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<!doctype html>
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content="AI Integration - Approaches for incorporating GenAI meaningfully into the assessment process. Part of the 2026 Assessment Redesign Framework."
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<h1>Integration of GenAI into Assessment</h1>
<p>
Approaches for incorporating AI meaningfully into the assessment
process.
</p>
</div>
</section>
<!-- Page Content -->
<main class="page-wrapper">
<div class="page-content">
<!-- Integration Factors -->
<section id="integration-factors">
<h2 class="mt-0">Integration Factors</h2>
<p>
The incorporation of GenAI into the assessment process can enhance
the learning experience and contribute towards establishing more
engaging and authentic practices. However, it is also important to
determine whether the integration of GenAI into your assessment is
appropriate.
</p>
<p>
The
<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
>
developed by the International College of Management in Sydney,
recommends consideration of the following factors:
</p>
<div class="callout callout-info">
<h4>Educational Reasoning</h4>
<p>
If students are asked to demonstrate their understanding, critical
thinking skills, or ability to apply knowledge independently,
relying heavily on AI could undermine the intended learning
outcomes.
</p>
</div>
<div class="callout callout-info">
<h4>The Nature of the Task</h4>
<p>
If the task aims to assess a student's writing proficiency, using
GenAI to produce the written content would make this impossible.
In contrast, if the task is focused on exploring AI capabilities
or understanding its applications, the use of GenAI may be
appropriate.
</p>
</div>
<div class="callout callout-info">
<h4>The Function of the Task</h4>
<p>
If a student's mastery of specific concepts or their ability to
solve complex problems are being assessed, relying on AI could
potentially hinder the accurate evaluation of their skills and
knowledge.
</p>
</div>
</section>
<!-- The AI Assessment Scale -->
<section id="ai-scale">
<h2>The AI Assessment Scale</h2>
<p>
The following scale developed by
<a
href="https://leonfurze.com/2023/12/18/the-ai-assessment-scale-version-2/"
target="_blank"
rel="noopener"
>Leon Furze</a
>
et al. outlines the varying levels of GenAI integration possible in
the assessment process. The different levels may be adopted
depending on a range of factors including: the discipline; the
nature of the assessment; the purpose of the assessment; and the
intended learning outcomes.
</p>
<figure style="margin: var(--space-xl) 0; text-align: center">
<img
src="images/furze-ai-scale.png"
alt="The AI Assessment Scale by Perkins, Furze, Roe and MacVaugh (2024), showing 5 levels: 1. No AI, 2. AI Planning, 3. AI Collaboration, 4. Full AI, 5. AI Exploration — each with descriptions and student-facing guidance."
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);
"
>
The AI Assessment Scale — Perkins, Furze, Roe & MacVaugh
(2024).
<a href="https://leonfurze.com" target="_blank" rel="noopener"
>leonfurze.com</a
>
</figcaption>
</figure>
<div class="callout callout-warning">
<p>
Note: The Leon Furze AI Assessment Scale is a visual reference
tool. Please refer to Furze, L. (2024) 'Practical AI strategies:
Engaging with Generative AI in Education' for the full framework.
</p>
</div>
</section>
<!-- The Two-Track Approach -->
<section id="two-track">
<h2>The Two-Track Approach</h2>
<p>
This is further simplified by Liu and Bridgeman (2023) at the
University of Sydney as
<a
href="https://educational-innovation.sydney.edu.au/teaching@sydney/embracing-the-future-of-assessment-at-the-university-of-sydney/"
target="_blank"
rel="noopener"
>two clear assessment tracks</a
>
are identified:
</p>
<div class="track-comparison">
<div class="track-box track1">
<h3>Track 1 — Secured</h3>
<span class="badge badge-track1">AI-Restricted</span>
<p>
AI use is typically not permitted unless the ethical use of an
AI tool is purposefully being assessed. The focus is on
'assessment of learning'. These assessments are supervised, and
unauthorised use of AI is considered a breach of academic
integrity.
</p>
</div>
<div class="track-box track2">
<h3>Track 2 — Open</h3>
<span class="badge badge-track2">AI-Integrated</span>
<p>
Responsible use of AI is encouraged. These assessments are less
supervised, promoting engagement with AI and preparing students
for an AI-integrated society. Acceptable AI-usage is clearly
detailed in the Assessment Briefs and any unauthorised use of AI
outside of this is considered a breach of academic integrity.
</p>
</div>
</div>
<p>
The incorporation of both tracks is viewed as a positive step in
creating a balanced assessment environment where foundational
knowledge and critical thinking skills remain relevant, while also
focusing on authentic assessments. It is suggested that most
assessments should fall into Track 2 as we prepare our learners for
future careers in an increasingly AI-enhanced landscape.
</p>
</section>
<!-- Structural Redesign vs Discursive Control -->
<section id="structural-redesign">
<h2>Structural Redesign vs Discursive Control</h2>
<p>
More recently, discussions have emerged around the enforceability of
such models and a distinction is emerging between discursive control
(rules and warnings) and structural redesign (changing the mechanics
of assessment). Structural redesign provides more sustainable
integrity solutions.
<a
href="https://www.tandfonline.com/doi/full/10.1080/02602938.2025.2503964"
target="_blank"
rel="noopener"
>(Corbin, Dawson, Liu, 2025)</a
>
</p>
<div class="table-wrapper">
<table>
<thead>
<tr>
<th>Approach</th>
<th>Focus</th>
<th>Limitation / Opportunity</th>
</tr>
</thead>
<tbody>
<tr>
<td>Discursive control</td>
<td>Rules, warnings, AI bans</td>
<td>Relies on student compliance</td>
</tr>
<tr>
<td>Structural redesign</td>
<td>Changing the mechanics of assessment</td>
<td>Builds validity into the task itself</td>
</tr>
</tbody>
</table>
</div>
</section>
<!-- Focusing on the Process -->
<section id="process">
<h2>Focusing on the Process</h2>
<p>
Process-based assessments emphasise how learners arrive at
conclusions, not just final products. This type of assessment is
particularly valuable in preparing students for complex, real-world
challenges where the journey is just as important as the
destination.
</p>
<p>
Requiring evidence of the learning process can provide deeper
insight into the level of understanding, reasoning processes, and
ability to apply knowledge. It also makes learning visible and
reduces reliance on surveillance approaches.
</p>
<p>
The learning process can be revealed through a variety of means
including reflective journals, drafts or sketches, staged
assessments, and iterative feedback cycles.
</p>
<ul>
<li>
Increased opportunities for peer learning, collaboration, and
teacher-student interaction
</li>
<li>
Development of self-awareness of their own strengths and
weaknesses
</li>
<li>Enhanced student engagement and deep learning</li>
<li>Development of critical thinking and problem-solving skills</li>
<li>Reduced pressure on a final outcome</li>
<li>Promotion of a culture of transparency and integrity</li>
</ul>
<div class="callout callout-success">
<p>
Process-focused assessment provides an alternative to
surveillance-based integrity approaches. By making learning
visible through drafts, checkpoints, oral interactions, and
reflective commentary, educators create evidence of authentic
engagement without depending on unreliable AI detection tools.
</p>
</div>
</section>
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<li><a href="#integration-factors">Integration Factors</a></li>
<li><a href="#ai-scale">AI Assessment Scale</a></li>
<li><a href="#two-track">Two-Track Approach</a></li>
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