• Resources for Assessment Redesign

Assessment Redesign with GenAI

In order to help educators redesign their assessment approaches to fully harness the potential of GenAI while mitigating its challenges, we have developed multiple resources to provide teachers with innovative assessment ideas and support during this transition.

Full guide: Redesigning Assessment with Generative AI: A Guide for Teachers (HKU Portal login required)

Leaflets of Assessment Redesign with GenAI

AI-Assisted Peer Review: Facilitating Constructive Feedback and Authentic Learning
Augmented Reality (AR) Based Learning: Bringing Authenticity and Immersion in Assessments
Concept Maps in the Era of Generative AI
Critical Evaluation of AI-Generated Essay
The Essay Plan Approach
Action Speaks Louder: Using OSCE to assess in Engineering and Law
Shifting the focus of assessment
Analyzing and Justifying Work
Collaborative Wiki Project: A Community Approach to Learning
Assessment Beyond Essays: Creative Multidisciplinary Project Showcase
Elevator Pitch: Pitching all the way
Game-Based Learning Assessment: Making Learning Engaging and Authentic
Role-Play Assessment: Bridging Theory and Practice for Authentic Learning
Utilising Limitations of ChatGPT
Reference
  • Chan, C.K.Y. (2023). Is AI changing the rules of academic misconduct? An in-depth look at students’ perceptions of ‘AI-giarism’. https://arxiv.org/abs/2306.03358
  • Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., … & Sandison, C. (2023). ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 1-56.
  • Dai, W., Lin, J., Jin, H., Li, T., Tsai, Y. S., Gašević, D., & Chen, G. (2023, July). Can large language models provide feedback to students? A case study on ChatGPT. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 323-325). IEEE.
  • Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., … & Chen, G. (2023). Can large language models write reflectively. Computers and Education: Artificial Intelligence, 4, 100140.
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