University of Technology Sydney released this week the latest result of its partnership with Microsoft Office’s Azure AI.
The project is an example of a growing number of institutions partnering with AI providers to trial solutions to specific problems in a secure enterprise environment.
In a 2-day hackathon, UTS academics and learning designers worked on providing prompts to create ‘CILObot’: a ChatGPT that can draft Course Intended Learning Outcomes (CILOs).
CILOs are concise statements that articulate what students are expected to know, understand, or demonstrate at the end of a course. For example: On successful completion of this course, graduates will be able to evaluate the healthcare needs of diverse societal groups.
While they may seem to magically appear on course documents, the formation of CILOs presents a real challenge. Here’s why:
Clear learning outcomes are really (really) important
For students, CILOs provide clear expectations, an opportunity to set goals and a roadmap to evaluate own progress against specific, achievable outcomes. For academics, they provide a framework for designing, delivering, and assessing the course.
But their value extends beyond the classroom. CILOs are used by prospective students, quality assurance teams, accreditation and evaluation bodies, employers and industry, and even alumni reflecting on learning experiences and employability.
But clear learning outcomes can be really (really) challenging to write
CILOs should be achievable, assessable and written in plain language. Although there are well-recognised principles to follow, wording needs to meet individual program objectives and institution-specific requirements around style, length, etc.
There are a range of further considerations, including accreditation requirements, interdisciplinary approaches and the characteristics of the student cohort. Institutions with a global focus or diverse student body, for example, may emphasise CILOs that promote cultural sensitivity, global awareness, and inclusivity.
In other words, CILOs are an opportunity for institutions to reinforce unique characteristics, values, and goals.
As the UTS team says, “effective implementation of CILOs requires both the subject matter expertise of academics and the pedagogical knowledge of learning designers”.
The results of the UTS – CILObot project are promising. After a day of work, ‘CILObot’ demonstrated it could rapidly refine 26 learning outcomes down to the UTS goal of 6, which can then be reviewed and validated by disciplinary experts.
The UTS team says that although the chatbot is not perfect, “in 30 seconds it can generate a coherent first draft…[which] would normally be a minimum of 3 hours’ work by the course director working with the lead academics in the program – that’s a pretty impressive trade-off!”
Impressive indeed. The project presents an emerging opportunity to use AI as a co-design tool to enhance the efficiency of academic team processes AND, perhaps more importantly, the quality and consistency of CILOs across institutions. Both of these outcomes will ultimately benefit students.
For more details, read the article by Simon Buckingham Shum (UTS Connected Intelligence Centre), Sharon Coutts, Ann Wilson, Michaela Zappia (UTS Institute for Interactive Media and Learning) and Susan Gibson (UTS Data Analytics and Insights Unit) here.
