Stakeholder Perspectives on AI in Education

This collection separates stakeholder viewpoints from formal government policy. It captures pedagogical, theoretical, philosophical, and practical reflections from classroom educators, computer science leaders, education researchers, and professional teaching associations. Each stakeholder perspective has a dedicated internal summary page that references the original primary source.

Consolidated Viewpoints & Thematic Pillars

By comparing stakeholder arguments across diverse educational environments, we synthesize six core thematic pillars that guide responsible AI adoption:

1. Student Agency & Micro-Scaffolding

  • Core Principle: AI should serve as targeted scaffolding that builds student confidence and encourages intellectual risk-taking, rather than an outsourcing agent that completes entire assignments.
  • Key Practices: Encouraging “micro-scaffolding”—using AI for discrete tasks such as explaining a specific line of code, suggesting grammar improvements, or generating test cases—while prohibiting total generation of whole essays or programs.
  • Stakeholder Focus: CSTA (Dr. Kate Lockwood)

2. Human-Centred Assessment Models

  • Core Principle: Assessment must reframe the role of technology around student learning and educator support, moving away from pure surveillance or AI detection.
  • Key Practices: Balancing three essential assessment questions: Did the student do it? Have they learned? Are we helping? Quality assessment occurs at the intersection of learning and educator support, prioritizing iterative feedback over passive surveillance.
  • Stakeholder Focus: Dave Cormier

3. Pedagogical Integration vs. Prohibition

  • Core Principle: AI is a permanent fixture in modern society; educational systems must proactively integrate AI into teaching and learning rather than treating it as a forbidden topic.
  • Key Practices: Teaching AI-integrated programming and writing skills directly in class; adapting curriculum dynamically through educator collaboration rather than relying on static bans.
  • Stakeholder Focus: CSTA (Dr. Kate Lockwood), CSTA (Lisette Antigua), The Barker Institute / University of Sydney

4. Critical Analysis & “The Analytic Eye”

  • Core Principle: Students must cultivate critical evaluation skills to verify, audit, and evaluate the logical quality of all AI outputs.
  • Key Practices: Teaching “hallucination literacy,” verifying generated sources, analyzing code logic failures, assessing why an AI produced a specific response, and applying the CARE Framework (Check data, Ask impact, Review & revise, Evaluate & explain).
  • Stakeholder Focus: CSTA (Dr. Kate Lockwood), CSTA (Lisette Antigua)

5. Institutional Readiness & Governance

  • Core Principle: Responsible adoption requires system-level foundations: data governance, cybersecurity, flexible guidelines, and protection of teacher-student relationships.
  • Key Practices: Establishing clear data security rules before scaling tools; utilizing flexible guidelines rather than rigid rules; protecting teacher time so educators can focus on mentoring, empathy, and personal connection.
  • Stakeholder Focus: Globe Content Studio / Dell Technologies

6. Developmentally Responsive & Evidence-Grounded Integration

  • Core Principle: AI integration must match learner age, prior knowledge, and self-regulatory capability while preserving cognitive friction.
  • Key Practices: Differentiating AI access by developmental stage; ensuring younger or less experienced learners receive structured guidance; preserving essential cognitive tasks (retrieval, reflection, revision).
  • Stakeholder Focus: The Barker Institute / University of Sydney

7. Critical AI Literacy Through Visible Thinking

  • Core Principle: Critical AI literacy means understanding AI’s operations, knowledge claims, beneficiaries, and appropriate refusal—not merely operating a chatbot.
  • Key Practices: Replace detector-led suspicion with process evidence; ask what students did and kept rather than why they used an approved tool; preserve non-use as a legitimate choice; and gather important evidence under shared classroom conditions.
  • Stakeholder Focus: George O’Toole / IGNITE English Resources

8. Purpose-Driven Implementation & Local Evidence

  • Core Principle: AI outcomes depend on the routines, people, culture, and evidence practices surrounding the tool; schools should generate local evidence and track both benefits and displacement.
  • Key Practices: Start with an educational purpose; invest in training, leadership, data governance, and family communication; preserve teacher-pupil relationships; and use iterative action research to evaluate local effects.
  • Stakeholder Focus: Shape the Future Leaders Coalition / Educate Ventures Research

Collected Stakeholder Summary Pages

Each stakeholder perspective has a dedicated internal summary page containing full details, classroom examples, and links to primary sources: