Research Agenda for AI & Education
A Multidisciplinary Research Agenda for Artificial Intelligence, Education, Learning, and Instruction
Shaping the future of learning with AI, ensuring equity, democracy, and human dignity.
Executive Impact Summary
This article develops a broader research agenda for AI and Education (AI&ED), bringing together Artificial Intelligence in Education (AIED) and AI literacy within an educational ecology framing. Using a collective writing methodology, an expert panel of internationally recognised scholars contributed reflections on challenges, opportunities, and transformations of AI&ED. Thematic analysis identified five main challenges, five areas of opportunity, and four transformational themes. The article proposes an educational ecology research agenda across macro, meso, and micro levels, advocating for a future-oriented, critical, and inter- or multidisciplinary approach that recognises AI as a socio-technical assemblage and sustains educational values such as equity, democracy, and human dignity in postdigital societies.
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Experts involved
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Key Challenges Identified
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Opportunities Identified
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Transformational Themes
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Challenges
Opportunities
Transformations
Research Agenda Flow
5
Main Challenges for AI&ED Research
| Theme | Identified challenges |
|---|---|
| Learning & instructional practices & curriculum |
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| Access & ethical issues |
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| Assessment & evaluation of learning |
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| AI capabilities, research, & resource constraints |
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| Readiness of instructors, leaders, & learners |
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5
Key Areas of Opportunity
| Theme | Identified opportunities |
|---|---|
| Enhanced pedagogies & educational design |
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| Innovation in design & research |
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| Support for learning processes |
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| Development of critical skills |
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| Hybrid knowledge & innovation |
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4
Transformational Themes
| Theme | Transformational ideas |
|---|---|
| AI technologies & the design of education, learning & instruction |
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| Interplay between humans & AI technologies |
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| A lifelong learning perspective on AI&ED |
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| Organisation & conduct of AI&ED research |
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Educational Ecology Research Agenda Flow
Micro Level (Instructors, Learners, Learning Processes)
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Meso Level (Curricula, Institutions, Leadership)
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Macro Level (Policy, Research Ecosystems, Society)
Holistic Educational Ecology Approach
The proposed research agenda emphasizes a holistic educational ecology approach, integrating micro, meso, and macro levels. At the micro level, it focuses on learners' experiences, critical thinking, personalization, and human-AI interplay. The meso level addresses curriculum design, leadership, and institutional support for AI literacy and safe experimentation. The macro level concerns policy, research ecosystems, societal impact, equity, and ethical governance of AI in education.
This multi-layered approach ensures that AI is not an external disruptor but a catalyst for rethinking educational purpose and design, aligning with values like equity, democracy, and human dignity.
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Your AI Research & Implementation Roadmap
A phased approach to integrate AI into your educational and research strategy, from discovery to continuous improvement.
Phase 1: Foundation & Discovery
Conduct a thorough assessment of existing AI infrastructure, data readiness, and organizational goals. Engage stakeholders across all levels (micro, meso, macro) to identify specific challenges and opportunities for AI integration in education, learning, and instruction. This phase includes initial expert panel consultations and literature reviews.
Phase 2: Pilot Design & Ethical Frameworking
Design and implement targeted AI pilot programs based on identified opportunities, focusing on enhanced pedagogies, critical skill development, and hybrid knowledge structures. Develop robust ethical guidelines and responsibility frameworks, ensuring human-centered design principles are paramount. Begin with small-scale, controlled experiments to gather initial empirical data.
Phase 3: Scalable Integration & Multidisciplinary Research
Scale up successful pilot programs, integrating AI solutions across various educational contexts while continuously monitoring impact on learning processes and outcomes. Establish multidisciplinary research collaborations (AI&ED, computer science, social sciences) for large-scale, longitudinal studies. Develop and adapt curricula to foster AI literacy and prepare learners for AI-integrated work-life contexts.
Phase 4: Continuous Evaluation & Iterative Refinement
Implement a continuous evaluation loop for AI systems and educational practices, drawing on empirical data to refine AI technologies and instructional designs. Promote ongoing professional development for instructors and leaders, fostering adaptability and innovation. Ensure AI governance evolves with technological advancements and societal needs, sustaining educational values like equity and democracy.
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