Cross-stakeholder policy, research, and practice actions for collaborative AI initiatives in education¶

Strategy · All strategies
Evidence · 1 claim (1 mixed) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

Based on the Delphi findings and persona reflections, the article recommends actions assigned to policymakers (PM), researchers (RE), and practitioners (PR), organised under six goals including fostering knowledge exchange, responsible use of multimodal data, inclusive and equitable access, a shared dynamic vision, acceptable AI usage, and lifelong learning. Recommended actions include "Fostering collaborative knowledge construction through equitable and sustainable public-private stakeholder partnerships (PM, RE, PR)", formulating actionable guidelines for equitable access and data protection (PM), and providing ongoing professional learning opportunities in formal, non-formal, and informal settings (PR).

Design Implications¶

Context¶

Requirements¶

• Coordination across policy, practice, and research domains; allocation of resources such as time, budget, and people

Constraints¶

• The article states students and parents, though important stakeholders and target users, were not involved at this stage of the study

Target Learners¶

• Learners and educators in AI-enhanced educational ecosystems

Target Learning Goals¶

• AI-related competences, equitable access to AI agents, and responsible use of AI in teaching and assessment

Affordances¶

• Educational Ecosystem Perspective Ai Integration

Claims¶

• Review identifies a sub-theme on educational providers expanding collaboration with other organisations amid digitalisation [~W]

Related Strategies¶

• Seven research priorities for AI in learning, spanning expanded scenarios, teacher assistance, assessment, responsible AI, equity policy, stakeholder engagement, and ecosystem strengthening
• Use the Recommendation's Policy Action Areas to translate AI ethics values into policy across education, data governance and other spheres
• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• Prioritize construct-to-data mappings, multimodal validation, ethical implementation, and cross-disciplinary collaboration for domain-specific adaptive systems

Examples¶

-

Key Sources¶

• Rwitajit Majumdar, Dirk Ifenthaler, Clara Schumacher, Enda Donlon, Hsiao-Ping Hsu, Jason Zagami, Maaike Heitink, Wolfgang Mueller. (2026). Fostering collaborative futures: Multidisciplinary approaches to AI integration in educational ecosystems. Computers and Education Open 11 (2026) 100405. https://doi.org/10.1016/j.caeo.2026.100405