Open assessments allow students to complete authentic, real-world tasks, supporting critical thinking, ethical judgement, and the application of knowledge in context.
Open assessments allow students to complete authentic, real-world tasks, supporting critical thinking, ethical judgement, and the application of knowledge in context.
Open assessments are designed to reflect the way knowledge and skills are applied in real-world contexts. These tasks intentionally allow students to use a wide range of tools and supports - including generative GenAI, research materials, collaboration, and digital resources - in iterative, self-paced ways because this mirrors the complexity and practices of the worlds they'll enter upon graduation.
Open assessments focus on:
Open assessments do not compromise academic standards. Instead, they help students learn how to work effectively, ethically and creatively in information-rich, GenAI -enabled environments.
Open assessments are vital in today's world where tools like GenAI, collaboration platforms, and digital resources are part of everyday work and life. They:
Open assessments are not "less serious" or "unimportant" - they simply reflect a different set of learning goals. They require careful design and teaching, especially when GenAI is in play.
To promote academic integrity in open tasks:
Open assessments are best used when the aim is to:
Importantly, open assessments complement secure assessments. Together, these two modes ensure that:
At the course level, open assessments should be intentionally mapped alongside secure assessments to ensure comprehensive coverage of learning outcomes. For some disciplines, open assessments will dominate. For others, they will appear less frequently.
Educators could consider:
Design open assessments with the same rigour and intention as secure assessments - just with different assumptions about the approaches students might take to completing the task.
Open assessment tasks:
Below are tip sheets regarding some of the more popular forms of open assessment tasks. These tip sheets include information about the type of assessment, the learning outcomes they best serve, the learning design considerations associated with them, and how GenAI could be integrated.
Chan, C.K.Y., Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43). https://doi.org/10.1186/s41239-023-00411-8
Cui, Y., Meng, Y., Qian, X., & Tang, L. (2025). Developing a college student feedback literacy scale for the GenAI context. Assessment & Evaluation in Higher Education, 1-14. https://doi.org/10.1080/02602938.2025.2495048
Foung, D., Lin, L., & Chen, J. (2024). Reinventing assessments with ChatGPT and other online tools: Opportunities for GenAI-empowered assessment practices. Computers and Education: Artificial Intelligence, 6, 100250. https://doi.org/10.1016/j.caeai.2024.100250
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