Assessment provides sufficient, trustworthy and inclusive evidence that intended outcomes and award standards have been achieved, including proportionate assured demonstration where independent capability matters. 

• Assessment provides valid evidence of learning at assessment and programme level 
  Assessment is designed to be valid and appropriate for the learning outcomes it assesses and, collectively, a programme’s assessment strategy provides coherent and sufficient evidence that the programme outcomes and the standard of the award have been achieved. Assessment is designed to reasonably verify the student’s own learning and capability, rather than the output of an AI1 tool, against the learning outcomes, including, where AI use is permitted, the student’s direction and critical evaluation of that use. Assessment design, and the facilitation of assessment in practice, remain fair, reliable and inclusive.  
  
  Assessment also provides confidence in students’ foundational knowledge and independent judgement, and their ability to use AI critically, effectively and responsibly. Each is evidenced in its own right across the programme, so that evidence of one is not taken as evidence of the other.  
• Summative assessment includes strategic points of assured demonstration across the programme  
  Students are likely to use AI differently when they know there are clear points of accountability for their learning. Summative assessment therefore includes some elements, not necessarily all assessments, that directly verify individual attainment of the learning outcomes under conditions where AI use can be controlled or verified. Providers identify meaningful points across the programme where independent learning and/or capability must be demonstrated. This assurance may be designed at programme level rather than repeated unnecessarily in every module. That demonstration can take different forms, such as dialogic, practical, observed, placement-based, simulation, presentation and questioning, or structured defence of an artefact. In doing so, providers review their assessment portfolios holistically, identifying where individual assessments are vulnerable to unacknowledged or inappropriate AI use, and ensure that, taken together, assessments build coherent evidence of student learning and achievement across the student journey.  
• All assessments have a clear and justifiable design rationale for their position on the use of AI tools  
  Whether the use of AI tools is permitted, expected or restricted in assessments, this is a deliberate design choice, explicitly justified, integrated into assessment rubrics and closely aligned with the assessment's intended learning outcomes. Because students may have the opportunity to use AI regardless of whether it is permitted, where the validity of an assessment depends on AI not being used, this is secured through the design of assessment rather than through assessment briefs alone. Where AI use is restricted, the rationale makes clear that the restriction does not extend to assistive technologies or reasonable adjustments. 
• Outright prohibition of AI and over-reliance on AI-detection tools are not effective primary safeguards of standards   
  Total bans on AI use are difficult to enforce in practice, and current AI-detection tools are widely reported to carry significant rates of false positives and false negatives, raising concerns about reliability and equity. Given this evolving and contested evidence base, providers should not rely on detection software alone as a primary safeguard; robust, valid assessment design, combined with meaningful assessment security actions, is a more effective and sustainable approach to ensuring academic integrity. 
• Assessment security is supported by realistic, meaningful and transparent academic regulations and accessible policy and guidance, not by relying students’ own academic integrity alone 
  Assessment security consists of meaningful actions to prevent and detect attempts by students to avoid accountability for their learning. Detection here means gathering evidence of learning, for example through questioning or evidence of process, rather than reliance on AI-detection software. These actions are underpinned by enforceable academic regulations, and explained through policy and student-facing guidance that are clear and accessible. There are clear, visible mechanisms by which regulations on AI use can actually be enforced and checked: for example, where students are permitted to use AI for some tasks but not others. It is insufficient, and unfair, to set rules that providers have no practical means of verifying, for instance, distinctions between permitted and prohibited AI use that cannot be evidenced, checked, or distinguished after the fact, and then rely solely on students' individual academic integrity to ensure compliance. This approach transfers all responsibility for assessment security onto students, and risks penalising honest students who gain no advantage from AI use while others who breach the rules go undetected. 

Assessment promotes learning, feedback, evaluative judgement, AI capability, agency and progressive development.

• Assessment drives learning, building foundational knowledge, skills and competencies over time, valuing process alongside outcome
  Assessment is part of the learning process, not just its endpoint. Tasks are sequenced across a programme so that foundational knowledge, key skills and competencies develop cumulatively, with each assessment building on the last. Students are assessed on how they develop, justify and refine their work as well as on the final product. Feedback is timely, effective and designed to be applied to improve later tasks. Students develop assessment literacy, including an understanding of the standards against which their work is judged and the ability to evaluate their own work, and are given structured opportunities to act on feedback and show how they have done so. In this way, assessment advances each student's learning while also giving credible evidence of what they have achieved.
• Assessment is designed across the programme to ensure that summative assessment is proportionate and balanced with formative assessment
  Overloaded assessment strategies and an imbalance toward summative assessment increase both student and marker burden while narrowing what is actually being tested, so portfolios are deliberately designed at programme level to balance summative and formative assessment approaches. Summative assessment is proportionate to what must be assured. This is a matter of purpose rather than volume: frequent summative testing may remain appropriate where it is secure and serves a clear purpose. Formative tasks are designed so that students undertake and value them, for example by drawing on them in later summative assessment through reflection on, or defence of, their work.

Assessment supports equitable progression and achievement and prepares students to exercise judgement and use contemporary tools responsibly in further learning and professional practice.

• Assessment is inclusive and accessible by design
  Assessment is designed to be inclusive and accessible from the outset, rather than adjusted after the fact. Providers meet their duties under applicable equality legislation², including their duty to anticipate reasonable adjustments. Beyond this, formats, timings, instructions and submission methods reflect the diversity of the student population and minimise barriers that are not relevant to what is being assessed, so that students are not disadvantaged by the design itself. This reduces reliance on individual adjustments, which continue to be provided where required. Where AI use is permitted or expected, providers ensure that students have equitable access to the tools required. Because the inclusion effects of AI-responsive assessment designs are not yet well evidenced, providers will evaluate new designs as they are introduced, working with students, including disabled students, as partners in assessment redesign and implementation as well as in evaluation, and act on the findings.
  
  Generative AI is considered distinct from assistive technologies and reasonable adjustments. Restrictions on AI use in assessment are framed so that they do not limit students’ access to assistive technologies or agreed adjustments, and this distinction is made clear to staff and students. Inclusion is integral to validity and is not compromised by measures to strengthen assessment security.
• Critical AI Literacy/ capability is a graduate attribute
  Students develop the capabilities to use AI tools effectively, critically and ethically, and to understand the benefits and limitations of AI, as core preparation for professional and personal life, along with the foundational discipline knowledge required to understand, interpret and verify AI outputs.