CARE-AI: a responsible framework for integrating artificial intelligence in primary education

Abstract

This paper proposes CARE-AI, a holistic conceptual framework for the responsible integration of artificial intelligence (AI) in primary education. The framework addresses a key gap in existing AI in Education (AIED) literature by integrating pedagogical alignment, adaptive learning, ethical governance and teacher and learner empowerment within a unified socio-technical model. The study aims to provide a human-centred foundation for guiding AI adoption in ways that support meaningful learning, educational equity and responsible classroom implementation. The study adopts a conceptual framework development approach grounded in integrative literature synthesis and theory-informed modelling. Interdisciplinary literature relating to AI in education, adaptive learning, intelligent tutoring systems, ethical AI and human-centred educational technology was synthesised to identify recurring challenges, theoretical gaps and implementation priorities within primary education contexts. Through iterative thematic analysis, four interdependent dimensions emerged to form the CARE-AI framework: Curriculum-Aligned AI, Adaptive Learning Support, Responsible and Ethical AI and Empowering Teachers and Learners. The study demonstrates that responsible AI integration in primary education requires more than technological functionality alone and must instead be supported through interconnected pedagogical, ethical and human-centred structures. CARE-AI conceptualises AI as a teacher-mediated support system that enhances personalised learning, adaptive instruction and data-informed decision-making while maintaining curriculum coherence, transparency, fairness and meaningful human oversight. The framework further highlights the importance of teacher agency, AI literacy and inclusive learning support as central components of sustainable AI-enabled educational environments. CARE-AI is conceptual in nature and has not yet been empirically validated within classroom settings. Its applicability across diverse educational systems, socio-cultural contexts and technological infrastructures may therefore require contextual adaptation. Future research should focus on empirical validation, longitudinal evaluation and cross-cultural implementation studies to assess the framework's effectiveness in supporting learner outcomes, ethical AI governance and teacher practice in real educational environments. The framework provides a structured foundation for educators, school leaders and policymakers seeking to implement AI technologies responsibly within primary education. CARE-AI supports the development of curriculum-aligned AI practices, inclusive adaptive learning strategies, ethical governance procedures and teacher professional development initiatives. It also offers guidance for designing AI-supported learning environments that balance technological innovation with pedagogical integrity and safeguarding requirements. CARE-AI promotes equitable and inclusive AI-supported learning by emphasising transparency, accessibility, learner wellbeing and responsible data use. By foregrounding human-centred educational values and ethical accountability, the framework seeks to mitigate risks associated with algorithmic bias, over-surveillance and reduced teacher autonomy. In doing so, it contributes to broader discussions regarding socially responsible and trustworthy AI adoption in education. This study offers a novel conceptual contribution to the AIED field by synthesising pedagogical, adaptive, ethical and human-centred perspectives into a single integrated framework tailored specifically to primary education. Unlike existing approaches that often address these dimensions independently, CARE-AI conceptualises them as mutually reinforcing components within a broader socio-technical educational ecosystem, thereby providing both a theoretical foundation and an implementation-oriented structure for responsible AI integration in schools.