Generative AI in education: immediate governance and learner-protection priorities

Consideration of generative AI in education should retain the date and status of International guidance released on 7 September 2023. Later developments should not be read into the position available at publication. Analysis should state the unit of analysis, reference period, coverage, exclusions and treatment of missing information. Material differences in population, setting or method should remain explicit in any comparison. Review of implementation should give particular attention to adverse cases, unequal effects and errors that learners may be unable to identify or remedy after the event.

At the publication date, International guidance released on 7 September 2023 provides the relevant international context for the policy position. For decisions concerning generative AI in education, any consequential application still requires evidence from the affected jurisdiction or institution. Data used for the issue should be interpreted against stable definitions and an identifiable population. A revision or break in series should not be reported as a change in performance. Public information on the issue should state the applicable scope and limitations in terms that affected users can understand, including the basis for any later correction.

Policy context for generative AI in education

At the publication date, International guidance released on 7 September 2023 provides the relevant international context for generative AI in education. Analysis of implementation should state the unit of analysis, reference period, coverage, exclusions and treatment of missing information. Review of the policy position should give particular attention to adverse cases, unequal effects and errors that learners may be unable to identify or remedy after the event.

In this case, the implementation record should distinguish binding duties, policy expectations and institutional choices, including any transition or jurisdictional limitation. Responsibility for the measure should be identifiable at each consequential decision point. For generative AI in education, delegation should identify both the operating role and the body retaining oversight of learner impact. Any indicator used in relation to the issue should distinguish description from causal explanation. Interpretation should retain uncertainty, distributional differences and limits on generalisation.

The principal risks associated with the issue should be assessed as connected conditions. In the context of generative AI in education, an imprecise scope or measure may produce a credible-looking record that does not answer the relevant decision question.

Implementation of the measure should be organised around a decision that can be tested. The record for the measure should identify the responsible function, decision authority and escalation route. For generative AI in education, apparent movement caused by revision should not be attributed to educational performance.

The evidential record for the measure should permit a reviewer to trace the matter from decision to outcome. Any indicator used in relation to the measure should distinguish description from causal explanation. In reviewing generative AI in education, material variation and uncertainty should be reported together with any restriction on wider application.

• Prohibit uses for which evidence or authority is insufficient.
• Review incidents and supplier changes.
• Control personal and confidential information.
• Retain accountable human decision-makers before it informs a consequential decision.
• Classify uses by effect on learners.

Responsibilities and affected parties

Analysis of generative AI in education should state the unit of analysis, reference period, coverage, exclusions and treatment of missing information. Reliability depends on preserving the material distinctions between the matters compared. The relationship between the risks is material: one failed safeguard may remove the evidence needed to activate another. Analysis of the policy position should state the unit of analysis, reference period, coverage, exclusions and treatment of missing information. Comparative findings should not conceal differences capable of changing their meaning.

The review method for the issue should be reproducible. Data used for the policy position should be interpreted against stable definitions and an identifiable population. In work concerning generative AI in education, changes in method, definition or series should remain separate from changes in the underlying result. Risk assessment for the issue should consider severity, reach, duration, recurrence and detectability, with escalation where learner impact may be material.

A conclusion should not imply uniform application where the governing law differs between jurisdictions. For decisions concerning generative AI in education, public communication should not present an aspiration, recommendation or proposed measure as an existing legal duty.

The principal risks associated with the measure should be assessed as connected conditions. In reviewing generative AI in education, a technical capability is not evidence that a use is educationally justified. A formal commitment concerning implementation does not establish effective operation. Review should test how the measure is applied, how exceptions are handled and what remedy is available.

Evidence concerning the issue should be current, attributable and representative of the affected scope. For generative AI in education, material gaps or contradictions should remain visible in the conclusion. Where reliance has occurred, correction may require review of affected decisions as well as amendment of published information.

Any indicator used in relation to the policy position should distinguish description from causal explanation. When examining generative AI in education, a reported result should state how outcomes are distributed and where transfer beyond the observed setting is not supported. Records concerning the issue should remain traceable from source evidence to decision and follow-up. Superseded conclusions should be retained where they informed a material outcome. Records concerning the policy position should remain traceable from source evidence to decision and follow-up.

Neither one indicator nor one control can establish the complete position on the issue. The review should give particular attention to adverse cases, unequal effects and errors that learners may be unable to identify or remedy after the event.