3.1. The Evolution Toward Society 5.0

Societal development can be understood as a sequence of stages, each defined by the organization of knowledge, labor, and value. Society 1.0 operated as a hunter-gatherer system with oral knowledge transmission. Society 2.0 introduced agrarian communities and spiritual institutions. Society 3.0 emerged with industrialization and standardized schooling, while Society 4.0 brought global connectivity and digital information systems (Figure 1). The proposed super-smart society of Society 5.0 merges intelligent infrastructure with human-centered values [1,2].

The evolution from Society 4.0 to Society 5.0 represents an epistemological transformation that surpasses technological advancement. Knowledge transforms in terms of its origins, verification processes, and methods of interpretation. This development has profound effects on educational systems. The educational system of Society 5.0 needs to transform its knowledge transmission methods into programs that develop learners who can use AI environments while making decisions and practicing ethical reasoning. Society 5.0 differs from other visions because it advocates human-AI harmony without embracing either technological utopianism or automated dystopia. The realization of this vision depends heavily on education, which needs to establish governance and curricular frameworks that emphasize interpretability and pluralism alongside cognitive sovereignty.

3.2. Key Characteristics of Society 5.0

The philosophical foundation of Society 5.0 combines technological optimism with social responsibility. Five core characteristics define this model and guide its application across societal domains [1,2]:

• Human-Centric Innovation: Society 5.0 prioritizes human needs over market efficiency. AI tools are designed for empathetic communication and inclusive hiring, supporting well-being and social equity;

• Cyber-Physical Integration: The fusion of cyberspace with the physical world enables AI-powered systems such as collaborative robots and wearable IoT devices that enhance workplace safety and preserve human oversight;

• Sustainability and Inclusivity: Equitable access to digital infrastructure is a central goal. AI and IoT technologies support marginalized communities in urban agriculture and remote work through accessible, inclusive platforms;

• Data-Driven Decision-Making: Big data analytics guide targeted interventions in education and workforce development, helping institutions respond to future employment challenges; and

• Decentralized Knowledge and Collaboration: Platform-based models and blockchain technologies empower individuals through credential verification, transparent compensation, and ethical, worker-owned ecosystems (Figure 2). Together, these characteristics articulate a vision where intelligent systems reinforce human dignity, autonomy, and shared progress.

3.3. Socio-Technical Systems (STS) Theory

The Socio-Technical Systems (STS) theory establishes a fundamental framework for analyzing how artificial intelligence transforms educational systems (Figure 3). According to STS, technology exists as part of cultural, institutional, and human systems, and develops through its evolution [4]. The educational application of AI extends beyond a mere tool, gradually becoming essential for transforming knowledge structures, teaching practices, and student learning engagement. Orlikowski [3] demonstrated that technology interacts with social systems through a continuous feedback process that transforms both the technology and the social structures.

The introduction of AI in classrooms starts with supporting functions such as assessment automation and content delivery, yet produces profound changes in teaching authority and student learning independence. A comprehensive understanding of these changes requires more than technical knowledge, as it also demands awareness of the cognitive, ethical, and cultural factors that interact with technology. Science and Technology Studies (STS) offer an effective approach to counter technological determinism by supporting human–AI collaborative design processes. Educational technology development should occur through collaborative efforts between humans and AI systems while maintaining focus on local values and specific learning environments. The co-development process is essential for AI systems, as it shapes how learners interpret concepts such as knowledge, evidence, and truth. The STS framework enables ethical and context-sensitive implementation of AI in education.

3.4. The Cognitive-AI Interaction Framework (CAIF)

The Cognitive-AI Interaction Framework (CAIF) is based on STS theory to describe three distinct ways AI interacts with knowledge and learning systems:

Epistemic Anchoring: Educational knowledge verification and institutional trust are supported by AI tools. Examples include blockchain-based credentialing, semantic citation validation, and plagiarism detection systems [6]. The systems ensure content authenticity but need to stop students from depending too heavily on machine verification systems [7].

Cognitive Mediation Systems: Adaptive learning environments, AI tutors, and recommendation engines personalize instructional pathways to support individualized student learning [8]. Although these educational tools enhance student outcomes, they simultaneously minimize intellectual diversity and generate algorithmic dependencies. Educators must ensure interpretive agency through complete transparency, together with human supervision and diverse pedagogical approaches.

Synthetic Knowledge Environments: AI-created materials combined with computer-generated characters and virtual educational simulations create interactive learning scenarios. The educational tools open up new teaching methods, but they make it harder to distinguish between authentic and fabricated content. Students tend to accept artificial representations without question due to the lack of epistemic labeling and critical reflection protocols [9].

The modalities operate as both a diagnostic and design framework, which helps educational professionals and institutions understand the implications of AI on knowledge development and educational ethics. CAIF provides an analytical approach to predict and direct educational evolution in AI-driven societies, though it does not specify a single solution. The analysis of technological form in relation to epistemic function enables researchers to differentiate between AI systems that assist learning from those that distort meaning. These theoretical foundations provide the basis for the following results, which summarize the CAIF framework’s core modalities and their implications for AI-integrated education.

