Governance

A policy that cannot reach the assessment is not yet a practice.

Akanya is designed to carry an institution’s position on responsible AI use into the place where the work is actually done: the task itself.

Principles the platform is being built against.

The lecturer decides

The lecturer controls assessment requirements, reviews the draft questions and approves the paper. Akanya does not set an institution's academic standards.

In practice: Review and edit generated questions before approving an assessment. Drafting assistance does not remove the approval step.

Assurance by design, not by observation

Confidence comes from how the task is built, not from watching the student. No cameras, no biometrics, no additional hardware.

In practice: Review the student's initial answer, response to a challenge and final judgement rather than a webcam recording.

Proportionality

Deeper reasoning is applied where the consequence of a weak judgement is highest. A weekly tutorial does not carry the load of a capstone.

In practice: Agree the learning outcomes, question demands and time available before testing an assessment with participants.

Evidence a lecturer can point to

A reasoning record is meant to support academic judgement in a review, not to replace it. A person makes the decision.

In practice: Use Review to inspect a participant's reasoning journey. Where Marking Assist is used, the lecturer reviews its suggestion and makes the final marking decision.

Accessibility is not optional

Low-bandwidth written interaction on the existing device, with configurable accommodations, so assessment adaptation does not depend on what a student can afford.

In practice: Confirm device access, timing and accommodation needs with the institution before any student pilot. Do not assume one setup fits every learner.

Say what is built

Anything illustrative, in development or on the roadmap is labelled as such across this site. Akanya is in active development.

In practice: The public demo uses scripted responses and sample insights. It does not run live AI marking or present the sample cohort as an actual pilot result.

This page describes a design approach, not a legal or regulatory opinion. Akanya has not completed a formal legal, privacy or accreditation review, and nothing here should be read as a compliance guarantee. Institutions remain responsible for their own policy, and specialist input would be appointed where a pilot requires it.

Translating policy into practice

Institutional AI policy becomes repeatable assessment practice.

Institutional AI policies establish the principles. Akanya helps translate those principles into repeatable assessment practice, so lecturers are not left to interpret broad policy statements alone.

  • Akanya could hold the permitted-use position at the level of the assessment rather than the policy document, so a lecturer sets it once when designing the task and the student sees it where the work happens.

    How Akanya could operationalise this. Developing alongside institutional pilots.