Research and Classroom Practice
This section complements the policy collection with empirical research reports, literature reviews, survey data, and theoretical frameworks on AI in education. Rather than focusing solely on legal mandates, this research examines classroom implementation, pedagogical models, student cognition, assessment reform, and institutional readiness. Each study has a dedicated internal summary page referencing the original primary source.
Research Focus Areas
| Focus Area | Description & Key Questions |
|---|---|
pedagogy |
Instructional methods, constructionist learning models, and learning theories surrounding AI tools. |
case-study |
Classroom implementation, teacher practices, and school-system adoption models. |
tool-evaluation |
Evidence regarding tool design, scaffolding capabilities, accuracy, and cognitive impact. |
equity-in-practice |
Digital divide, access to advanced AI tools, differentiated instruction, and inclusion. |
governance |
Institutional readiness, data security, assessment integrity, and policy implementation. |
Featured Research Studies
Each featured study has a dedicated internal summary page containing full findings, methodology details, and links to primary sources:
1. Actua / Abacus Data: Ready for AI? National Survey Report
- Authors: Actua & Abacus Data (2025)
- Scope: National survey of 502 Canadian K–12 educators, 1,000 youth (aged 12–18), and 1,000 parents/caregivers.
- Core Findings: Reveals a major confidence gap (71% of youth vs. 38% of educators feel youth use AI effectively; 73% vs. 36% for responsible use); 87% of teachers have tried AI but few use it regularly; identifies urgent professional development and AI literacy needs.
2. The Barker Institute & University of Sydney: Young People, Learning, and Generative AI
- Authors: Natasha Arthars, Hongzhi (Veronica) Yang, Matthew Hill, Danny Liu, & Lina Markauskaite (2026)
- Scope: Rapid literature review screening 975 publications and analyzing 271 PreK–12 studies.
- Core Findings: Distinguishes affective gains from durable learning; analyzes task completion vs. skill retention; evaluates 5 ecological conditions and 4 core pedagogical functions.
3. ISTE 24: Generative AI as Mathland and Constructionist Frontier
- Authors: Gary Stager & Sylvia Martinez (2024)
- Scope: Theoretical and practical exploration of Generative AI applied to Papert’s constructionist learning paradigm.
- Core Findings: Positions AI as a “Mathland” microworld for open-ended exploration, emphasizing designs where students construct artifacts rather than receive generated answers.
4. Digital Education Council: AI in Higher Education Global Survey 2026
- Authors: Digital Education Council (DEC, 2026)
- Scope: Global survey encompassing 45,398 respondents across 35 countries.
- Core Findings: Identifies widespread course-level integration gaps (43% no integration), faculty preparation gaps (only 29% feel instructors are prepared), assessment disconnects, and academic integrity concerns.
Policy & Governance Research Syntheses
Our research corpus includes cross-jurisdictional comparative syntheses analyzing policy trends across 97 jurisdictions:
- Policy Landscape Overview: Synthesizes common provincial, state, and national strategies across privacy, literacy, and equity.
- Policy Traits Analysis: Categorizes legislative mechanisms including mandatory curriculum credit, usage guardrails, teacher training programs, and research taskforces.
- Governance Patterns: Compares centralized top-down state directives against local-control models delegated to district school boards.
- Implementation Models: Examines practical guidelines for human-in-the-loop oversight, data privacy protections, and classroom deployment.