential of GenAI in teaching and learning. In the context of programs that rely on unit level outcomes to determine the overall student progression (not the programmatic approach to progression), this study identifies and explores four strategies that may offer potential improvements to assessment security: I-risk-level analysis, which aligns the mix of supervised and unsupervised assessments with their susceptibility to GenAI-enabled misconduct; II-adaptive grade scaling, which links marks for unsupervised work to performance in supervised tasks, thereby discouraging dishonest outsourcing; III-gatekeeper units, which embed high-stakes supervised checkpoints that verify the essential competencies before progression; and IV-maintaining the status quo, which exposes the limitations of solely relying on the existing security measures. The analysis highlights the potential benefits and weaknesses of each approach, supporting holistic decision-making on policies that improve the ability of students to meet the competency requirements (compared to strategy IV) while fostering a culture of honesty and ethical behavior. By providing a thorough examination of these strategies, this study contributes valuable insights for educators, policymakers, and researchers, aiming to facilitate a balanced approach that aligns with the accreditation requirements and prepares students for the ethical use of GenAI in their professional careers. This commentary broadens and stimulates discussions on academic integrity in the GenAI era, thus providing practical guidance for educators and institutions.

Document Type

Journal Article

Date of Publication

1-1-2025

Volume

5

Issue

4

School

School of Engineering

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publisher

American Institute of Mathematical Sciences

Identifier

Yasir M. Al-Abdeli: https://orcid.org/0000-0001-5672-9448

Comments

Nikolic, S., Ros, M., Al-Abdeli, Y. M., & Fairweather, H. (2025). Beyond assessment security: A critical policy analysis of four alternative strategies to uphold academic integrity and adopt the GenAI transformation of teaching and learning for an accredited engineering degree. STEM Education, 5(4), 564–586. https://doi.org/10.3934/steme.2025027

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