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Two-lane approach to assessment
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general
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607ad7b7356c49a891e398c5a4664624
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https://teachwell.auckland.ac.nz/assessment/two-lane-approach-to-assessment/
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https://teachwell.auckland.ac.nz/
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2026-03-16T03:33:43+00:00
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Two-lane approach to assessment

Source: https://teachwell.auckland.ac.nz/assessment/two-lane-approach-to-assessment/ Parent: https://teachwell.auckland.ac.nz/

Assessment

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Two-lane approach to assessment

The two-lane approach offers a way of thinking about assessment and artificial intelligence (AI).

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The two-lane approach adopted at the University of Auckland is designed to help teachers manage assessments given widespread availability of, and student use of AI tools. This approach helps teachers be more deliberate and clearer about the conditions under which students may use AI in assessment tasks.

The two lanes work in tandem and support good assessment practice by helping teachers:

Teachers should check with their faculty’s Associate Dean Learning and Teaching or programme director as to specific implications for each programme and course. All courses and programmes must implement the two-lane approach in assessment design by 2027 unless differently specified by your faculty.

Image: Anonymous author on Pixabay

Lanes 1 and 2 explained

Criteria

Definition

Rationale

How it works

(Programme directors and Associate Deans Learning and Teaching will give direction as to the balance and placement)

Examples

Lane 1: Controlled assessment

Graded assessment undertaken in controlled conditions. By default, AI is restricted. In specified circumstances, students may be permitted to use certain AI tools.

Provides assurance about the authenticity of students’ work, maintains integrity of degrees, and supports student progression.

Graded assessment activities to evidence and assure student’s attainment of relevant Learning Outcomes.

Interactive oral assessments, performance and/or practical work assessed by observation, invigilated paper-based or invigilated digitally controlled exams.

Criteria

Definition

Rationale

How it works

(Programme directors and Associate Deans Learning and Teaching will give direction as to the balance and placement)

Examples

Lane 2: Uncontrolled assessment

Graded or ungraded assessment undertaken in uncontrolled conditions. Students may use AI to assist them, and this use is not restricted.

Provides opportunities to build students’ confidence as discerning AI users.

Assessment activities, which can be graded and ungraded, designed to support students develop and evidence relevant Learning Outcomes.

Lab/studio reports, portfolios, essays, case analyses, designing and creating artifacts, practical simulations, presentations, posters, etc.

University of Auckland’s position

The AI in Education Action Plan prioritised “to adopt and embed the two-lane approach in ‘Assessment of Course’ guidelines, to consolidate a position with respect to AI use for delivery of assessments and for the marking and grading of student assessments”.

Following Senate’s endorsement on 17th November 2025, amendments to the Assessment of Course Procedures were approved by the Vice Chancellor on 18th November 2025.

Recent amendments to the Assessment of Course Procedures are:

What does this mean for us?

As we move to implement the two-lane approach for 2027, anyone involved in programme and course design and delivery will be asked to identify which assessments should be completed under controlled conditions (Lane 1) and which assessments can be completed under uncontrolled conditions (Lane 2).

Rather than a strictly binary approach, the two-lane approach also offers a flexible framework where assessment tasks can combine Lane 1 (controlled) and Lane 2 (uncontrolled) elements to both verify authentic learning, and support students’ learning development. This then may require rethinking both the placement and design of assessment to ensure they are fit-for-purpose.

Academic Heads and Associate Deans Learning and Teaching play important roles in encouraging and supporting teachers and students in their departments/schools to build capability with AI and to use it ethically, effectively, and sustainably.

Programme directors are encouraged to revise graduate capabilities to add AI competency and to plan how the two-lane approach can be implemented programmatically across the full programme of study. The University of Auckland is committed to assessing AI capabilities as a ‘whole of programme’ approach, therefore a broad, programmatic view of assessment consistent with the Assessment Policy and Procedures should be adopted.

Course directors are encouraged to embed AI into their courses in ways that support students to learn how to use AI tools ethically, critically and effectively.

Teachers are encouraged to develop their own facility with and capabilities using AI tools. Resources are available through this website via information on generative AI and case studies in teaching.

FAQs

Do I have to choose a lane?

The two-lane approach does not require a programme, or course to choose exclusively between Lane 1 or Lane 2 for assessment. Instead, decisions should be guided by the overall purpose of the assessment, the most effective ways to demonstrate the learning outcomes, and the need for clear, transparent communication. This approach also supports offering students a diverse range of assessment types. Indicating the relevant lane (or lanes) for each assessment task will further clarify expectations for students.

Why not just call Lane 2 ‘open’ or ‘unsecured’?

Lane 2 assessments are not simply unsecured or open book tasks. Lane 2 ‘uncontrolled assessments’ are designed to align with the AI Education Action Plan. These action areas support students to develop the skills needed in a world where AI is ubiquitous.

Lane 2 assessments are authentic, often mirroring real-world or discipline-specific contexts where AI tools are already in use. Just as teachers may use AI tools in teaching, research, and administrative tasks, students are expected to use AI thoughtfully and critically in Lane 2 assessments.

Is Lane 1 just 'no AI' and Lane 2 'full AI'?

Not exactly. The distinction is not about the presence or absence of AI, but about assessment conditions and purpose.

