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Cited by 85Artificial Intelligence in EducationAcademic integrity and plagiarismStudent Assessment and Feedback

AI-resistant assessments in higher education: Practical insights from faculty training workshops

Open access
Awadallah Alkouk, Wejdan, Khlaif, Zuheir N.·2024·Frontiers in Education
DOI 10.3389/feduc.2024.1499495
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Abstract

The emergence of generative AI in education introduces both opportunities and challenges, especially in student assessment. This paper explores the transformative influence of generative AI on assessment practices, drawing from recent training workshops conducted with educators in the Global South. It examines how AI can enrich traditional assessment approaches by fostering critical thinking, creativity, and collaboration. The paper introduces innovative frameworks, such as AI-resistant assessments and the Process-Product Assessment Approach, which emphasize evaluating not only the final product but also the student’s interaction with AI tools throughout their learning journey. Additionally, it provides practical strategies for integrating AI into assessments, underscoring the ethical use and preservation of academic integrity. Addressing the complexities of AI adoption, including concerns around academic misconduct, this paper equips educators with tools to navigate the intricacies of human-AI collaboration in learning settings. Finally, it discusses the significance of institutional policies for guiding the ethical use of AI and offers recommendations for faculty development to align with the evolving educational landscape.

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Crossref · 10.3389/feduc.2024.1499495

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  • Assignments that integrate real-world applications, such as case studies, oral defences, and practical problem-solving tasks, ensure that students engage deeply with the content rather than relying on AI-generated responses.

    “Assignments that integrate real-world applications, such as case studies, oral defences, and practical problem-solving tasks, further ensure that stu -dents engage deeply with the content rather than relying on AI-generated responses [ 68 ].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [68]

    The “AI and authentic assessments” and “Key insights from AI assessment workshops” sections support real-world case studies and problem-solving tasks as ways to deepen engagement and reduce reliance on AI-generated content. The passages do not identify oral defences as an example, and they say these tasks make reliance on AI more difficult—not that they ensure students will not rely on it. No page numbers are available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The ability to generate polished AI-driven responses has contributed to increasing submission of work that students do not create, undermining the authenticity of assessments.

    “The ability to generate pol -ished AI-driven responses has also contributed to the increasing submission of work that students do not create, thus undermining the authenticity of assessments [ 68, 72, 85, 86].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [68]

    The paper’s section on AI and authentic assessments says generative AI makes automated content easier to produce and increases the risk of plagiarism. The Key insights section also reports educators’ concerns that AI might dilute the originality of student work. Neither passage establishes that submissions of work students did not create are actually increasing; the paper frames this as a risk, not an observed trend. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Multi-stage assignments, where students submit drafts before finalising their work, allow educators to track students' learning progress and interaction with AI throughout the assessment process, making it more difficult to misuse AI tools.

    “A structured approach to assessment redesign has led to multi-stage assignments, where students submit drafts before finalising their work [ 70]. This method allows educators to track students' learning progress and interaction with AI throughout the assessment process, making it more difficult to misuse AI tools [ 68, 69, 82 ].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [68]

    The source’s “Key insights from AI assessment workshops” section describes a process–product approach that evaluates the steps students take as well as their final work. It does not describe multi-stage assignments in which students submit drafts before finalising their work—the specific approach attributed to this source by citation [70]. The later sentence about tracking AI interaction has separate citations. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • [68] recommend integrating AI into the assessment process while maintaining academic integrity by ensuring that assignments focus on analytical thinking rather than simple information retrieval.

    “In addition, [ 68 ] recommend integrating AI into the assessment process while maintaining academic integrity by ensuring that assignments focus on analytical thinking rather than simple information retrieval.”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [68]

  • AI evaluations frequently encounter ethical issues related to bias, privacy, and the ownership of data.

    “Notably, AI evaluations frequently encounter ethical issues related to bias, privacy, and the ownership of data.”

    From Redesigning assessments for AI-enhanced learning: a framework for educators in the generative AI era (2025) · cites this paper as Khlaif et al., 2024

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  1. educsci14101090 · DOI · Paper page

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Authors
Awadallah Alkouk, W., & Khlaif, Z. N.
Affiliations
An-Najah National University (PS)
Corresponding author
Khlaif, Zuheir N.
ORCID
Khlaif, Zuheir N.
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2024-12-04
Volume
9
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2024.1499495
OpenAlex
W4405016792
Cited by
68
References
29
Citations by year
2026: 43 · 2025: 25
FWCI
25.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
generative AI in education, student assessment, faculty development, academic integrity, academic misconduct, human-AI collaboration, AI ethics in higher education, institutional AI policies, Global South higher education

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