Two-lane approach to assessment
The two-lane approach offers a way of thinking about assessment and generative AI.
The two-lane approach adopted at the University of Auckland is designed to help teachers effectively manage assessments in response to the widespread availability and use of generative AI (AI) tools like MS Copilot, Chat GPT, Google Gemini and NotebookLM, to name a few. It helps us be more deliberate about the conditions under which AI will be used in assessment tasks, and offers clarity to students. It’s important to recognise that both lanes work in tandem. The two-lane approach supports good assessment practice by helping you:
- Strategically assess core knowledge and skills where authenticity of student work is essential
- Design authentic, varied tasks that require critical and responsible AI use
- Ensure assessments are inclusive, transparent, and adaptable to evolving AI capabilities
Always check with your Associate Dean Learning and Teaching or Programme Director as to what this means in your faculty.
Image: Anonymous author on Pixabay
Lanes 1 and 2 explained
Assessment policy and principles
There are a number of current policy settings that align with the two-lane approach. These include a greater emphasis on taking a programme approach to assessment, moving away from course-by-course assessment design, making use of contemporary digital modalities and increasing the amount of formative assessment.
1
Authentic assessment
2
Emphasis on formative assessment
3
Encouraging the use of digital modalities
The policy encourages assessment design and delivery, that takes account of new technologies, including digital modalities for all stages of the assessment process. It emphasises the role of assessment in helping students build the ability to work ethically with technologies such as generative AI.
4
Whole of programme design
The policy advocates for a ‘whole-of-programme’ approach, ensuring assessment tasks are coherent at a programme level and aligned to the Graduate Profile. A programmatic approach has the potential to support an overall reduction in assessments and emphasises validation of students’ attainment at critical points across a programme. Where a ‘whole-of-programme’ approach is not practical we encourage colleagues to explore opportunities within a more contained slice, for example, stage 1, a common core, a major.
Notes and guidance
In thinking about Generative AI, we take the position that Generative AI has no agency, so the user who is prompting the tool is to be treated as the author. They are responsible for the work generated by the model. It is important that authors are aware of the limitations of Gen AI and treat the output critically since they are responsible for that output. The following documents and notes may clarify the position adopted by University.
Two-lane approach at University of Sydney
Our approach is informed by the position of University of Sydney. A lot has been written about this approach, but the following is a good introduction:
What to do about assessments if we can’t out-design or out-run AI?
University of Auckland position
- “We will adopt and embrace Artificial Intelligence confidently and ethically in ways that maximise value and benefit for our people, our institution, and our world.” — University Executive Committee, October 2024
The AI in Education Action Plan, endorsed in June 2025, sets the wider framework for this work. Its five action areas—policy, guidance, tools, professional learning, and research—are where the two-lane approach to assessment sits. As the Plan notes:
- “… the University seeks to improve student AI literacy, and provide access to a dynamic set of ‘AI’ skills…including …the ability to operate with AI in a safe, ethical and effective way.” (2.0)
- “… the University seeks to ensure that students in every discipline have the opportunity to learn about AI, and to be ethical users and creators of AI. As a strategic principle, AI is to be integrated into the curriculum.” (3.0)
FAQs
Why not just call Lane 2 ‘open’ or ‘unsecured’?
Lane 2 assessments are not simply “unsecured” or “open-book” tasks. They are designed to align with the five action areas in the AI Education Action Plan, supporting students to develop the skills needed in a world where AI is ubiquitous. Lane 2 assessments are authentic, often mirroring real-world or disciplinary contexts where AI tools are already in use. Just as academic staff may use AI tools in teaching, research, and administration, students are expected to use them thoughtfully in Lane 2 assessments.
A better term for assessments that are not fully secured but not yet fully embracing AI might be “towards Lane 2”. In contrast, Lane 1 assessments (assessment of learning) are typically more controlled and may occur in less authentic settings (e.g. exam halls). The rise of generative AI makes securing these assessments more challenging and potentially more artificial.
Isn’t 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.
- Lane 1 assessments are controlled environments used to verify attainment of learning outcomes. These may or may not involve AI, depending on the discipline and task.
- Lane 2 assessments are more open and formative, supporting students to learn with AI tools where appropriate.
For example, in architecture, generative AI is already used in industry for ideation. It makes sense for students to engage with these tools in Lane 2, and for learning outcomes to reflect this. However, a Lane 1 assessment might still involve a live, authentic task (e.g., a mock client meeting) without AI support.
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 is, but how well students:
- Select appropriate tools
- Use them effectively
- Critically evaluate outputs
This aligns with UoA’s graduate profile, which emphasises critical thinking, digital capability, and ethical judgement.
Who decides where secured assessments (Lane 1) should be placed?
Strategic placement of Lane 1 assessments should be coordinated at the faculty level, with input from:
- Programme directors who oversee curriculum coherence
- Associate Deans Learning and Teaching who ensure alignment with faculty assessment policy
This approach supports consistency, reduces duplication, and helps manage workload across programmes.
How does Lane 2 ensure students still “use their brains”?
Using AI well requires cognitive effort. Students must:
- Understand the task
- Choose the right tools
- Interpret and refine outputs
- Justify their decisions
These are not passive processes. They demand disciplinary knowledge, critical thinking, and ethical awareness, all of which are tested in Lane 1 assessments.
Put simply: if students bypass learning in Lane 2, they will struggle in Lane 1.
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 are improving, but they will always lag behind the latest generative models. They are also more likely to catch students who use AI poorly or who lack access to premium tools, raising equity concerns.
Instead of relying on detection, UoA’s approach focuses on:
- Assessment design that integrates AI use transparently
- Clear expectations for students
- Strategic use of Lane 1 assessments to assure learning outcomes
Do you have a question of your own?
Drop us a line and help us grow this series of FAQs.
Support
- AI literacy self-paced sessions and facilitated workshops (as available) via the TeachWell Professional Learning Series.
- For tailored advice, book some time with a learning designer through the TeachWell Consult Service.
Page updated 20/08/2025 (page added)
- Notes and guidance provided curtesy Professor Andrew Luxton-Reilly, Associate Dean Learning and Teaching, Faculty of Science. ↩