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Exploring novel assessment approaches in Health policy and Management using GenAI

• Dr Carlos Bruen Carlos Bruen is Assistant Professor in Health Policy and Management and Course Director of the MSc in Health Policy and Management, Trinity College Dublin. His research and teaching (as module lead/co-lead at postgraduate and doctorate levels) focuses on different dimensions of health policy at national and international levels, including health policy and governance; comparative health systems and policy analysis; and knowledge translation and mobilisation for policy and practice.

Context

We began our explorations in GenAI within the MSc in Health Policy and Management, a new postgraduate-level programme (as of September 2023) at the Centre for Health Policy and Management, School of Medicine. This programme has both full-time and part-time students.

What was your goal in utilising GenAI as part of the teaching process?

Our assessments combine presentations and take-home assignments. We recognised the risks and opportunities of GenAI with these assessment formats. Taking the position that GenAI is staying with us, we sought to find ways to:

1. teach students about GenAI;
2. set out a clear policy on the use of GenAI in the context of academic integrity;
3. integrate GenAI in assessments in novel ways.

How did you use GenAI to enhance teaching, learning and/or assessment?

1. Teaching students about GenAI:
   We opened the academic year with a session on GenAI (what it is, when it is appropriate/inappropriate to use it, ethical and sustainability issues related to GenAI, and other new AI tools that might be of use (e.g. Elicit – an AI Research Assistant). In research methods training, we look in more detail on alternative AI tools like Elicit for literature reviews/scanning.
2. Setting out a clear policy on the use of GenAI in the context of academic integrity:
   We included a section in the course handbook that considers the ethical context of GenAI, as well as procedures for declaring where GenAI has contributed to content development within assignments.
3. Integrating GenAI in assessments into novel ways:
   We submitted a prompt to GenAI on a module-related issue. Students were asked to critique the GenAI output drawing on the relevant literature and class sessions. This is linked to critical thinking learning outcomes at module and course levels.

What were the outcomes of using GenAI in this way?

Students have spoken positively about time being given to learn more about GenAI, and about our focus on being explicit about its risks and opportunities.

We will run the module assignment again next academic year as students performed well in critiquing the GenAI output. It also provided them with an opportunity to see some limitations of GenAI outputs.

What did you learn as part of this process and is there anything you would do differently?

This is our first year incorporating GenAI. It is a significant learning curve and was helped by attending presentations and discussions by peers who are grappling with similar issues. By running our draft assignments repeatedly through GenAI, patterns are emerging on how GenAI structures particular questions. We will need to continue doing these to ensure we are alert to developments in GenAI.

Also, while we have set out guidelines and policies on declaring GenAI use, no student has declared their use of it this year. We will be revising these policies and guidelines and developing related class activities for next year’s Student Orientation.