Integrate AI into learning outcomes

New technologies provide new ways of engaging with information and opening up new ways of learning. These technologies demand careful consideration and responsible use. The expansion of AI tools touches on virtually every area of education and poses both challenges and significant opportunities to cautiously explore. Monash aims to prepare students and disciplines to operate in the Age of AI.

To ensure the calibration of our courses for contemporary contexts and as part of addressing the plan set out in the Tertiary Education Quality and Standards Agency (TEQSA) 2024 Request for Information (RFI) on AI in education, Monash is undertaking a thorough review and programmatic adjustment of all coursework.

Reforming assessment design to strategically integrate AI and the consideration of securing assessments requires a review of existing learning outcomes both at the unit level and the course level. From revised learning outcomes, assessments can be designed to accurately represent students’ efforts. This needs critical attention to ensure responsible use and clear demonstration of human achievements. This information of revising learning outcomes is fundamental to assessment re(design) and reform.

There is further guidance and examples available in the AI and assessment pages. A key question pack on integrating AI into learning outcomes has been developed to support discussions — available only with a Monash login.

Program level discussions about learning outcomes

Join Professor Ari Seligmann and Associate Professor Tim Fawns conversation (21 minutes) about program level discussions and thinking through adapting learning outcomes at both a unit and course level. They identify considerations and challenges of where to integrate Generative Artificial Intelligence (GenAI) and how to think about programmatic approaches to assessment. They discuss examples from the Faculty of Business, Faculty of Arts, Faculty of Art, Design and Architecture and Faculty of Information Technology.

In summary

The following video (4 minutes) summarises key considerations in this work of revising learning outcomes.

  • Ensure a shared understanding of what is valued in the learning outcome by creating careful definitions and checking assumptions.
  • Decide what needs to be done individually or collectively and then be clear about what tasks can benefit from AI assistance and those that need to exclude the use of GenAI. Be very clear on the rationale for all of these decisions.
  • Use language that enables diversity, flexibility and student growth across the course.