Choosing Assessment Formats
A useful place to start when choosing an assessment format is the learning you want students to demonstrate. From there, consider the purpose of the assessment and the context in which it will be used, including relevant disciplinary and professional requirements, class size, available support, accessibility, and grading workload. Assessment format is one design decision among many, and different formats may be appropriate depending on the learning you want students to show.
The format alone does not determine the complexity or quality of an assessment. Consider what the task asks students to do, the criteria used to judge their work, the choices and interactions built into the task, and the opportunities they have to practice, receive feedback, and use it to improve.
Before choosing an assessment format
Start with the learning you want the assessment to make visible. Consider:
- What purpose will this assessment serve: helping students develop their learning, demonstrating achievement for grading, or both?
- What do students need to demonstrate: knowledge, analysis, application, creation, communication, reflection, performance, or something else?
- Is the format itself part of the learning outcome, or could students demonstrate the same learning in more than one way?
- What would an authentic or meaningful demonstration of learning look like in your discipline or course?
- Could the format require students to demonstrate abilities that are not part of the learning outcome or introduce other unnecessary barriers?
- What is feasible given your class size, available time, teaching assistants, technology, and grading workload?
- What interactions does the format require (for example, with instructors, peers, external audiences, or others)? Are those interactions feasible and relevant to the learning?
- How does this assessment fit with other assessment tasks in the course or program?
The well-established Assessment Design Decisions Framework provides a practical way to further unpack these questions in relation to the purposes and contexts of assessments, learner outcomes, tasks, and associated feedback processes. More recent scholarship has extended this work by examining inclusive and authentic assessment, assessment in digital environments, and the implications of generative AI for assessment validity and design (see Learn More).
Where the format itself is not part of the learning outcome, consider whether students could demonstrate the same learning in different ways. Providing multiple means of action and expression is one way assessment design can support Universal Design for Learning (UDL) and reduce unnecessary barriers.
Assessment validity and generative AI
Generative AI challenges traditional approaches to assessment far beyond students using it covertly on a quiz or essay. As AI transforms how your students access, create, and apply knowledge, you need nuanced strategies to evaluate what they can demonstrate both with and without technological support.
- Prioritize assessment validity over surveillance. Most universities, including the U of A, reject AI detectors because they are fundamentally unreliable. Other than in-person, proctored environments, you cannot reliably detect or prevent AI use. Assessment expert Phil Dawson recommends focusing instead on assessment validity: does a submission reliably demonstrate that your student has met the learning outcomes? If AI can easily complete an assignment, shift toward formats that capture genuine learning even when AI tools are accessible.
- Use supervised settings for AI-free evaluation. When it is important to measure what students can accomplish entirely on their own, administer assessments in a supervised setting. Syllabus statements and honor declarations offer only an illusion of enforcement, leaving compliant students at a disadvantage while changing little about actual behavior.
- Focus on process rather than product. Try shifting your focus from evaluating a polished final output to capturing your students' learning process over time. Build authenticated checkpoints into your course where students demonstrate evolving thought in real time, such as:
- Live draft discussions
- In-person lab sign-offs
- Documented reflections on peer feedback
- Embrace the trade-offs. You cannot maximize security, authenticity, and workload sustainability all at once. Identify your non-negotiables and grant yourself permission to compromise. For instance, sacrificing absolute security on a take-home task may be a worthy trade-off for deeper student engagement. Most importantly, give yourself permission to iterate as technology continues to evolve.
Learn more about AI and Assessment
Assessment format comparison guide
Different assessment formats make different kinds of learning visible. Use this guide to compare options against your learning outcomes, course context, and what you need students to demonstrate.
