Review AI governance, accountability and decision rights
Responsible AI governance and assurance
AI Quality Readiness Diagnostic for Universities
Establish an evidence-led view of whether institutional governance, assessment, technology, capability and assurance are ready for responsible AI adoption.
The institutional challenge
Connect intention, implementation and evidence.
AI adoption often advances faster than policy, academic practice and institutional oversight. SPECS tests whether responsibilities, controls and evidence are connected well enough to protect academic standards while enabling purposeful innovation.
How SPECS can help
A focused engagement shaped around your context and decision needs.
Assess assessment integrity, learning design and human oversight
Evaluate privacy, data, procurement and technology controls
Prioritise capability, evidence and implementation requirements
Typical outputs
Practical deliverables that support leadership decisions and institutional action.
AI quality-readiness scorecard
Governance and risk heat map
Assessment-integrity findings
90-day plan and 12-month roadmap
What the engagement enables
Improvement that can be owned, evidenced and sustained.
- Clear institutional accountability
- Protected academic standards
- More confident adoption decisions
- Evidence-led implementation and scaling
The SPECS approach
Five connected stages from governance to demonstrable improvement.
- 01
Govern
Clarify purpose, accountability and standards.
- 02
Design
Align the engagement with context and outcomes.
- 03
Enable
Build capability and consistent practice.
- 04
Verify
Test implementation, evidence and effectiveness.
- 05
Improve & Scale
Close gaps and sustain what works.
Start a conversation
What quality priority should your institution address next?
Tell us the review, risk or improvement priority you are working through. A senior SPECS consultant will help you define a useful first step.