Schools are beginning to rethink how they handle student data in the age of artificial intelligence, moving away from top-down decisions toward genuine participation from students, parents, and teachers. A new initiative at King's College London is working directly with UK schools to explore what meaningful data governance looks like in practice, focusing on how educational institutions can involve their communities in shaping decisions about technology and personal information.

Why Are Schools Struggling With Data Governance?

Education generates enormous amounts of personal and institutional data, yet students, teachers, and communities often have limited insight into how that information is collected, stored, or shared. This information gap creates a fundamental problem: the people most affected by data decisions have little say in making them. The challenge becomes even more complex when artificial intelligence (AI) enters the classroom, as algorithmic systems can make decisions about student learning pathways, special education needs, and academic performance based on data that students and families may not fully understand.

The Data Empowerment Clinic at King's College London is tackling this problem head-on by partnering with schools that are part of the Shape the Future Leaders Coalition, a network bringing together school leaders, researchers, and innovators to co-create ethical and inclusive AI for education.

What Are Schools Actually Trying to Accomplish?

The coalition is pursuing seven key research areas to reshape how schools approach technology and data:

• AI and Digital Pedagogy: Exploring how artificial intelligence can enhance teaching methods while maintaining transparency about how algorithms influence learning.
• Leveraging Data and Generating Insights: Finding ways to use student data productively without compromising privacy or creating surveillance-like environments.
• Business Operations and AI Systems: Examining how schools can implement AI in administrative functions while protecting sensitive information.
• Addressing Inequity and the Digital Divide: Ensuring that AI implementation doesn't widen gaps between students with different access to technology and resources.
• Implementing Innovative AI: Testing new approaches to educational technology that prioritize student and community input.
• Special Educational Needs and Personalized Learning: Developing AI systems that support students with disabilities and diverse learning needs without reducing them to data points.

The clinic's student team is working directly with two UK schools to explore what genuine participation looks like in practice, examining how schools can meaningfully involve students, parents, and staff in shaping decisions about their data.

How to Build Community-Centered Data Governance in Schools

• Create Transparent Decision-Making Processes: Schools should establish clear mechanisms for explaining how student data is collected, used, and shared, making this information accessible to students, parents, and teachers rather than keeping it hidden in policy documents.
• Involve Multiple Stakeholders in Planning: Rather than having administrators alone decide which AI tools to adopt, schools should include students, parents, teachers, and support staff in conversations about technology choices and their implications.
• Implement Community-Led Oversight Frameworks: Establish structures like data trusts or community review boards that give communities ongoing agency over the digital infrastructure governing how their data is used.
• Balance Innovation With Accountability: Schools should pursue educational technology that respects user rights and fosters trust, rather than adopting tools simply because they are new or promise efficiency gains.
• Design for Accessibility and Inclusion: Ensure that data governance systems and AI tools are transparent and usable for vulnerable populations, including students with special educational needs and families with limited digital literacy.

The interdisciplinary team working on this initiative brings together legal expertise, technical knowledge, and community engagement skills. One team member, an LLM student specializing in intellectual property and information law, noted the importance of this approach: the work reflects a belief that effective data governance must extend beyond purely legal or technical perspectives. Her background includes co-founding a digital identity verification startup and working at international law firms on regulatory compliance, giving her insight into how legal frameworks can serve as tools for empowerment rather than control.

Another team member, focused on the intersection of law, technology, and ethics, brings experience in data-driven research and compliance. This person emphasized that data governance frameworks should be used to advance justice and accountability rather than exclusion and control, drawing from previous work as director of research at an organization studying the effects of technology on vulnerable populations.

A third team member combines legal training with advanced cybersecurity knowledge, including CompTIA Security+ certification and training in cloud security. This perspective is crucial because data protection often fails not due to bad intentions but because organizations struggle to translate legal requirements into actionable steps that actually strengthen security and transparency. The team member noted particular concern about the lack of transparency and community participation in how public-service providers collect and share citizen data, and how automated systems can weaken people's ability to exercise their data rights.

What Does Real Participation Actually Look Like?

The clinic is exploring what genuine participation means in practice, moving beyond token consultation where schools ask for feedback but don't meaningfully change their practices. This requires schools to see students, parents, and teachers not as subjects of data collection but as stakeholders with legitimate authority over decisions affecting them. One team member with expertise in human-computer interaction and a background in psychology emphasized the need to align multiple incentives, both corporate and community, to ensure that companies develop technology that respects user rights and fosters trust.

The work also addresses a critical gap in education technology: the relationship between data and law within a global, collaborative context. A team member pursuing a law and professional practice qualification noted that education generates vast amounts of personal and institutional data, yet students, teachers, and communities often have limited insight into how that data is used or shared. Through this clinic work, the goal is to help close this information asymmetry by connecting legal, ethical, and technical perspectives to support more equitable data practices.

This initiative represents a shift in how institutions think about AI regulation and data governance. Rather than waiting for government mandates or top-down rules, schools are proactively building frameworks that put communities in control. The approach recognizes that before institutions digitize educational processes, they must humanize the protection of student data, because ownership over one's data is ultimately ownership over one's self.