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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.

Designed forUniversity boards and executive leadersAcademic, quality, technology and data leadersInstitutions moving from experimentation to managed 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.

01

Review AI governance, accountability and decision rights

02

Apply a grading-governance test to any AI use that may influence marks, feedback or progression

03

Evaluate privacy, data, procurement and technology controls

04

Prioritise capability, evidence and implementation requirements

Featured diagnostic test

Test whether human accountability is real before AI influences a grade.

The grading-governance test examines a defined use case against twelve evidence requirements. It distinguishes controlled assistance from an arrangement in which academic responsibility has effectively been transferred to a model or vendor.

  1. 01

    Purpose, scope, prohibited uses and accountable academic authority

  2. 02

    Alignment with learning outcomes, approved criteria and disciplinary judgement

  3. 03

    Validation against qualified human markers using representative student work

  4. 04

    Reliability, uncertainty, fairness, accessibility and subgroup impact

  5. 05

    Privacy, consent, security, intellectual-property and vendor controls

  6. 06

    Meaningful human review, override, student transparency and appeal

  7. 07

    Moderation, anomaly monitoring, version control and incident response

Typical outputs

Practical deliverables that support leadership decisions and institutional action.

AI quality-readiness scorecard

Governance and risk heat map

AI-assisted grading governance test and decision record

90-day plan and 12-month roadmap

What the engagement enables

Improvement that can be owned, evidenced and sustained.

The SPECS approach

Five connected stages from governance to demonstrable improvement.

  1. 01

    Govern

    Clarify purpose, accountability and standards.

  2. 02

    Design

    Align the engagement with context and outcomes.

  3. 03

    Enable

    Build capability and consistent practice.

  4. 04

    Verify

    Test implementation, evidence and effectiveness.

  5. 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.