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<h1>Resources & Tips</h1>
<p>
Practical tips for redesigning assessment, the postplagiarism concept,
and a comprehensive reading list.
</p>
</div>
</section>
<!-- Main Content -->
<main class="page-wrapper">
<div class="page-content">
<!-- Tips for Redesigning Your Assessment -->
<section id="tips">
<h2 class="mt-0">Tips for Redesigning Your Assessment</h2>
<p class="section-intro">
The following practical tips provide a starting point for educators
looking to redesign their assessments in response to GenAI.
</p>
<div class="tip-card">
<div class="tip-number">1</div>
<div class="tip-content">
<h3>Start with the learning outcomes.</h3>
<p>
Clearly define what you are assessing — application of
knowledge, critical thinking, problem solving, analytical or
evaluative skills — and determine whether the use of GenAI would
support or undermine those outcomes.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">2</div>
<div class="tip-content">
<h3>Be explicit about AI permissions.</h3>
<p>
Clearly communicate whether learners are permitted to use GenAI
and, if so, to what extent and for which purposes. Where
expectations cannot realistically be monitored or evidenced,
consider redesigning the task so that learning is made visible
rather than relying on unenforceable restrictions.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">3</div>
<div class="tip-content">
<h3>
Balance higher-order thinking with foundational knowledge.
</h3>
<p>
Design assessments that prioritise analysis, evaluation, and
creation, while also ensuring that students demonstrate the
essential disciplinary knowledge needed to underpin those
higher-order skills — sometimes independently of AI support.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">4</div>
<div class="tip-content">
<h3>Use multi-faceted and individualised assessment formats.</h3>
<p>
Combine artefact-based tasks with lower-risk approaches such as
oral presentations, peer evaluation, drafts or sketches,
reflective journals, or short viva-style discussions to verify
understanding and personalise learning.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">5</div>
<div class="tip-content">
<h3>Assess the learning process, not only the final product.</h3>
<p>
Build in touchpoints or staged components where each part of the
assessment develops from the previous one. This might include
proposals, annotated drafts, progress reflections, or
interactive oral elements. These approaches make student
thinking visible and strengthen assessment validity.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">6</div>
<div class="tip-content">
<h3>Incorporate structured collaboration.</h3>
<p>
Include opportunities for collaborative problem-solving, peer
feedback, or group projects, with mechanisms to recognise
individual contributions. Collaboration reflects authentic
professional practice and makes learning less easily outsourced.
</p>
</div>
</div>
<div class="tip-card">
<div class="tip-number">7</div>
<div class="tip-content">
<h3>Engage students in dialogue about AI and assessment.</h3>
<p>
Facilitate open conversations about GenAI use, academic
integrity, and responsible practice. Where appropriate, involve
learners in aspects of assessment design — such as discussing
criteria or co-creating elements of marking rubrics — in line
with inclusive and UDL-informed approaches.
</p>
</div>
</div>
</section>
<!-- Postplagiarism -->
<section id="postplagiarism">
<h2>Postplagiarism</h2>
<blockquote>
<p>
Historical definitions of plagiarism will not be rewritten because
of artificial intelligence; they will be transcended
</p>
<cite>— Sarah Eaton</cite>
</blockquote>
<p>
As AI technology continues to develop at a rapid pace, it is
important to consider what the future may look like for educators.
While plagiarism traditionally refers to the copying or paraphrasing
someone else's work without proper attribution, 'postplagiarism' in
the context of GenAI is a term that encapsulates the new challenges
to academic integrity in higher education.
</p>
<h3>Key Aspects of Postplagiarism</h3>
<div class="numbered-list">
<div class="numbered-card">
<h3>AI-Generated Content</h3>
<p>
Students and researchers might use AI to generate essays,
reports, or other academic materials. The question arises as to
whether this content should be considered original or if it
constitutes a form of plagiarism, especially if the use of AI is
not disclosed.
</p>
</div>
<div class="numbered-card">
<h3>Authorship and Ownership</h3>
<p>
Traditional academic work is credited to individuals based on
their intellectual contribution. However, when AI plays a
significant role in content creation, the lines of authorship
and ownership become blurred.
</p>
</div>
<div class="numbered-card">
<h3>Attribution</h3>
<p>
There is ongoing debate about how to attribute AI-generated
content. Should students cite the AI as a source, similar to a
book or article? Or is the use of AI tools similar to using a
calculator or spellchecker?
