Artificial Intelligence in Higher Ed.

With expertise in prompt and context engineering, I create adaptive learning environments that cater to the needs of students and educators. My proficiency in higher education governance ensures AI solutions are compliant, sustainable, and in line with the intricate policies and regulations that shape the sector.

Book a consultation

If you want to explore a use case, send:

the unit/program context,
what you want AI to support (and what must remain human-led),
the data types involved (none / de-identified / student personal info),
your current policy constraints and timeline. </aside>

Services

AI Governance, Risk, and Assurance of Learning

AI assurance approaches for education use cases (risk register, controls, decision logs, approval pathways) aligned to recognised frameworks.
Draft or refine acceptable-use standards for staff and students (what’s allowed, what’s not, and why), including documentation and acknowledgement norms.
Design “integrity-by-design” assessment patterns that reduce over-reliance on detection and strengthen evidence of learning. </aside>

Capability uplift and professional learning

Run workshops for educators on responsible classroom use, assessment redesign, student guidance, and good documentation habits.
Create student-facing guidance that sets clear expectations (what counts as acceptable assistance, how to acknowledge, how to avoid integrity breaches). </aside>

Safe AI workflow design for educators and students

Design human-led workflows where AI output is treated as draft material requiring verification and attribution (e.g., drafting, summarising, formative feedback, study supports).
Build prompting and verification patterns that prevent “false mastery” and strengthen metacognition (what to check, how to test claims, how to cite/trace). </aside>

Vendor and platform due diligence (privacy + data integrity first)

Develop an AI product selection checklist for education (data handling, retention, training use, access controls, logging, auditability, residency, support model).
Conduct a privacy and risk review for proposed tools (including “free” tools) and advise on safer alternatives and controls.
Align your implementation to privacy obligations and practical controls for personal information minimisation and protection. </aside>

Data integrity and safety: How I Work (Non-negotiables)

Typical engagements

“Safe adoption” package (2–4 weeks)

Use-case triage + risk review + minimum controls + staff/student guidance + implementation plan.

Assessment redesign in an AI-rich environment (2–6 weeks)

Redesign tasks + integrity-by-design measures + marking guidance + student-facing expectations, aligned with TEQSA emerging practice.

AI policy-to-practice uplift (4–8 weeks)

Translate policy into operational artefacts: checklists, templates, approval pathways, training assets, and a repeatable governance workflow.

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