Australia's new AI framework for higher education puts equity first
The Australian Centre for Student Equity and Success (ACSES) has released The Australian Framework for Artificial Intelligence in Higher Education, a national guide for how universities can implement AI with care and clarity. The message is direct: integrate AI while protecting human-centred learning, research integrity, and fair access for every student.
The Framework recognises AI's upside in teaching, student support, and research, while being honest about its risks-from bias to academic integrity concerns. It sets expectations for ethical deployment and calls for sector-wide collaboration, not competition.
Why this matters for your institution
Professor Jason Lodge (University of Queensland) stressed that no single university can solve AI's challenges alone and that equity must be the anchor: we cannot allow AI to widen digital divides; it should expand opportunity for all students.
Professor Ian Li (ACSES) noted that AI can lift learning outcomes but could also deepen inequality, particularly for students from equity backgrounds. The Framework offers practical guidance to capture benefits while avoiding harm.
The Framework at a glance
- Human-centred education: Keep human connection, critical thinking, and equity at the core. Use AI with care and clear purpose.
- Ethical and inclusive deployment: Uphold standards for fairness, transparency, and accessibility so AI benefits all students across campuses and placements.
- Policy development: Co-create policies with government, academic staff, students, and active researchers-especially those from diverse backgrounds-focused on priority areas affected by AI.
- Professional learning: Build AI capability across universities and professional bodies so staff can implement and govern AI well.
- Integrity in research: Address risks from generative AI in data production and analysis. Strengthen procedures that protect research quality and integrity.
What to do next in your university
- Run an AI impact audit: Map current and proposed AI use across teaching, student services, research, and placements. Identify equity, privacy, and integrity risks early.
- Create co-governance: Establish a cross-functional group (students, academics, professional staff, IT, legal, government links) to set policy, approve pilots, and review outcomes.
- Adopt equity-by-design: Require accessibility, affordability, and bias testing in all AI procurement and development. Provide non-AI pathways for students who opt out or lack access.
- Strengthen assessment and integrity: Update assessment design, disclosure rules, and detection/verification processes. Teach students how to use AI ethically and transparently.
- Invest in staff capability: Offer targeted PD on AI pedagogy, academic integrity, data governance, and prompt practices. Recognise and reward adoption that improves learning.
- Protect data and ethics: Set standards for data minimisation, model selection, and vendor risk. Document limitations and known failure modes for any adopted tool.
- Pilot, evaluate, iterate: Start small, measure outcomes by student group, and adjust. Share what works across the sector.
Collaboration behind the Framework
This is a sector-wide effort involving Professor Jason Lodge (University of Queensland), Professor Matt Bower (Macquarie University), Professor Kalervo Gulson (University of Sydney), Professor Michael Henderson (Monash University), Associate Professor Christine Slade (University of Queensland), and Associate Professor Erica Southgate (University of Newcastle), with the Australian National AI in Schools Taskforce.
Further reading and capability building
Full report available on the ACSES website. For broader policy context, see UNESCO's resources on AI in education: UNESCO: AI and Education.
If you're building staff capability, explore curated AI upskilling options for education roles: Complete AI Training - Courses by Job.
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