Core Principles
Assessment redesign should be guided by a clear set of educational principles that safeguard academic standards while supporting meaningful learning. Assessment should prioritise:
1 Validity
Ensuring that assessment tasks genuinely measure the knowledge, skills, and competencies they are intended to assess, even in contexts where AI tools are widely available.
2 Fairness and Equity
Recognising differences in access to technology, levels of AI literacy, language background, and learning needs, and designing assessment that does not disadvantage particular groups of students.
3 Transparency
Clearly communicating expectations around the purpose of assessment, criteria for success, and the extent to which AI use is permitted or restricted.
4 Accessibility and Inclusion
Aligning assessment design with Universal Design for Learning (UDL) principles and offering diverse ways for students to demonstrate their learning.
5 Alignment with Learning Outcomes
Ensuring that decisions about assessment format, level of supervision, and AI use are driven by what students are expected to know, understand, and be able to do.
In Practice
In practice, this means placing greater emphasis on:
- Higher-order thinking skills, including analysis, evaluation, synthesis, and creation
- Authentic application of knowledge in realistic or discipline-relevant contexts
- Demonstration of reasoning processes, not just final products
Educators should critically consider whether the use of GenAI in a given assessment supports or undermines the intended learning outcomes. Where AI use is appropriate, expectations should be explicit and supported through the development of student AI literacy. Where independent performance is required, this should be clearly justified and communicated.
Together, these principles ensure that assessment redesign remains pedagogically grounded, ethically informed, and responsive to the realities of learning in a world where AI continues to become increasingly prevalent.
AI Literacy
Assessment redesign and AI literacy are intrinsically linked. As GenAI becomes embedded in learning, work, and everyday life, students need more than technical familiarity with tools — they need the capacity to engage with AI critically, ethically, and effectively.
AI literacy includes the ability to:
- Critically evaluate AI outputs, recognising inaccuracies, bias, hallucinations, and limitations
- Understand how and when AI can be appropriately used, and when independent thinking or professional judgement is required
- Use AI ethically and transparently, including disclosure of use and respect for academic integrity and data privacy
- Integrate AI as a support for thinking, problem-solving, and creativity — not as a substitute for learning
- Recognise the role of the human in the learning process
- Understand how AI may influence thinking, behaviour, and decision-making
At programme level, teams should determine:
- Where AI literacy is an explicit learning outcome
- Where AI literacy contributes to broader graduate attributes
- Where foundational disciplinary knowledge must be demonstrated independently of AI support
Assessment design should therefore include a deliberate mix of:
AI-Restricted Tasks
Where independent demonstration of knowledge, understanding, or professional capability is required
AI-Supported Tasks
Where students are expected to engage with AI tools critically and responsibly as part of the learning process
Assessment redesign is therefore not only about preventing misuse, but about educating students to become informed, critical, and responsible participants in this rapidly changing technological world.