Responsible AI in Practice: What Governance Looks Like Beyond the Policy

For most higher education institutions, responsible AI adoption began with policy. In the past couple of years, many ethics statements have been drafted, along with AI Principle documents, and conversations among leadership and faculty to decide on what acceptable use of AI entails, and what guardrails to set in place to maintain safety and academic integrity. While these steps are essential to laying down a foundation for AI use across institutions, AI usage has moved from experimentation, and into daily academic and operational workflows, revealing that policy does not equal governance. In 2026 and beyond, how should higher education think about implementing and governing AI tools? Read on for the shift of AI experimentation to adoption, and our suggestions for AI governance beyond policy.

How are students using AI in higher education?

I 2026, the speed of AI adoption across higher education has outpaced formal institutional controls. Recent surveys illustrate the gap between usage and governance:

• In the UK, over 90% of students report using generative AI in some form, according to the Higher Education Policy Institute (HEPI).
  
• In the US, EDUCAUSE consistently finds that governance, trust, and staff confidence, not technology, are the biggest blockers to sustainable AI adoption.
  
• OECD policy briefs increasingly stress that AI risk emerges not from intent, but from unmanaged deployment at scale.
  

Institutions now face a familiar problem: AI is already embedded in teaching and learning, but governance often exists only on paper. In simple terms, this This creates risk; not just regulatory risk, but operational and reputational risk.

How to move beyond AI policy to AI governance in higher education

Across Europe and North America, leadership conversations are changing. In recent years, questions about AI focused on restrictions, academic integrity concerns, and whether AI should be definitively banned. However, today's questions are more practical - where should AI operate, and where should it not? Who owns accuracy, escalation, and oversight? And, how can institutions ensure AI behaves and supports users according to each context?

This shift mirrors regulatory thinking across the European Union and the United States. The EU AI Act has introduced a risk-based framework that emphasizes human oversight, transparency, contextual safeguards, and continuous AI monitoring. Similarly, the U.S. AI Bill of Rights highlights the need for notice and explanation, human alternatives, and maintaining accountability in automated systems. Across both documents, the message remains consistent: responsible AI is operational, and requires ownership, guardrails, and collaboration.‍

What is responsible AI governance?

Institutions moving beyond policy are converging on a common understanding of governance: beyond a single committee or approval step, it is a system of controls embedded into everyday use. In practice, AI governance includes several core components: human-in-the-loop design, contextual guardrails, clear ownership of knowledge, and continuous review. Together, these components can ensure holistic, sustainable AI governance that maintains safety, academic integrity and support at the forefront.

1. Human-in-the-Loop by design

Responsible AI does not remove humans from decision-making, it formalizes their role. In practical terms, this means clear approval points for high-stakes outputs, instructor review of AI-generated feedback, and setting clear escalation paths when AI confidence is low.

2. Contextual Guardrails

Through our discussions with partners, we have discovered that AI governance stalls when the same rules are applied everywhere. Core activities such as student support, academic tutoring, and assessment, each carry different risks and opportunities. Institutions implementing AI responsibly define context-specific behavior, such as: AI governance fails when the same rules are applied everywhere.

Student support, academic tutoring, and assessment each carry different risks. Institutions implementing AI responsibly define context-specific behavior, such as:

• What sources AI can reference
• How personalized responses should be
• When AI should defer to human support

This prevents overreach while preserving usefulness.

3. Clear ownership of knowledge and content

One of the most overlooked governance questions is simple: who owns the answers? Institutions governing AI solutions assign responsibility for content accuracy, knowledge updates, policy alignment and escalation handling. By empowering faculty and staff, institutions can ensure long-standing governance models, as has been the case with other academic policies, materials and resources - only now, extended to AI.

4. Continuous review

In the world of AI, systems are constantly evolving, at the same rate that content changes and student needs' shift. Therefore, governance must assume a changing landscape where students and faculty might need different materials and knowledge access at different times. To ensure the AI solution is implemented in an efficient, impactful way, institutions must monitor the following trends:

• Usage patterns
  
• Escalation rates
• Knowledge gaps
• Feedback from students and staff

By doing so, governance becomes an ongoing process that can help inform better support, knowledge management and content updates more proactively, improving student outcomes and the overall student experience.

Governance in action: From theory to deployment

This operational approach is already visible in institutions moving from pilots to production. In collaborations such as LearnWise’s work with Coleg y Cymoedd, governance was embedded into deployment from the start:

• Clear boundaries on how AI could support students
• Transparent communication with staff
• Defined ownership of content and escalation

The result was not slower adoption; it was faster, more confident uptake across teams. When expectations are clearly outlined, staff can trust the systems, allowing students to benefit from consistent, reliable support.

Why responsible AI is becoming a strategic advantage in higher education

Governance is often framed as a constraint. In reality, it is becoming a differentiator. From our discussions with partners and institutions across the sector, organizations that operationalize responsible AI see:

• Higher staff confidence
• Stronger adoption across departments
• Reduced fragmentation of tools
• Lower risk as AI scales
  

Conversely, institutions that rely on policy alone often stall at pilot stage, unable to expand without reintroducing risk. In a climate of rising scrutiny - from regulators, students, and the public - trust is becoming a prerequisite for scale.

From policy to practice: A readiness check for leaders

For institutional leaders, the question is no longer “Do we have an AI policy?” More useful questions include:

• Can we explain how AI behaves in different academic contexts?
• Do staff know when AI should escalate to humans?
• Are governance responsibilities clearly owned?
• Can we scale AI without increasing risk?

These are the questions explored in depth in LearnWise’s AI Readiness Strategic Playbook, which focuses on turning principles into practice.

Implementing responsible AI in higher education

As AI moves from experimentation to expectation, higher education leaders need more than policy - they need governance and proof.

The 2025 State of AI Support in Higher Education Report analyzes nearly 100,000 real AI-powered support conversations across 24 global institutions to show how universities are using AI today, what delivers measurable ROI, and where to focus next. It offers a clear, data-backed roadmap for turning AI into lasting academic and operational impact through secure, LMS-integrated support for every campus stakeholder.

Get the report here.