Objectives
Our commitment to advancing society-centered AI through rigorous research, critical scholarship, and transformative practice
General Objectives
To investigate, theorize, and advance the principles of society-centered artificial intelligence—positioning AI systems not as neutral technical instruments, but as inherently political artifacts shaped by, and shaping, the social contexts in which they operate.
To examine the philosophical and epistemological implications of AI-mediated knowledge production in educational settings, interrogating how algorithmic systems alter the conditions of learning, knowing, and meaning-making.
To produce rigorous, evidence-based scholarship that challenges techno-solutionist narratives, revealing the ways AI systems encode, amplify, or disrupt power relations across global educational contexts.
To center educational equity as the organizing principle of AI research and deployment, recognizing that technology's value lies not in efficiency gains, but in its capacity to advance human dignity, democratic participation, and collective flourishing.
To cultivate public discourse and policy guidance that reorients AI development toward society-wide benefit, ensuring that AI serves pedagogical integrity, cultural sovereignty, and the common good rather than extraction, surveillance, or consolidation of power.
Specific Objectives
To map the landscape of AI in education through systematic research that identifies leverage points where intervention can catalyze equitable transformation, particularly in under-resourced and marginalized educational contexts.
To develop and disseminate frameworks for assessing AI systems' alignment with society-centered principles—providing educators, policymakers, and communities with tools to evaluate whether AI systems honor or undermine their values.
To investigate the cognitive, social, and ethical implications of AI integration in learning environments, examining questions of agency, authenticity, attention, and the human capacity for sustained intellectual engagement.
To interrogate issues of data sovereignty and epistemic justice, challenging extractive data practices and advocating for models of AI development that honor local knowledge, cultural diversity, and community ownership.
To document and amplify alternative pathways for AI development—decentralized, context-sensitive, and community-governed systems that resist the concentration of intelligence and power in corporate or state monopolies.
To support educators, scholars, and practitioners through accessible research, critical analysis, and practical guidance that empowers them to navigate AI's integration with pedagogical wisdom, ethical clarity, and political awareness.
To ensure the long-term sustainability of society-centered AI scholarship by building networks of collaboration, fostering interdisciplinary dialogue, and contributing to a global movement for education as a site of resistance, reimagination, and renewal.
Guided by Three Core Principles
Society-Centered
AI systems must serve collective flourishing, not corporate extraction
Equity-Driven
Educational justice as the organizing principle of technological design
Evidence-Based
Rigorous scholarship that challenges, not confirms, existing assumptions
International Framework
Aligned with the UNESCO Recommendation on Open Science
The UNESCO Recommendation on Open Science (2021) is the first international standard-setting instrument on open science, adopted by 193 countries. It defines open science as making research accessible, inclusive, equitable, and sustainable — for the benefit of scientists and society as a whole. Society & AI adopts this framework as a working standard for its scholarship and publishing practices.
View Our Open Science Commitments →Quality & Integrity
Editorial review, transparent positionality, and evidence-based scholarship that can be scrutinized and built upon.
Collective Benefit
All published research is freely accessible — no paywall, no login — because knowledge is a global public good.
Equity & Fairness
Open access for all producers and consumers of knowledge, regardless of location, institution, or career stage.
Diversity & Inclusiveness
Embracing diverse knowledge systems, methodologies, languages, and epistemologies beyond the Western research canon.
Power Asymmetry in AI Development
Educational AI is largely designed from a narrow set of power centers, often embedding one worldview into systems used across diverse classrooms. Without community-grounded scholarship, these tools can define what counts as legitimate knowledge and flatten epistemic diversity.
Velocity-Deliberation Gap
AI is reshaping assessment, placement, and instructional decisions faster than schools and communities can deliberate about consequences. When adoption outpaces governance, inequities and weak pedagogical assumptions can harden into long-term institutional practice.
Incentive Misalignment
Commercial systems often optimize for engagement, scale, and efficiency rather than civic agency, ethical judgment, and deep learning. Independent scholarship is needed to realign AI development with educational purposes that matter for democratic flourishing.
Our Research Approach
Our methodology integrates learning sciences, critical pedagogy, systems thinking, and participatory design to produce scholarship that is theoretically rigorous, practically actionable, and philosophically honest — willing to ask whether the questions being posed are the right ones.
Methodological Pluralism
We employ mixed methods—combining qualitative inquiry, quantitative analysis, design-based research, and systems modeling—to capture the multidimensional nature of educational phenomena.
Participatory Frameworks
Community knowledge is epistemically valuable, not merely a source of implementation feedback. Our research includes educators, learners, and community members as co-investigators.
Systems Perspective
Education is a complex adaptive system characterized by feedback loops, emergence, and nonlinearity. We map how AI interventions ripple through educational ecosystems.
Translational Scholarship
We translate findings into frameworks, toolkits, and design principles that practitioners can implement, policymakers can operationalize, and communities can adapt.
Critical Reflexivity
We continuously examine our own positionality, assumptions, and potential complicity in the systems we study. Independent research is not neutral.
Interactive · Click Nodes · Drag/Zoom
Society-Centered AI Science Communication Cycle
This seven-stage model is science communication-first by default: situated evidence is continuously translated for public understanding, educator action, and accountable AI practice.
Select a stage in the cycle
Click any icon to see what that stage indicates in practice.
Our seven-stage iterative research cycle ensures AI development remains grounded in SDG principles, educational needs, ethical obligations, and continuous evidence-based refinement
Conceptual Framework of Society-Centered AI
Our research synthesizes insights across disciplinary boundaries—integrating learning sciences, critical pedagogy, systems thinking, and participatory design—to develop theoretical frameworks that are both intellectually rigorous and practically actionable. The interactive knowledge graph below visualizes the conceptual architecture underpinning our scholarly work.
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Interactive Knowledge Graph: Hover over nodes to explore conceptual
relationships
Our Commitment
We pursue these objectives not as abstract ideals, but as daily practice. Every research project, every publication, every pedagogical intervention asks: Does this serve human flourishing? Does this strengthen democratic capacity? Does this honor the dignity of learners and the wisdom of educators? Our work stands against extraction, surveillance, and the reduction of education to data. It stands for sovereignty, agency, and the belief that intelligence—human and artificial—must serve society, not the reverse.