‘Whose Voice Counts?’ A case to explore Participatory Governance and Inclusive Engagement in AI for Education
ENGAGEMENT & DEMOCRATIC GOVERNANCE IN AI
How to cite this learning scenario
Arantes, J. (2025). Whose Voice Counts? Case Studies in AI Governance for Education. www.AI4education.org. Licensed under a Creative Commons Attribution 4.0 International License.
abstract
This case study highlights the consequences of excluding key stakeholders—students, educators, families, and policymakers—from decisions about AI implementation in schools. It focuses on a fictionalized but research-informed scenario in which a government department partners with a private AI vendor to roll out a predictive learning analytics tool. Without co-design or community input, the tool is deployed across public schools. Issues quickly emerge: student data is misinterpreted, cultural and contextual knowledge is ignored, and teachers feel disempowered. The case asks what it means to “govern with” rather than “govern over,” and calls for participatory frameworks to ensure that AI systems reflect the values and needs of those most affected.
AI in education should not be something that happens to communities—it should be shaped with them. Engagement is not an afterthought; it’s a safeguard for democratic, ethical, and equitable systems.
Whose Voice Counts?
In 2023, the Ministry of Education launched a pilot to integrate AI-powered learning analytics across middle and secondary schools. The technology, developed by a global edtech firm, promised to identify "at-risk" students and optimize learning pathways. However, neither students nor teachers were consulted before implementation, and parents were informed only after the program began.
Educators quickly raised red flags: students were being flagged inaccurately, often due to cultural misunderstandings or attendance data taken out of context. Indigenous and refugee students were disproportionately categorized as "low potential." Teachers reported that the system overrode their professional judgment, and some began altering classroom practices to match the algorithm's recommendations—often at the expense of relational, human-centered pedagogy.
Families voiced concerns about data privacy and consent. Many were unsure how their children's data was being used or stored. A parent-led campaign demanded transparency and involvement in future AI-related decisions. Students, too, pushed back, stating they felt surveilled, misunderstood, and excluded from conversations about the tools shaping their education. The lack of engagement led to a public inquiry, ultimately resulting in policy reform mandating stakeholder representation on all future AI governance committees.
This case underscores the importance of democratic participation in AI governance, especially in education. It shows how failing to include those most affected by technology can erode trust, exacerbate inequity, and undermine educational outcomes. Genuine engagement is critical—not only to ensure ethical use but to promote just and responsive systems.
research topics
research questions
What are the risks of implementing AI in schools without meaningful stakeholder engagement? How can students, educators, and families be more effectively included in AI decision-making processes? What does authentic engagement look like across different cultural and community contexts? How can we ensure that AI tools reflect community values, and not just institutional priorities? What lessons can be drawn from this case to guide participatory policy reform?
What are the risks of implementing AI in schools without meaningful stakeholder engagement? How can students, educators, and families be more effectively included in AI decision-making processes? What does authentic engagement look like across different cultural and community contexts? How can we ensure that AI tools reflect community values, and not just institutional priorities? What lessons can be drawn from this case to guide participatory policy reform?
- Explore the ethical implications of excluding key voices in AI decision-making. Analyze how community engagement can improve the fairness and responsiveness of AI tools. Identify strategies to embed participatory governance into local, national, and global AI education policies. Consider culturally responsive and trauma-informed approaches to AI engagement in diverse learning communities.
data collection
Conduct structured interviews or surveys with school leaders and educators to identify existing engagement strategies around digital technologies.
Facilitate focus groups with students from historically marginalized backgrounds to explore how they can be better supported to participate in AI policy development.