People and Audiences in AI Education

People and audiences in AI education — the range of human stakeholders involved in, affected by, and responsible for AI in education, and the umbrella concept for the wiki'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 wiki treats them as the human context in which AI tools are designed, deployed, and evaluated.

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 wiki organizes coverage of these audiences across several concept pages.

The stakeholder landscape

Learners (students). The primary audience — students in K 12, Higher Ed, and Adult Learning. The wiki covers learners through Student Experience, Student Engagement, Student Misconceptions AI, Student Modeling, Well Being, and Agency. Learners' AI AI Literacy, self-regulation (Self Regulated Learning), and risk of over-reliance are central concerns.
Teachers and faculty. Educators who integrate AI into instruction. Covered by Teacher Role, Teacher AI Competency, Teacher Education, Faculty Development, and TPACK. Teachers face the dual challenge of using AI in their own teaching and teaching students to use it responsibly (see pedagogies and teaching strategies).
Instructional designers and learning technologists. The professionals who design courses, curricula, and learning experiences around AI. Related to Instructional Design (the discipline) and Curriculum Design, though the people/role of instructional designer is not yet a dedicated page — it is grouped here.
Administrators and institutional leaders. Provosts, deans, CIOs, and leaders who set policy, allocate resources, and govern adoption. Covered by Administrator, and connected to Educational Policy AI, Governance, and Regulation.
Policymakers and regulators. Government and institutional bodies that set the legal and regulatory framework. Related to Educational Policy AI, Regulation, and Governance.
Parents and families. Present in the research (e.g., monitoring student AI use, attitudes toward AI) but not yet a dedicated page — grouped here as a stakeholder.

How stakeholders appear in the research

Role-specific competency frameworks. Teacher AI Competency and 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, Teacher Role studies pedagogical integration, Administrator studies institutional strategy, and Faculty Development studies professional learning.
Multi-stakeholder governance. Governance and Educational Policy AI research emphasizes aligning national, institutional, and classroom stakeholders — policymakers set expectations, administrators implement, teachers adapt, and students experience the result.
Equity across audiences. Equity In AI Education examines how AI's benefits and harms distribute across learners and institutions, connecting stakeholders to fairness and access.

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, Faculty Development).
Align across levels: the wiki'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.

Connected Concepts

Connected Articles

GenAI Student Experiences Uk He Survey 2026 — Student experiences of GenAI in UK higher education

AI Uk Higher Education Policy 2026 — AI in UK higher-education policy (students and institutions)
AI Campus Wellbeing Tools — AI-driven tools for campus well-being
AI Acceptance Preservice Science Teachers 2026 — Preservice science teachers' AI acceptance
AI TPACK Mathematics Teacher Education 2026 — AI-TPACK readiness in mathematics teacher education
GenAI Policies Higher Ed Computing — Institutional GenAI policy in computing
Ethical AI Higher Ed Game Theory — Coordination game framework for ethical AI use in higher education
Student Rationalization AI Writing — Student rationalization of AI use in academic writing
AI Changing Teaching Workflows — How AI is changing teaching workflows