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Stakeholders

created 2026-08-19 · updated 2026-10-01

Stakeholders — the range of human stakeholders involved in, affected by, and responsible for AI in education, and the umbrella concept for the knowledge base's coverage of who the actors are. AI in education is a multi-stakeholder field: learners who use AI, teachers and faculty who integrate it, administrators who govern it, instructional designers who build learning experiences around it, and policymakers who regulate it. Each audience has distinct needs, competencies, roles, and perspectives, and the knowledge base treats them as the human context in which AI tools are designed, deployed, and evaluated.

Questions to Consider

  • The page argues the same AI system looks different from every vantage point — a tool a student experiences as support may look to a teacher like an integrity risk and to an administrator like a governance decision. Which role do you most identify with, and what do you think you're prone to miss from the others?
  • Before reading on, try to list everyone in your institution who is touched by an AI-in-education decision — beyond just students and teachers. Who did you forget, and what would each of them care about most?
  • If learners, teachers, administrators, instructional designers, and policymakers each have distinct needs and competencies, who should have the final say over how an AI tool is deployed — and why?
  • A student's 'personalized support' can simultaneously be a teacher's 'integrity risk.' How would you design a conversation or process that gives each stakeholder's concern genuine weight rather than letting the loudest voice win?

Introduction

AI in education is fundamentally about people — the learners and educators whose work it transforms, and the leaders and designers who decide how it is used. Understanding the distinct stakeholders is essential because the same AI system looks very different from different vantage points: a tool a student experiences as personalized support may appear to a teacher as an integrity risk, to an administrator as a procurement and governance decision, and to a designer as a pedagogical choice. The knowledge base organizes coverage of these audiences across several concept pages.

The stakeholder landscape

How stakeholders appear in the research

  • Role-specific competency frameworks. Teacher AI Competency and Technological Pedagogical Content Knowledge (TPACK) define what teachers need to use AI effectively; AI Literacy defines what all audiences (especially students) need.

  • Differential impacts by role. Research examines how AI affects different audiences differently — Student Experience studies student outcomes, Teaching studies pedagogical integration, Administrators studies institutional strategy, and Educational Development studies professional learning.

  • Multi-stakeholder governance. AI Governance and Educational AI Policy research emphasizes aligning national, institutional, and classroom stakeholders — policymakers set expectations, administrators implement, teachers adapt, and students experience the result.

  • Equity across audiences. Equity examines how AI's benefits and harms distribute across learners and institutions, connecting stakeholders to fairness and access.

  • Role differences can be asserted more easily than observed. A SWOT of 167 contributions from 152 higher-education personnel found teachers, researchers and administrators converging on the same strengths and threats, while fully anonymous collection left roles unidentifiable, so the study could not test its plan to contrast them (Humble & Mozelius, 2026).

Identity across audiences

A common thread across these stakeholders is identity — the sense of who one is and is becoming in relation to AI and to the domain. The knowledge base treats identity as distributed across audiences rather than belonging to any single group.

  • Learner identity — the evolving disciplinary, professional, creative, and academic identity of students (Learner Identity). It is distinct from, but causally connected to, Learner Agency: agency is the situated capacity to act, while identity is the durable sense of self that accumulates from agentic acts and is threatened by authorship loss and competence doubt under AI.
  • Teacher identity — the professional self-understanding of educators (Teaching), reshaped by AI as a question of purpose and role rather than skills alone (see GenAI as identity crisis).
  • Designers and leaders — professional identity also shapes how instructional designers, administrators, and policymakers orient to AI, though the knowledge base's explicit identity coverage concentrates on learners and teachers.

Identity is the human anchor of the stakeholder landscape: it is what AI must support (not erode) for each audience, and it is the construct that connects otherwise separate role pages — Learner Identity, Learner Agency, and Teaching in particular. Where agency concerns control and identity concerns self, AI design must preserve both: control over one's learning and a robust, authorial sense of who one is in the domain.

Implications for AI in education

  • Design for the full stakeholder set: effective AI in education must serve learners, support teachers, inform administrators, and align with policy — not just optimize one audience.
  • Build role-specific competencies: teachers, students, designers, and leaders each need tailored AI literacy and support (see AI Literacy, Teacher AI Competency, Educational Development).
  • Align across levels: the knowledge base's governance research shows AI succeeds when institutional leadership, teacher practice, and student experience are aligned rather than fragmented.
  • Consider parents and the broader community: families are stakeholders in AI adoption whose role and concerns deserve explicit attention.

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Metadata

LevelHigher Ed, K 12
Intended audienceInstructors, Learners, Administrators
Foundations of AI in educationAI Literacy, Learning Design, Teaching
SourceHistoryEditChat

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