For example, in architecture, AI is already used in industry for ideation. Therefore, architecture programmes should teach students to engage with these tools ethically, critically, and effectively through Lane 2 assessments aligning course learning outcomes accordingly. A Lane 1 assessment, however, might still involve a live, authentic task (e.g., a mock client meeting) without students using AI support.

Is Lane 1 just for tests and exams?

Not entirely. Lane 1 speaks to controlled conditions and not to a specific assessment type. There is a broad range of assessment tasks which may be appropriate to Lane 1 conditions, depending on the discipline, purpose of assessment, learning outcomes to be evidenced, and ability to administer. For example, other types of assessment in lane 1 include interactive oral assessments, in-class assessments, practical work assessed by observation, live performance etc.

Who decides where secured assessments (Lane 1) should be placed?

A programmatic approach to the strategic placement of Lane 1 assessments should be coordinated at the faculty level, with leadership coming from:

This approach supports consistency, reduces duplication, and helps manage workload across programmes.

What are we really assessing in Lane 2?

Lane 2 assessments are primarily assessment for and as learning. The focus is on how students engage with tools, apply disciplinary knowledge, and develop evaluative judgement.

We are not assessing how “good” the AI tools are, but how well students:

This aligns with University’s Graduate Profile, which emphasises critical thinking, digital capability, and ethical judgement. Resources are available to support students in developing these capabilities, e.g., GenAI Essentials | UoA.

How does Lane 2 ensure students still “use their brains”?

Using AI well requires cognitive effort. Students must:

These are not passive processes; they demand disciplinary knowledge, critical thinking, and ethical awareness, all of which are tested in Lane 1 assessment tasks. If students bypass learning in Lane 2, they will struggle in Lane 1.

Can course directors apply limitations to Lane 2 assessments?

For example: “AI use is prohibited, or only certain AI tools are permitted / not permitted?”

No, unless using Lane 1, a teacher cannot restrict AI use in a Lane 2 assessment. The point of Lane 2 is to accept that AI may be part of the student’s assessment artifact.

“It is also not possible to reliably or equitably detect that it has been used, either because a student has access to the latest technology, because they know how to change the raw output, or can afford to pay someone to do this for them. Any unenforceable restriction damages assessment validity so a scale or traffic light approach of telling students that they can only use AI for certain purposes, or use certain AI tools, is untenable. A clearer and more realistic approach is to consider the use of AI in Lane 2 assessments as a menu, where students can pick anything and it is our role as educators to guide them which options are more delectable (better for learning).” Danny Liu & Adam Bridgeman, University of Sydney.

Teachers should strive for clarity in messaging attached to assessment so that students are able to understand what is expected of them.

Does academic integrity still apply to a Lane 2 assessment?

With respect to academic integrity and AI use, part of the appeal of the two-lane approach is that it has the potential to shift our focus away from policing student misuse of AI, to enabling students to be ethical, discerning and productive users of these tools. It also responds to student concern more widely around being falsely accused of inappropriate AI use. This is not to say that there may be other aspects of academic integrity behaviour and breaches that are still relevant, only that the act of using AI is, in and of itself, not academic misconduct.

In thinking about AI, we take the position that it has no agency, so the user (student) who is prompting these tools is to be treated as the author. They are responsible for the work generated by the ‘model’; this may include express acknowledgement. It is important that students are aware of the limitations of AI and treat the output critically since they are responsible for that output.

What about students who cheat? Can AI detection software help?

The two-lane approach is built on the principle that using AI appropriately is not cheating. Detection tools will always lag behind the latest AI models. They are also more likely to catch students who use AI poorly or who lack access to premium tools – raising valid equity concerns.

Instead of relying on detection, teachers are encouraged to focus on:

The University of Auckland does not endorse the use of third-party AI detection tools, as these may be unreliable, create false positives, and may use student data to train their models.

What types of assessments do I need to provide reasonable accommodations for?

Reasonable accommodations, including special conditions for tests and exams, support equitable participation for disabled students.

You may need to provide reasonable accommodations for:

Quizzes are considered ‘tests’ when they are time-limited, contribute to the final course grade, and held outside the examination period.  Disabled students are entitled to use their special conditions for these assessments.  This approach aligns with the definitions of:

Do both Lane 1 and Lane 2 assessments need to be inclusive?

Regardless of the assessment lane, inclusive design is important to:

Many learner needs are under-reported or unrealised, even by learners themselves.  We therefore cannot only rely on reactive adjustments such as special conditions for tests and exams or reasonable accommodations.  Proactively designing inclusive assessments can support all learners to thrive.

Where to find support

References/resources

Bassett, M. A., Bradshaw, W., Hogg, A., Murdoch, K., Bornsztejn, H., Pearce, B., & Webber, C. (2025). Heads we win, tails you lose: AI detectors in education. https://doi.org/10.1080/1360080X.2026.2622146

Bridgeman, A., & Liu, D. (2024, July 2). Frequently asked questions about the two-lane approach to assessment in the age of AI – Teaching@Sydney. Sydney.edu.au. https://educational-innovation.sydney.edu.au/teaching@sydney/frequently-asked-questions-about-the-two-lane-approach-to-assessment-in-the-age-of-ai/

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Policy and procedures

Page updated 16/02/2026 (document refresh)