Assessment & feedback
Whether you are amending an existing assessment or developing a new assessment from scratch, learning designers can support you with the process.
For major amendments, including assessment amendments, we work alongside colleagues in the Teaching Learning Support Office and the Quality, Standards, Design and Enhancement (QSDE) committee to support you through each stage of the approval process.
Read more about TLSE and the QSDE approval process
Evaluate an assessment strategy
You may wish to review and evaluate your assessment strategy–perhaps you’re updating a unit or programme, or you feel the current strategy isn’t meeting student needs. You may wish to embed University goals such as digital capabilities, employability, inclusiveness, and sustainability. So, where do you start?
Reviewing an assessment strategy
Here are some key questions to help you begin your assessment review:
- Which programmes or course units will be included in this review?
- Why do I want to review the assessments? Are there specific issues I’m trying to solve?
- What is the aim of this review?
- What feedback have I obtained from students and staff about the current assessment strategy?
- Have I reviewed the university guidance on assessment and feedback, including guidance on Maximum Summative Assessment?
- What is the timeline for this review?
- Have I reviewed the process for programme/course unit amendment noting critical deadlines and milestones?
For further information and guidance, please book a consultation to get you started.
See also the HTA Assessing Online page
Design / re-design an assessment
You may be designing a new assessment for an existing or a new course unit. In both cases, there are some steps you can take to ensure that the assessment you design sits appropriately in a wider (programme) assessment strategy.
Designing a new assessment means first understanding the constraints which exist in the form of university, faculty or school regulations or guidelines such as the Maximum Summative Assessment policy, the Attendance and Participation policy, programme and unit learning outcomes, and the precise nature of the methods you plan to use to measure achievement.
Learning Design can support you to explore :
- assessment for learning (formative)
- assessment of learning (summative)
- assessment rubrics
- peer assessment
- feedback methods
- developing students’ assessment literacy
For further information and guidance, please book a consultation to get you started on your assessment design journey.
See also the HTA Assessing Online page
Explore student co-creation of assessment
If you are thinking about involving students in the evaluation or redesign of your assessment strategy or design, great! There are many potential benefits to this from both a student experience and a pedagogical perspective.
For further information and guidance, please see our Student Co-creation page or book a consultation to get you started on your co-creation journey.
Assessment Toolkit (Flexible Learning Programme)
A one-stop-shop resource for assessments has been developed by the FLP (Flexible Learning Programme)
- Access the Assessment Toolkit
- Find out more about the Flexible Learning Programme
Explore AI resistant assessment
AI-resistant assessments are designed to minimise the risk of academic dishonesty facilitated by AI tools. These assessments focus on critical thinking, creativity, and real-world application, making it difficult for AI to generate valid responses.
AI-Detection or AI-Resistant Assessment?
AI-detection tools struggle to keep up with rapidly evolving AI systems, making it hard to catch all AI-generated content. They also raise ethical concerns, like privacy issues and the risk of wrongly accusing students of cheating.
AI-resistant assessments are designed to be difficult for AI to complete, promoting genuine learning. They focus on tasks that require real-time demonstration of skills, critical thinking, and problem-solving, which helps maintain academic integrity and develop valuable skills.
Key Approaches
| Approach | Description | Examples | Benefits |
| Authentic Assessments | Tasks reflecting real-world challenges relevant to the subject | Case studies, projects, simulations, portfolios | Encourages practical application, reducing AI-generated content usefulness |
| Oral Assessments | Assessments through spoken responses | Viva voce, presentations, debates | Requires immediate, personalised responses, challenging for AI |
| Collaborative Assessments | Group-based tasks requiring collective effort | Group projects, peer reviews, collaborative research | Encourages teamwork and accountability, reducing AI effectiveness |
| Process-Oriented Assessments | Focus on the learning process rather than the final product | Reflective journals, process logs, draft submissions | Emphasises development and iteration, making AI replication difficult |
| Dynamic and Interactive Assessments | Tasks evolving based on student responses | Adaptive quizzes, interactive simulations, problem-solving scenarios | Requires active engagement and adaptability, challenging for AI |
Examples of AI-Resistant Assessments
| Example | Description |
| Advanced Problem-Solving | Complex, multi-step problems requiring the application of advanced theoretical concepts or real-world data interpretation. |
| Authentic, Context-Specific Assignments | Assignments that are personalized and context-specific, making it difficult for AI to generate relevant responses. |
| In-Class and Group Assignments | Incorporating more in-class and group assignments to promote collaboration and reduce the risk of AI misuse. |
| Interactive Learning Platforms | Platforms that support interactive and adaptive learning, making it difficult for AI to generate appropriate responses. |
| Oral Interviews | Assessments that include an oral or performance element to test understanding or application of knowledge. |
| Physical Tasks and Experiments | Assignments that require physical interaction, such as conducting a laboratory experiment, building a physical model, engaging in fieldwork, or a live performance. |
| Video Submissions | Using video submissions for assessments to ensure the authenticity of student work and improve communication skills. |
Learning Technologies for AI-Resistant Assessment
| Technologies we support |
AI-resistant assessments |
| Blackboard / Canvas | Group assignments and AI-resistant exam questions |
| Cadmus |
Authentic assessment design Monitoring student progress Real-time analytics help identify suspicious activity, such as copy-pasting or unusual time patterns Academic support and feedback throughout the assignment process |
| Canvas Studio | Video submissions |
| Podcast Studio | Video submissions |
| Teams | Online/recorded group presentations, debates |
| Voicethread | Video submissions |
Resources
Assessment for the Future | Institute of Teaching and Learning
This resource was prepared with the aid of ChatGPT -4o(https://chatgpt.com) and Copilot (https://copilot.microsoft.com)
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