Implementing the NIST AI Risk management framework in UK schools: A practical guide for education leaders

Before diving into this framework, you may wish to read the previous instalment in this series:

When discussing responsible AI in education, many frameworks feel abstract, theoretical, or built for industries far removed from the realities of school life. The NIST AI Risk Management Framework (RMF), however, stands out because it is practical, structured, and immediately usable for school leadership teams.

Although created by the US National Institute of Standards and Technology, it has quickly become a global reference point for organisations seeking to deploy artificial intelligence safely and responsibly.

A roadmap for UK education settings

For UK education settings, the NIST RMF offers something rare – a roadmap that helps schools actually operationalise responsible AI, not just talk about it. While it is voluntary and not tied to UK law, it provides a clear, actionable way for Trust executives and Headteachers to evaluate AI systems, identify risks, and build sustainable school governance.

This blog explains what the NIST AI RMF is, why it matters for the UK education sector, and how schools and Multi–Academy Trusts (MATs) can use it to guide safe, consistent, and confident AI adoption.

Why NIST matters for UK school leadership

Although NIST is an American standards body, its frameworks influence global practice in much the same way as ISO standards. When universities, edtech vendors, and school suppliers discuss AI safety and risk management, they often use NIST terminology. Increasingly, UK schools are being encouraged to adopt this mindset as AI tools become embedded in assessment, safeguarding, analytics, and classroom workflows.

For UK educators, the value of the NIST AI RMF is found in clarity rather than compliance. The framework breaks down responsible AI usage into four core functions that align with high–impact educational leadership:

1. Govern 2. Map 3. Measure 4. Manage

When implemented correctly, these functions help schools build a culture where artificial intelligence supports learning without undermining safeguarding protocols, equity, or staff confidence.

1. GOVERN – Setting the foundations for safe AI in schools

NIST begins with the governance layer because without clear ownership, AI adoption in education becomes inconsistent and high–risk.

What AI governance looks like in a UK school

Consider the patchwork adoption many leaders now face. A teacher uses generative AI for reports, a department head experiments with AI–supported assessment, and an admin team uses AI summarisation for meeting notes. Each action might seem harmless, but together they create hidden risks – data exposure, algorithmic bias, or non–compliance with KCSIE and UK GDPR. A strong governance structure prevents this fragmented approach.

Practical AI governance steps for schools

• Create an AI working group – Include your SLT, DSL, IT Lead, SENDCO, and Data Protection Lead

• Establish a school–wide AI policy – Define clear rules on approved tools, data handling, and staff expectations

• Set AI procurement thresholds – Ensure any tool using automated decision–making undergoes a formal risk assessment

• Provide practical staff training – Focus on safe, everyday use rather than theoretical concepts

Governance is not about restricting innovation; it is about creating the safeguarding guardrails that protect your students and staff.

Transform your school data strategy with Deesha

If you are looking to move from abstract frameworks to actionable results, explore how Deesha can unify your school data insights and simplify governance for Multi–Academy Trusts. Our platform is designed specifically for school business leaders who need an integrated AI analytics solution that prioritises KCSIE compliance, data integrity, and safeguarding visualisations.

Do not let fragmented data hinder your digital transformation. Discover how our predictive analytics and attendance monitoring tools provide the 'insightful vision' required to lead your school with confidence.

2. MAP – Understanding what an AI system is actually doing

The Map function is the part of the framework that encourages schools to examine the purpose, function, and context of an AI tool before adoption.

In UK education, this is vital because artificial intelligence is often packaged in broad, intangible terms like 'insights', 'recommendations', or 'personalisation'. Mapping forces school leaders to ask critical questions: What does the system really do? What data does it use? And what are the potential risks?

Scenario – Predictive behaviour analytics in schools

Imagine a school considering a behaviour analytics platform – such as Deesha – that identifies patterns to highlight students at risk of disengagement. It uses historical attendance, behaviour logs, and assessment data to empower leaders with earlier intervention.

Mapping would require the school to ask:

• Is this tool making automated predictions or simply highlighting data patterns

• Could historical bias in behaviour logging unintentionally disadvantage certain groups

• How transparent is the reasoning behind a 'risk score' or data insight

• What specific actions will staff take based on these insights

By mapping the system thoroughly, the school avoids the trap of blindly trusting an algorithm that could unintentionally stigmatise students.

Mapping does not require technical expertise

NIST encourages the use of simple, non–technical tools to ensure AI safety and clarity:

• Purpose statements – Clearly defining why the tool is being used

• Data flow diagrams – Visualising how student data moves through the system

• User impact summaries – Assessing how the tool affects teachers and learners

• Stakeholder lists – Identifying who is responsible for the system's oversight

• Harm–identification exercises – Proactively spotting potential safeguarding risks

In other words, mapping is just structured common sense applied to your school digital strategy.

