This session is part of the wider ‘AI Literacy’ webinar series, check out some of the subjects covered in future sessions here and join us as a member to receive free access!

Artificial intelligence is increasingly present in higher education, but many staff are unsure what AI actually is, how it is being used, and what this means for their role.

AI systems are shaped by the data they are trained on, and the ways they are used within institutions. In higher education, this has important implications for fairness, transparency, and decision-making across areas such as student success, admissions, marketing, and workforce planning.

This session explores the relationship between data, bias, and AI-assisted decision-making in higher education contexts. It introduces key concepts around data quality, representativeness, and bias, and examines how these factors can influence AI outputs and recommendations. You will consider real and realistic examples of how bias can be introduced through data, system design, or organisational processes.

The session emphasises critical questioning rather than technical detail, helping you develop the confidence to ask how AI systems reach conclusions, what assumptions are embedded in data, and where risks may arise. It also explores the role of professional judgement, transparency, and accountability when AI-supported insights are used to inform decisions that affect staff or students.

Why attend?

Understand how data quality and bias influence AI-driven insights and decisions

Learn how bias can emerge in higher education systems and processes

Build confidence in questioning and interpreting AI-assisted outputs

Who is this session suitable for:

This session is aimed at higher education professionals who are involved in interpreting data, using dashboards or reports, or contributing to decision-making processes. It is particularly relevant to professional services staff, managers, and leaders who want to better understand the implications of AI-assisted decision-making.

What you will do in the session:  

You will be engaged through short case examples, guided discussion prompts, and reflective questions focused on your own institutional contexts. Polls and discussion points will be used to surface different perspectives and encourage critical thinking about data, bias, and decision-making.