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Assessment has become a thorn in the side of many educators over the past three years. First, the rapid shift to remote teaching during the pandemic forced many educators to adopt assessment approaches that they may not have been comfortable with or that they recognized were not ideal for student learning. Then – just as many of us were returning to the more familiar assessment circumstances of in-person classes – OpenAI released ChatGPT. Any assessment with a non-invigilated written component, including the writing of computer code, now raises questions about if – and to what extent – students are making use of generative AI.

At the same time, the events of the past three years have highlighted existing troubles with our assessment practices and prompted us to reflect on the purpose of assessment in teaching. The language of care in teaching that became more prevalent during the pandemic helped to reframe the conversation about academic integrity into a deeper consideration of why students cheat. One culprit is poorly designed assessments, which may:

  • only require students to recall what they have already learned, and/or
  • are mismatched with what students expect to do and learn in the course, and/or
  • unfairly disadvantage some students and not others, and/or
  • have unnecessarily high stakes.

Assessment (re)design thus offers educators the opportunity to have a meaningful impact on issues of academic integrity.

Sask Polytech recently revised the Evaluation of Student Learning Policy (Policy 119) which underscores the importance of being fair and transparent, using a variety of methods, following Universal Design for Learning principles, growing competency through formative assessments, and providing meaningful, authentic assessment opportunities.

The following panel conversation by educators at University of Louisiana System explores how educators can harness AI to create more meaningful and relevant assessments for students. The panel tackles the crucial question of adapting assessment formats to the changing demands of the modern world. It’s worth watching (26 minutes)!

 

 

Suggestions for assignment and assessment design

The next two chapters offer strategies based on faculty experiences and current research, to address the use of AI in assessments and assignments. They include approaches for mitigating AI use, as well as ways to leverage generative AI in assessment design. These strategies aim to promote fair, authentic, and inclusive assessment practices, in which students’ knowledge and skills can be accurately assessed.

We hope there is a path through the resource for all educators, acknowledging that you will each be teaching in different contexts, be at different points of your career, and be working under different conditions. We also encourage you to not go through the resource in isolation – reach out to the Instructional and Leadership Development Centre (ILDC) and discuss your assessment further.

Attribution:

Generative Artificial Intelligence in Teaching and Learning Copyright © 2023 by Centre for Faculty Development and Teaching Innovation, Centennial College is licensed under a Creative Commons Attribution 4.0 International License

Generative Artificial Intelligence in Teaching and Learning at McMaster University Copyright © 2023 by Paul R MacPherson Institute for Leadership, Innovation and Excellence in Teaching is licensed under a Creative Commons Attribution 4.0 International License

Suggestions for assignment and assessment design by the Centre for Teaching, Learning and Technology at UBC is licensed under CC BY-SA 4.0

License

Icon for the Creative Commons Attribution 4.0 International License

Generative Artificial Intelligence in Teaching and Learning Copyright © 2023 by Sask Polytechnic is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.