</p>
</div>
<div class="numbered-card">
<h3>Academic Integrity Policies</h3>
<p>
Updating these policies has become necessary to reflect the
challenges presented by AI, to achieve a balance in encouraging
the responsible use of technology with maintaining the integrity
of academic work.
</p>
</div>
<div class="numbered-card">
<h3>Ethical Considerations</h3>
<p>
The ease of generating content with AI is tempting for students
and researchers to submit work they didn't meaningfully engage
with or understand.
</p>
</div>
</div>
<div class="callout callout-success">
<p>
While there are significant challenges, there are also
opportunities to develop innovative approaches and embrace new
mindsets.
</p>
</div>
<figure style="margin: var(--space-xl) 0; text-align: center">
<img
src="images/eaton-postplagiarism.jpeg"
alt="6 Tenets of Postplagiarism: Writing in the Age of Artificial Intelligence by Sarah Elaine Eaton (2023). The six tenets are: Hybrid Human-AI Writing Will Become Normal, Human Creativity is Enhanced, Language Barriers Disappear, Humans can Relinquish Control but not Responsibility, Attribution Remains Important, and Historical Definitions of Plagiarism No Longer Apply."
class="lightbox-img"
style="
max-width: 100%;
border-radius: var(--radius-md);
border: 1px solid var(--color-border);
"
/>
<figcaption
style="
margin-top: var(--space-sm);
font-size: 0.85rem;
color: var(--color-text-muted);
"
>
6 Tenets of Postplagiarism — Sarah Elaine Eaton (February 2023)
</figcaption>
</figure>
</section>
<!-- Conclusion -->
<section id="conclusion">
<h2>Conclusion</h2>
<p>
GenAI does not diminish the value of assessment; it sharpens our
understanding of why assessment matters. In an AI-enhanced world,
assessment is no longer simply a mechanism for measuring knowledge,
but a means of making learning visible and revealing how students
think, apply, question, create, and grow.
</p>
<p>
Redesigning assessment in response to GenAI requires informed,
reflective, and collaborative engagement from educators. It also
requires institutional support, space for professional development,
and recognition of the emotional and cognitive load that rapid
technological change can bring. Equity, access, and ethical
considerations must remain central.
</p>
<p>
This framework affirms that sustainable academic integrity is
achieved not through surveillance or detection alone, but through
thoughtful, structurally sound assessment design, the development of
AI literacy, and a culture that values learning processes as much as
outcomes.
</p>
<p>
Initially developed as part of the N-TUTORR national project
GenAI:N3, this framework is a living resource that continues to
evolve. The approaches outlined here can be adapted, combined, and
expanded in response to disciplinary needs, emerging research, and
evolving technologies.
</p>
<p>
Many thanks to reviewers Dr Jim O'Mahony, MTU and Damien Raftery,
SETU.
</p>
</section>
<!-- Reading List -->
<section id="reading-list">
<h2>Reading List</h2>
<ul>
<li>
Artificial Intelligence Act, “EU Artificial Intelligence Act | Up-to-date developments and analyses of the EU AI Act,
<a href="https://artificialintelligenceact.eu/" target="_blank" rel="noopener">https://artificialintelligenceact.eu/</a>
</li>
<li>
Bassett, M. A., Bradshaw, W., Bornsztejn, H., Hogg, A., Murdoch, K., Pearce, B., & Webber, C. (2026).
<em>Heads we win, tails you lose: AI detectors in education.</em> Journal of Higher Education Policy and Management, 1–16.
<a href="https://doi.org/10.1080/1360080X.2026.2622146" target="_blank" rel="noopener">doi:10.1080/1360080X.2026.2622146</a>
</li>
<li>
Chan, C.K. and Colloton, T. (2024) 'Redesigning assessment in the
AI era', <em>Generative AI in Higher Education</em>, pp. 87–126.
<a
href="https://doi.org/10.4324/9781003459026-4"
target="_blank"
rel="noopener"
>doi:10.4324/9781003459026-4</a
>.
</li>
<li>
Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why
structural assessment changes are needed for a time of GenAI.
<em>Assessment & Evaluation in Higher Education</em>, 50(7),
1087–1097.
