Social humanoid autonomous robots in K–12 schools: a role–evidence–governance framework for distributed instructional capacity
This article proposes a Role-Evidence-Governance (REG) Framework to examine the use of social humanoid autonomous robots (SHARs) in K-12 schools. Through an integrative synthesis spanning educational robotics, learning theory, and AI governance, the framework evaluates the roles assigned to SHARs, the evidentiary warrants for those functions, and the governance conditions required for educational defensibility. It distinguishes between directly supported functions, adjacent-technology functions, and speculative high-risk applications while identifying nondelegable human responsibilities.
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Who issued it
Who it governs
This article is for researchers, educators, and policymakers examining the integration and governance of educational robotics in K-12 schools.
What the document contains
The document covers the theoretical and socio-technical considerations of deploying social humanoid autonomous robots in K-12 schools, establishing a framework to assess their roles, evidentiary support, and necessary governance mechanisms.
The text does not provide binding statutory regulations, official government policy directives, or enforceable legal penalties.
- The article introduces a Role-Evidence-Governance (REG) Framework for evaluating social humanoid autonomous robots in K-12 classrooms.
- The framework examines assigned robot roles, evidentiary warrants, and governance conditions while factoring in deployment scale.
- Theoretical lenses like distributed cognition and Activity Theory clarify how robots reorganize classroom participation and teacher-student relations.
- The analysis distinguishes directly supported functions from speculative high-risk applications, noting current SHARs are not ready as unrestricted teachers of record.
- It identifies critical governance requirements including privacy, surveillance, bias, over-trust, accountability, labor, and safety.
· Abstract · Introduction and Background · The Role-Evidence-Governance (REG) Framework · Near-Future Limit-Case Vignette · Theoretical Foundations (Distributed Cognition, Activity Theory, ICAP) · Function Distinctions and Governance Requirements · Research Pathways and Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
It identifies bounded research pathways, nondelegable human responsibilities, and governance requirements around privacy, surveillance, bias, over-trust, accountability, labor, and safety.
ABSTRACT
screening score · 2026-10-24
Fairness
2/5Non-discrimination and equitable treatment
It identifies bounded research pathways, nondelegable human responsibilities, and governance requirements around privacy, surveillance, bias, over-trust, accountability, labor, and safety.
ABSTRACT
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
ABSTRACT
screening score · 2026-10-24
Human Oversight
2/5Humans remain in control of consequential decisions
It identifies bounded research pathways, nondelegable human responsibilities, and governance requirements around privacy, surveillance, bias, over-trust, accountability, labor, and safety.
ABSTRACT
screening score · 2026-10-24
Privacy
2/5Protection of personal data and private life
It identifies bounded research pathways, nondelegable human responsibilities, and governance requirements around privacy, surveillance, bias, over-trust, accountability, labor, and safety.
ABSTRACT
screening score · 2026-10-24
Transparency
0/5Openness about how AI systems work and are used
ABSTRACT
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
ABSTRACT
screening score · 2026-10-24
Impact Assessment
1/5Required assessment of an AI system's risks/impacts before or during use
REG examines the role assigned to a SHAR, the evidentiary warrant for that function, and the governance conditions required for educational defensibility
ABSTRACT
screening score · 2026-10-24