Bloom’s Revised Taxonomy and Fink’s Taxonomy of Significant Learning offer useful lenses for thinking about the learning involved in an assessment. You do not need to classify every assessment using a taxonomy; use these frameworks where they help clarify what students are being asked to do. (Learn more: Developing Learning Outcomes)
Format Examples
Format: Mixed-format exam (multiple choice, short answer, problem solving)
What students can demonstrate:
- Recall and explain knowledge
- Apply concepts
- Analyze information
- Solve problems
- Evaluate evidence
Strengths:
- Can efficiently assess learning across a broad range of course content
- Scales well in larger classes
- Some question types can be automatically graded in Canvas
Design Considerations:
- Strict time constraints can introduce barriers when speed is not part of the intended learning
- May overemphasize recall if questions are not designed for higher-order thinking
- Consider the stakes and timing: relying heavily on a single final exam can limit opportunities for feedback and multiple demonstrations of learning
- Requires careful attention to question design, accessibility, exam conditions, and manual grading load
- Learn more: Universal Time Multipliers
Format: Essay/long-form writing
What students can demonstrate: Analyze, synthesize, evaluate, develop, and support arguments, and communicate complex ideas in writing
Strengths: Allows students to develop and sustain an argument, work with evidence, and practice disciplinary writing
Design Considerations:
- Can be time-intensive to grade
- Benefits from clear criteria, scaffolding, and opportunities for feedback
- Places greater emphasis on written communication skills
- Can introduce barriers when written communication or language proficiency is not part of the intended learning
Format: Presentation (in-class, recorded, podcast, video)
What students can demonstrate: Explain, apply, synthesize, create, and communicate learning to an audience
Strengths:
- Useful when communicating to an audience is part of the learning
- Different modes can also allow students to work with spoken, visual, or recorded forms of communication
Design Considerations:
- May require substantial class time or technology
- Oral or public performance can introduce barriers when those abilities are not part of the intended learning
- Provide clear expectations, preparation, and appropriate flexibility
Format: Project
What students can demonstrate: Apply, integrate, create, evaluate, solve problems, and make connections across areas of learning
Strengths: Gives students time to make decisions, work through complex problems, and apply learning in a larger task
Design Considerations:
- Can require significant instructor and student time
- Benefits from milestones, clear criteria, feedback, and attention to how individual and group contributions will be assessed
Format: Portfolio
What students can demonstrate: Integrate learning, demonstrate development over time, select and justify evidence, and reflect on learning
Strengths: Makes development over time visible and asks students to select, organize, and explain evidence of their learning
Design Considerations:
- Requires clear criteria, milestones, and examples to make expectations visible
- Reviewing portfolios can be time-intensive
- Students may need support selecting, organizing, and reflecting on evidence
Format: Case Study
What students can demonstrate: Apply, analyze, evaluate, make decisions, and connect theory with practice
Strengths: Allows students to apply concepts and make decisions in a realistic or disciplinary context
Design Considerations:
- Developing or selecting effective cases can require significant preparation
- Ensure scenarios do not depend on unstated cultural, local, professional, or disciplinary knowledge that is not part of the intended learning
Format: Concept Map
What students can demonstrate: Identify, organize, and explain relationships among concepts; integrate and synthesize knowledge
Strengths:
- Makes relationships among ideas visible
- Can reveal misconceptions or gaps in understanding
Design Considerations:
- May be unfamiliar to some students and require modelling or practice
- Assessment criteria should focus on the quality of relationships and reasoning rather than visual polish
Format: Reflective Journal
What students can demonstrate: Reflect, make connections, evaluate experiences or decisions, and examine development in thinking or practice
Strengths:
- Can provide a lower-stakes space for reflection and make growth in thinking visible
- Can provide insight into students’ reasoning and development
Design Considerations:
- Requires clear prompts, criteria, and examples
- Reflection may be unfamiliar to some students and may need modelling or scaffolding
- Make the purpose of reflection clear and consider how grading may shape what students feel able to share
Select the appropriate format
Across a course, different assessment tasks can help students develop and demonstrate different kinds of learning. Consider different formats when they elicit relevant evidence of different learning outcomes or provide appropriate flexibility.
- Where appropriate, offer meaningful flexibility in topic, format, tools, or other aspects of the task when these are not part of the learning outcome.
- Keep equivalent options aligned to the same outcome and standards, with clear criteria for each format.
- Look across the course: Do students have opportunities to practice, receive feedback, apply learning, and build toward more complex work?
For example, a podcast and an analytical essay may both be appropriate if the outcome is to analyze evidence and communicate an argument. If disciplinary writing is itself part of the outcome, those formats would not be equivalent.
Putting it into practice
As you select or reconsider an assessment format, ask:
- Alignment and authenticity: What do students need to demonstrate, and does this format allow them to do so in ways that reflect disciplinary practice or meaningful application?
- Flexibility and inclusion: Where in your course could students have more than one way to demonstrate the same learning outcome when the format itself is not part of what you are assessing? Could any requirements of the format create barriers unrelated to that learning?
- Feasibility: Is the assessment manageable for students and for you, given the time, class size, available support, and required feedback?
- AI: What role, if any, should generative AI play in completing the assessment, and how will you communicate those expectations?
Learn more
Bearman, M., Dawson, P., Boud, D., Bennett, S., Hall, M., Molloy, E., & Joughin, G. (2014). Assessment design framework. Assessment Design Decisions.
Bearman, M., Dawson, P., Boud, D., Bennett, S., Hall, M., Molloy, E., & Joughin, G. (2016). Support for assessment practice: Developing the Assessment Design Decisions Framework. Teaching in Higher Education, 21(5), 545–556.
Bearman, M., Nieminen, J. H., & Ajjawi, R. (2023). Designing assessment in a digital world: an organising framework. Assessment & Evaluation in Higher Education, 48(3), 291–304.
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2026). The wicked problem of AI and assessment. Assessment & Evaluation in Higher Education, 51(4), 736–752.
Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005–1016.
French, S., Dickerson, A., & Mulder, R. A. (2024). A review of the benefits and drawbacks of high-stakes final examinations in higher education. Higher Education, 88, 893–918.
Tai, J., Ajjawi, R., Bearman, M., Boud, D., Dawson, P., & Jorre de St Jorre, T. (2023). Assessment for inclusion: rethinking contemporary strategies in assessment design. Higher Education Research & Development, 42(2), 483–497.
Zhan, Y., Boud, D., & Du, Z. (2025). Designing for authentic assessment: A scoping review. Higher Education.