<a
href="https://doi.org/10.1080/02602938.2025.2503964"
target="_blank"
rel="noopener"
>doi:10.1080/02602938.2025.2503964</a
>.
</li>
<li>
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2025). The
wicked problem of AI and assessment.
<em>Assessment & Evaluation in Higher Education</em>, 1–17.
<a
href="https://doi.org/10.1080/02602938.2025.2553340"
target="_blank"
rel="noopener"
>doi:10.1080/02602938.2025.2553340</a
>.
</li>
<li>
Curtis, G. J. (2025). The two-lane road to hell is paved with good
intentions: why an all-or-none approach to generative AI,
integrity, and assessment is insupportable.
<em>Higher Education Research & Development</em>, 44(8),
2151–2158.
</li>
<li>
Dawson, P. (August 2024) '<a
href="https://www.linkedin.com/pulse/how-make-100-accurate-ai-detector-why-we-need-accuracy-phillip-dawson-dobvc/"
target="_blank"
rel="noopener"
>How to make a 100% accurate AI detector: or why we need
specificity in discussions of AI detector accuracy</a
>'.
</li>
<li>
Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024).
Validity matters more than cheating.
<em>Assessment & Evaluation in Higher Education</em>, 1–12.
<a
href="https://doi.org/10.1080/02602938.2024.2386662"
target="_blank"
rel="noopener"
>doi:10.1080/02602938.2024.2386662</a
>.
</li>
<li>
Deep, P. D., (2025). Evaluating the effectiveness and ethical
implications of AI detection tools in higher education.
<em>Information</em>, 16(10), 905.
<a
href="https://doi.org/10.3390/info16100905"
target="_blank"
rel="noopener"
>doi:10.3390/info16100905</a
>.
</li>
<li>
Dietis, N. (2025, March 25).
<a
href="https://futurium.ec.europa.eu/en/european-ai-alliance/community-content/ai-higher-education-mapping-key-guidelines-recommendations"
target="_blank"
rel="noopener"
>AI in Higher Education: Mapping Key Guidelines and
Recommendations</a
>. <em>European Commission Futurium</em>.
</li>
<li>
Farrell, H. & McCarthy, K. (2025).
<em>Manifesto for Generative AI in Higher Education: A living reflection on teaching, learning, and technology in an age of abundance.</em>
GenAI:N3, South East Technological University
<a href="https://manifesto.genain3.ie/" target="_blank" rel="noopener">https://manifesto.genain3.ie/</a>
</li>
<li>
Furze, L. (2024)
<em
>Practical AI strategies: Engaging with Generative AI in
Education</em
>. Melbourne, Australia: Amba Press.
</li>
<li>
Hartley, P. et al. (2024) 'Using generative AI effectively in
Higher Education'.
<a
href="https://doi.org/10.4324/9781003482918-1"
target="_blank"
rel="noopener"
>doi:10.4324/9781003482918-1</a
>.
</li>
<li>
International College of Management Sydney (2024):
<em
><a
href="https://policies.icms.edu.au/wp-content/uploads/2024/05/Artificial-Intelligence-in-Education-Framework_approved-AB_20251121.pdf"
target="_blank"
rel="noopener"
>Artificial Intelligence in Education (AIED) Framework</a
></em
>.
</li>
<li>
Kim, H.N. (2025). Detecting the Undetectable: The Need for a New
Paradigm for Academic Writing Evaluation in the AI Era.
<a
href="https://doi.org/10.1007/978-3-031-94153-1"
target="_blank"
rel="noopener"
>doi:10.1007/978-3-031-94153-1</a
>.
</li>
<li>
Liu, D. and Bridgeman, A. (June 2023) '<a
href="https://educational-innovation.sydney.edu.au/teaching@sydney/chatgpt-is-old-news-how-do-we-assess-in-the-age-of-ai-writing-co-pilots/"
target="_blank"
rel="noopener"
>ChatGPT is old news: How do we assess in the age of AI writing
co-pilots?</a
>'
</li>
<li>
Liu, D. and Bridgeman, A. (Dec. 2023) '<a
href="https://educational-innovation.sydney.edu.au/teaching@sydney/embracing-the-future-of-assessment-at-the-university-of-sydney/"
target="_blank"
rel="noopener"
>Embracing the Future of Assessment at the University of
Sydney</a
>'.
