'Automation and Abdication' A case to explore Accountability and Ethical Governance in AI for Education

ACCOUNTABILITY & LEADERSHIP IN AI

how to cite this learning scenario

Arantes, J. (2025). Automation and Abdication. Case Studies in AI Governance for Education. www.AI4education.org. Licensed under a Creative Commons Attribution 4.0 International License.
abstract
This case study examines the ethical, regulatory, and leadership failures surrounding the rollout of generative AI technologies in school systems through public-private partnerships. 
Drawing on reports from education unions, policy watchdogs, and digital rights groups, this scenario follows a fictionalized but research-informed investigation into a global edtech firm, Edunome, which piloted AI-driven learning platforms across underserved school districts. Concerns emerged when AI systems made discriminatory decisions, collected student data without consent, and replaced teacher-led pedagogy with opaque algorithms. Despite red flags raised by educators and parents, policymakers continued to endorse the rollout, prioritizing innovation over safety, equity, and oversight. 
This case challenges educators, policymakers, and developers to confront the growing risks of automation in education and to develop strong frameworks for ethical, inclusive, and human-centered AI governance.

True accountability in AI-driven education is not just about performance metrics and dashboards—it’s about protecting student agency, ensuring ethical use of data, and upholding the right to human-led education. When efficiency overshadows equity, we fail future generations.

Automation and Abdication

Edunome, an AI-driven education company, entered into government contracts in 2023 to provide adaptive learning platforms in low-income public schools. Their “Teacher in a Box” solution promised to personalize learning through data analysis, predictive algorithms, and automated content delivery. The platform replaced key teaching functions, including assessment, feedback, and lesson planning with generative AI tools. As the rollout expanded, educators and families raised concerns: students were being profiled based on biased training data, privacy was breached, and human educators were marginalized. In 2024, a whistleblower report revealed that Edunome’s platform was flagging students for behavioral risk without transparent criteria, disproportionately affecting neurodiverse and racialized learners. Subsequent investigations showed that neither school leaders nor government officials had conducted sufficient ethical reviews or obtained meaningful consent from students and families. The platform's recommendation engine had mislabelled students, and data collected was sold to third-party vendors under vague terms of service. The education department, facing public backlash, commissioned a review—but it was undermined by non-disclosure agreements and lobbying from the company. Despite growing outcry, investments from venture capitalists and global AI coalitions continued to support Edunome, emphasizing scalability over scrutiny. The case illustrates the dangers of outsourcing pedagogical authority to opaque algorithms and the urgent need for robust human oversight, transparent data practices, and culturally responsive approaches to AI integration.

Overview

DISCUSSION QUESTIONS

This case study invites critical engagement with the complex intersection of AI, education, and governance. It challenges educators, policymakers, and technologists to reflect on their responsibilities when introducing emerging technologies into learning environments.. Learning Objectives
Practical Applications:This narrative is valuable for use in training teachers in K-12, TAFE, and Higher Education (HE) settings. It can be integrated into professional learning modules, educational leadership courses, and as a teaching tool in discussions on ethics and governance in education.
  • What governance mechanisms could have prevented the harms described in the Edunome case?
  • How can education systems balance AI innovation with ethical obligations and democratic accountability?
  • What principles should guide AI use in schools to ensure fairness, transparency, and student safety?
  • How does this case reframe our understanding of “teaching” in an age of automation?
  • What role should teachers, unions, and communities play in the oversight of AI technologies in education? How might this case shape future regulations, professional learning, and accreditation standards in AI-enhanced education?
  • Examine the ethical and governance challenges of introducing generative AI tools into public education.
  • Assess the impact of data-driven models on student equity, safety, and learning autonomy.
  • Analyze how lack of transparency and stakeholder consultation can erode trust in education systems.
  • Identify strategies to support inclusive, human-centered AI implementation in schools and teacher education programs.

supplementary materials

To extend this case in your professional context, consider integrating the following prompts and resources into your school or Initial Teacher Education (ITE) program:
UNESCO's Guidance on AI in Education Human Rights Watch: AI Surveillance in Schools eSafety Commission: AI and Children’s Rights
AI Governance for Education: case studies
CONTACT
janine.arantes@vu.edu.au
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