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Cited by 21Artificial Intelligence in EducationStudent Assessment and FeedbackAcademic integrity and plagiarism

Designing assessments in the generative AI era: A tailored assessment framework for ICT tertiary education

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Ahangama, Nadeera·2026·International Journal of Educational Technology in Higher Education
DOI 10.1186/s41239-026-00582-0
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Abstract

The rapid expansion of generative artificial intelligence (GenAI) as a disruptive force in modern times presents tertiary education with both unprecedented opportunities and profound challenges for assessment design. As GenAI can create plausible assessment responses with limited student engagement, many traditional assessments are now at risk of becoming obsolete. Assessment reform is particularly important in ICT programs, where GenAI can readily generate assessment content, such as coding, and algorithms, and technologically savvy students may be prone to misuse it rather than engage in authentic learning. Consequently, sector-wide discussions are centred on how to redesign assessment to enable the appropriate and ethical use of GenAI while ensuring students genuinely achieve the expected learning outcomes. Despite calls for updated strategies from quality assurance bodies, a significant gap remains between the pace of GenAI advancement and the implementation of such assessment reforms. The objective of this study is to identify how educators can design assessments for ICT tertiary students that accurately measure student learning outcomes in an era of widespread access to GenAI tools. Using a qualitative interpretivist approach and thematic analysis of scholarly and institutional practice documents, this research proposes the GENESIS framework (GenAI-Enabled Strategic and Instructional System for Assessment), a five-pillar, institution-wide model emphasising shifts in institutional values, governance, and support as prerequisites for sustainable assessment reform. GENESIS is complemented by the Assessment Design Matrix (ADM), a tripartite structure integrating graduate competencies, assessment strategies, and assessment methods. ADM provides a practical, hands-on guide for assessment designers to craft integrity-focused assessments that assess ICT graduate competencies. Together, GENESIS and ADM propose a structured, discipline-tailored framework to ensure assessments continue to validly measure student learning within the GenAI ecosystem.

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  • Large language models generate plausible essays, summaries, laboratory write-ups, and examination responses with limited student engagement.

    “As large language models generate plausible essays, summaries, laboratory write-ups, and examination responses with limited student engagement (Ahangama, 2026)”

    From OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design (2026) · cites this paper as Ahangama, 2026

    Theme 1–Values & Dispositions (page unavailable) discusses students submitting AI-generated content as their own work, and Theme 7–Assessment Methods describes staged essays intended to foster authentic engagement. These support the general concern that AI can substitute for student work, but our copy does not substantiate the specific list of summaries, laboratory write-ups, and examination responses, or establish that LLMs produce all of them plausibly with limited student engagement. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The proliferation of generative artificial intelligence (GenAI) has fundamentally challenged the validity of traditional assessments in higher education.

    “The proliferation of generative artificial intelligence (GenAI) has fundamentally challenged the validity of traditional assessments in higher education.”

    From OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design (2026) · cites this paper as Ahangama, 2026

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Cited by

Sorted by citations

Authors
Ahangama, N.
Corresponding author
Ahangama, Nadeera
ORCID
Ahangama, Nadeera
Venue
International Journal of Educational Technology in Higher Education
Publisher
Springer Science and Business Media LLC
Published
2026-03-04
Volume
23
Issue
1
ISSN
2365-9440 (ISSN-L)
DOI
10.1186/s41239-026-00582-0
OpenAlex
W7133336059
Cited by
21
References
15
Citations by year
2026: 21
FWCI
95.42
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Education › Student Assessment and Feedback
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
generative AI, assessment design, assessment reform, ICT education, higher education, graduate competencies, academic integrity, assessment validity, institutional governance, quality assurance

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