3. MEASURE – Assessing risks, bias, and performance

This is the phase of the framework UK schools tend to skip, often because it feels overly technical. However, the NIST approach is intentionally accessible. Measuring simply means evaluating whether a system is working as expected and checking for potential risks before they impact your school community.

A UK school example – AI–assisted marking tools

Suppose a school adopts an AI marking assistant to reduce teacher workload. Teachers use it for extended writing tasks, students appreciate the instant feedback, and the SLT sees clear productivity benefits.

However, a NIST–aligned measurement process asks:

• Does the tool mark consistently across protected groups and diverse student demographics

• Are specific writing styles or cultural references being misinterpreted by the algorithm

• Is there an over–reliance on the tool that erodes professional teacher judgement

• Does the system maintain its accuracy over time, or is there 'model drift'

Conducting a simple internal AI audit

Measurement is not about catching a tool out; it is about ensuring fairness, safeguarding, and educational integrity. A school can conduct a straightforward audit by:

• Comparative marking – Benchmarking AI–generated marks against expert teacher assessments

• SEND sampling – Specifically reviewing outputs for students with SEND to ensure equity

• Trend analysis – Checking for systematic under– or over–marking in specific subjects

• Staff feedback – Surveying teachers regularly regarding their trust in the system's accuracy

Experience the Deesha difference – AI insights built on measurement and integrity

Measuring the impact of technology is at the heart of everything we do. If you are tired of 'black box' solutions and want to lead your school with absolute data confidence, it is time to explore Deesha. Our platform goes beyond basic visualisations to provide strategic data insights that are rigorously tested for accuracy and fairness.

Whether you are a Trust Leader overseeing a large Multi–Academy Trust or a School Business Manager focused on attendance intervention and attainment gaps, Deesha provides the high–fidelity reports you need to prove your impact. We align our AI analytics engine with the highest standards of UK school governance and KCSIE compliance, ensuring that every 'insightful vision' we deliver is backed by measurable evidence.

4. MANAGE: Taking Action on What You’ve Learned

Managing risks means deciding what to do next: continue, adapt, monitor or stop using the tool.

An Example: ChatGPT-Enabled School Workflows

Consider a scenario where staff begin using generative AI to communicate with parents or draft safeguarding notes. Although productivity improves, the DSL notices inconsistencies in the summaries produced and occasional inaccuracies in phrasing that could be misleading.

Management actions could include:

· Prohibiting AI use for safeguarding notes

· Allowing generative AI for internal drafts only

· Updating policy to require human verification of all outputs

· Providing training on safe prompt use

· Setting up a monitoring schedule

Managing is about practical adjustments, not dramatic interventions.

Why the NIST RMF works for the UK education sector

• Alignment with safeguarding priorities – The framework reinforces harm prevention, bias mitigation, and transparency, mapping directly to KCSIE and UK GDPR principles.

• Optimised for MAT structures – Its governance–first approach is ideal for Multi–Academy Trust digital strategies, providing much–needed consistency across multiple school sites.

• Empowered vendor accountability – NIST provides school leaders with the vocabulary to challenge edtech suppliers with questions regarding bias testing, decision pathways, and risk documentation.

• Scalable for digital maturity – Whether a school is just starting its AI journey or already utilising complex analytics platforms, the framework scales to match their specific needs.

Final thoughts – Confidence through structure

AI adoption in UK schools does not need to be chaotic or high–risk. The NIST AI RMF provides a steady, structured foundation that any educational setting can apply. Having personally implemented the NIST AI RMF in universities across North America, Australia, and Europe, I have seen first–hand how effective it is when executed with intention.

There is a profound sense of empowerment that comes from using cutting–edge technology while knowing you are maintaining the highest standards of safeguarding for your students and staff. You do not need deep technical expertise or the ability to predict the future of artificial intelligence – you simply need a process.

Govern. Map. Measure. Manage.

For more official resources, you can visit the NIST AI Risk Management Framework page.

Lead your school into the future with confidence

Are you ready to turn your school data strategy into a robust, governance–led success story? At Deesha, we build our solutions on the very principles of transparency and risk management discussed in this guide. We help school business leaders and MAT executives gain an 'insightful vision' into their data while ensuring absolute compliance and student safety.

See how our AI–powered insights can transform your school's attendance, attainment, and operational efficiency.

Catch up on the rest of our series, including our deep dives into AI intention setting and data-driven leadership in the UK education sector