</li>
<li>
Liu, D. and Bridgeman, A. (July 2024) '<a
href="https://educational-innovation.sydney.edu.au/teaching@sydney/frequently-asked-questions-about-the-two-lane-approach-to-assessment-in-the-age-of-ai/"
target="_blank"
rel="noopener"
>Frequently asked questions about the two-lane approach to
assessment in the age of AI</a
>'.
</li>
<li>
Lodge, J. M., Howard, S., Bearman, M., Dawson, P, & Associates
(2023) '<a
href="https://www.teqsa.gov.au/guides-resources/resources/corporate-publications/assessment-reform-age-artificial-intelligence"
target="_blank"
rel="noopener"
>Assessment reform for the age of Artificial Intelligence</a
>'. TEQSA.
</li>
<li>
NAIN AI guidelines (2023):
<a
href="https://www.qqi.ie/sites/default/files/2023-11/NAIN%20Generative%20AI%20Guidelines%20for%20Educators%202023.pdf"
target="_blank"
rel="noopener"
><em>NAIN Generative AI Guidelines for Educators</em></a
>.
</li>
<li>
Newton, P. M., & Draper, M. J. (2025). Widespread use of
summative online unsupervised remote (SOUR) examinations in UK
higher education. <em>Quality in Higher Education</em>, 31(1),
127–141.
<a
href="https://doi.org/10.1080/13538322.2025.2521174"
target="_blank"
rel="noopener"
>doi:10.1080/13538322.2025.2521174</a
>.
</li>
<li>
O'Sullivan, James, Colin Lowry, Ross Woods & Tim Conlon.
<em> AI in Higher Education Teaching & Learning: Policy Framework.</em>
Higher Education Authority, 2025.
<a https://doi.org/10.82110/073e-hg66
href="https://doi.org/10.82110/073e-hg66"
target="_blank"
rel="noopener"
>doi.org/10.82110/073e-hg66</a
>
</li>
<li>
Perkins, M., Roe, J., Furze, L. (2025) 'Reimagining the Artificial
Intelligence Assessment Scale'.
<em>Journal of University Teaching and Learning Practice</em>,
22(7).
<a
href="https://doi.org/10.53761/rrm4y757"
target="_blank"
rel="noopener"
>doi:10.53761/rrm4y757</a
>.
</li>
<li>
Suhail, A.H., Guangul, F.M. and Nazeer, A. (2024) 'Evolution of
assessment and feedback methods in Higher Education'.
<a
href="https://doi.org/10.4018/979-8-3693-2145-4.ch003"
target="_blank"
rel="noopener"
>doi:10.4018/979-8-3693-2145-4.ch003</a
>.
</li>
<li>
TEQSA guidelines (Nov. 2023):
<a
href="https://www.teqsa.gov.au/guides-resources/resources/corporate-publications/assessment-reform-age-artificial-intelligence"
target="_blank"
rel="noopener"
><em
>Assessment reform for the age of artificial intelligence</em
></a
>.
</li>
<li>
UNESCO. 2021. Recommendation on the Ethics of Artificial
Intelligence. Paris: United Nations Educational, Scientific and
Cultural Organization.
<a
href="https://unesdoc.unesco.org/ark:/48223/pf0000381137"
target="_blank"
rel="noopener"
><em>https://unesdoc.unesco.org/ark:/48223/pf0000381137</em></a
>.
</li>
<li>
Upsher, R. et al. (2024) 'Embracing generative AI in authentic
assessment'.
<a
href="https://doi.org/10.4324/9781003482918-16"
target="_blank"
rel="noopener"
>doi:10.4324/9781003482918-16</a
>.
</li>
<li>
Various articles — '<a
href="https://www.timeshighereducation.com/campus/spotlight/ai-and-assessment-higher-education"
target="_blank"
rel="noopener"
>AI and Assessment in Higher Education</a
>', <em>Times Higher Education Spotlight</em>.
</li>
<li>
Virmani, S. and Lau, A. (June 2024) '<a
href="https://thesedablog.wordpress.com/2024/06/27/balancing-impact-and-time-a-strategy-for-adapting-assessments-in-the-age-of-genai/"
target="_blank"
rel="noopener"
>Balancing impact and time: A strategy for adapting assessments
in the age of GenAI</a
>', <em>The SEDA Blog</em>.
</li>
</ul>
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