FutureLearning AI Readiness - Principals Demo

1 — Unaware: AI in teaching and learning is not on our radar. There is no school-wide conversation and no shared position.

2 — Emerging: Some teachers are experimenting individually for planning or resources, but there is no school position or shared understanding.

3 — Developing: We have a documented position on AI in teaching and learning. Staff have access to guidance, and teacher professional judgement is clearly the anchor.

4 — Embedding: AI use in teaching and learning is purposeful and consistent across most of the school. Professional learning is ongoing and connected to visible practice.

5 — Leading: We evaluate whether AI is improving learning outcomes, not just workload. Equity of benefit is actively monitored, and we contribute to professional learning beyond our school.

1 — Unaware: We have no guidance for students on AI use and no AI-specific approach to assessment authenticity.

2 — Emerging: Some teachers talk with students about AI on an ad hoc basis, but there is no consistent school position on when and how students may use it.

3 — Developing: We have a clear school position on student AI use that has been communicated to students, staff, and whānau. Students receive some structured learning on what AI is and what honest and appropriate use looks like.

4 — Embedding: Authenticity policy is consistently applied and reviewed regularly. Student AI literacy education is developmental and cumulative. Students can articulate what academic integrity means in relation to AI.

5 — Leading: Students genuinely understand AI’s capabilities, limitations, and ethical dimensions. We actively monitor whether AI use is affecting equity of outcome across student groups, including Māori and Pacific learners.

1 — Unaware: No systematic effort has been made to develop staff capability around AI. Any knowledge staff have is personally acquired.

2 — Emerging: Some informal or self-directed learning is happening but it is uneven. Where PLD has occurred, it has focused on how to use specific tools.

3 — Developing: All staff have access to a structured programme covering what AI tools are, appropriate use cases, ethical dimensions, cultural bias, and privacy obligations.

4 — Embedding: Professional learning on AI is ongoing, differentiated by role and stage, and connected to visible practice. Staff can articulate the pedagogical rationale for AI use, not just how to operate tools.

5 — Leading: Staff capability is strong, broadly distributed, and sustained through professional culture rather than episodic events. PLD success is measured by improved outcomes for students, not teacher confidence alone.

1 — Unaware: I have not yet considered AI as a leadership matter for this school. There is no shared direction and no school position.

2 — Emerging: I am aware that AI is becoming significant in schools but have not yet formed or communicated a clear position. Our responses to AI questions are reactive rather than direction-led.

3 — Developing: I have a personal position on AI in this school and have begun to communicate it to staff. The board has had at least one substantive discussion about AI at a strategic level.

4 — Embedding: Leadership has a clear, communicated position on AI connected to our school’s vision and improvement priorities. The board understands its governance role and contributes to strategic direction.

5 — Leading: Our approach to AI is strategic, evidence-informed, and evaluated against equity and learning outcomes. The board exercises active scrutiny. We contribute to sector leadership beyond our own school.

1 — Unaware: We have no AI-specific policy, no named oversight responsibility, and have not considered whether our privacy obligations extend to AI tools. The board has no visibility of AI-related risk. NOTE: This is a non-compliance state.

2 — Emerging: We are aware that AI-specific governance is needed. There may be informal expectations but nothing is documented or consistently applied. A privacy officer is named but has not considered AI as part of their role. NOTE: This is a non-compliance state.

3 — Developing: We have a documented AI use policy covering purpose, scope, principles, and a review date. The NZQA authenticity policy explicitly addresses AI. A named person holds oversight responsibility. The board has adopted a governance position. This level meets the compliance floor.

4 — Embedding: Policy is comprehensive and applied consistently across all AI use contexts. The board receives regular reporting on AI use, risk, and incidents. A decision-making process for new AI tool adoption is embedded, including privacy and security checks.

5 — Leading: Governance is proactive and subject to regular self-review. Policy is kept current as guidance evolves. Community and whānau are transparently informed. Te Tiriti obligations — including Māori data sovereignty — are explicitly addressed in governance.

1 — Unaware: AI tools are being used for administrative and communication tasks without any school-level awareness or governance. We do not know what is being used or whether personal information is being entered into these tools.

2 — Emerging: Some staff are using AI tools for admin tasks and leadership is aware. There is a general expectation to be careful, particularly about student data, but nothing is documented or enforced.

3 — Developing: We have documented guidance for staff on AI use in administrative and communication contexts. This covers what information must not be entered into AI tools, expectations for checking outputs, and the distinction between AI assistance in drafting and AI informing decisions about individuals.

4 — Embedding: Organisational AI use is comprehensively governed. All significant admin AI use cases are documented and governed by risk level. AI-generated content in official external communications is reviewed for accuracy and cultural appropriateness before publication.

5 — Leading: We have a mature, documented approach to all organisational AI use that is regularly reviewed. Any AI use that might influence a decision about a student, staff member, or family is governed by explicit protocols with a named human decision-maker and documented rationale.

1 — Unaware: We have not considered whether AI use at our school is equitable across student groups. We are unaware that AI tools are documented to perform weakly on te reo Māori, Mātauranga Māori, and Pacific languages. No consideration has been given to Māori Data Sovereignty obligations or wellbeing risks from AI.

2 — Emerging: We have a general awareness that equity and cultural considerations matter in relation to AI, but we have not translated this into action. Māori Data Sovereignty has been heard of but is not understood as a set of specific obligations.

3 — Developing: We have taken concrete steps to understand and address equity and cultural responsiveness in our AI use. Staff are aware of the documented limitations of AI tools for Māori and Pacific learners. At least one conversation has occurred with Māori community members about AI use and its implications for their tamariki and data.

4 — Embedding: Equity, cultural responsiveness, and wellbeing are active considerations in all significant AI decisions. We review AI tools against equity and cultural criteria before adoption. Māori Data Sovereignty principles are applied to our data governance.

5 — Leading: Our approach to AI equity and cultural responsiveness is genuinely co-designed with Māori community. Decisions about AI tools that affect Māori students or Māori data involve tangata whenua as partners. We treat Mātauranga Māori as taonga that AI tools cannot reproduce or replace.

1 — Unaware: We have no systematic awareness of what AI tools are being used, by whom, or for what purposes. If something went wrong with an AI tool, we have no process for identifying, escalating, or learning from it.

2 — Emerging: Some informal monitoring exists. Policy review is intended but no review date has been set or met. The board receives minimal or no information about AI use.

3 — Developing: We have a basic monitoring and review structure in place. AI-related policies have review dates that are met. An incident escalation pathway exists. The board receives at least annual reporting on AI use.

4 — Embedding: Monitoring is systematic and multi-source. We track AI use across domains and review evidence of impact, not just compliance. The board receives substantive reporting. Community transparency is active.

5 — Leading: We have a mature, evidence-led monitoring and improvement cycle for AI. We can demonstrate that practice has changed based on what monitoring revealed. Community members including Māori and Pacific whānau have a meaningful way to raise concerns.

FutureLearning AI Readiness - Principals Demo

Respondent Role