4.1. Global Ethical Anchors for Human-Centric AI in Education

The increasing integration of AI systems into educational structures requires stronger ethical frameworks that respect cultural diversity. Society 5.0 originated as a Japanese philosophical concept rooted in the values of ikigai and wa [1]; however, its global adoption necessitates an ethical framework that extends beyond Japan. The governance of AI in education requires multiple global perspectives that value human dignity together with collective knowledge autonomy. This section provides a brief analysis of five regional ethical systems, from Africa, Oceania, Asia, the Americas, and Europe, to assess their influence on the design and deployment of CAIF modalities.

Ubuntu (Sub-Saharan Africa): Ubuntu represents a key relational philosophy in sub-Saharan Africa, which states, “I am because we are”. This approach emphasizes the importance of interdependence along with community and shared knowledge production responsibilities [10]. Through education, Ubuntu challenges learning personalization that creates individual learning isolation and breaks down collaborative knowledge systems. According to Higgs [11], Ubuntu-based ethics reject individualistic Western approaches by promoting communal values, collective intelligence, and dialogical participation in education. Ubuntu matches the principles of cognitive mediation systems within the CAIF framework by supporting the development of AI tools that promote peer learning and social knowledge scaffolding, and co-engagement activities.

Kaitiakitanga (Oceania-Māori Worldview): In Māori thought, Kaitiakitanga refers to stewardship and guardianship, particularly over cultural and environmental resources [12]. Applied to AI in education, this concept insists that knowledge must be preserved with reverence, especially when it concerns endangered languages, indigenous epistemologies, or oral traditions. Kaitiakitanga emphasizes the importance of epistemic anchoring, ensuring that AI systems do not extract or commodify knowledge, but instead serve as stewards of cultural continuity [13]. AI-driven archival systems, for example, can be designed to preserve community ownership over data and embed indigenous consent protocols into content generation.

Confucian and Buddhist Ethics (East and Southeast Asia): Philosophical systems in East and Southeast Asia emphasize ethical self-cultivation, interdependence, and social harmony. Confucian ethics, particularly the value of li (ritual propriety), prioritize structured moral development and relational responsibility in learning [14]. Buddhist thought adds a contemplative dimension, promoting mindfulness, non-attachment, and moral discernment [15]. These frameworks challenge the techno-solutionism often seen in AI-based education and argue for the preservation of pedagogical intentionality. In CAIF terms, this maps onto cognitive mediation systems, where AI systems must support—not manipulate—learner reflection, autonomy, and moral reasoning.

Buen Vivir (Latin America): Buen Vivir (or Sumak Kawsay), rooted in Andean cosmologies, emphasizes holistic well-being, reciprocity, and harmony with the natural world [16]. It critiques linear, industrial notions of development and advocates for culturally situated and ecologically balanced knowledge systems. When applied to AI in education, Buen Vivir problematizes Synthetic Knowledge Environments that are disconnected from learners’ lived experiences or that marginalize indigenous perspectives. AI-generated simulations and content must be grounded in contextual relevance and moral depth. As Walsh [17] explains, digital technologies in Latin America must be reoriented to serve epistemic justice and community-based validation.

Human Rights Tradition (Europe and Global Institutions): The Human Rights tradition, which emerged from Enlightenment philosophy and post-war global institutions, emphasizes equality and freedom of expression and access to information. The UNESCO Recommendation on the Ethics of Artificial Intelligence [18] demonstrates this tradition by establishing obligations for transparency, non-discrimination, and inclusive access. These principles apply across all CAIF modalities—particularly the principles function to regulate algorithmic bias and protect learner data while ensuring explainability.

To clarify their relevance across the framework, Figure 4 summarizes the core principles of each ethical tradition and how they align with the modalities of the CAIF.

The application of AI systems requires compliance with legal standards as well as epistemically just principles to allow learners to develop their own independent understanding. The ethical anchors operate as a system to develop a multicultural epistemic framework, which leads the governance and development of AI in education. The philosophical framework of CAIF modalities is grounded in these principles, which ensure that AI tools fulfill the ethical, cognitive, and cultural requirements of learners across diverse backgrounds. Educational systems can transition from technological advancement to moral development. The integration of values into policy and pedagogy, and platform design leads to the development of both smart and wise learners.

4.2. Applying the Cognitive-AI Interaction Framework (CAIF) to Educational Systems

The Cognitive-AI Interaction Framework (CAIF) provides a framework to study how artificial intelligence transforms education by changing the verification process of knowledge, and the experience of knowledge and trust in knowledge. This section applies the three CAIF modalities (Epistemic Anchoring, Cognitive Mediation Systems, Synthetic Knowledge Environments) to educational practices, with attention to curriculum, pedagogy, assessment, and governance. While Section 3.4 outlined the foundational principles and theoretical architecture of CAIF, the following section applies the framework to concrete educational processes, including curriculum, pedagogy, and assessment.

5.1. Governance and Policy Implications for Society 5.0 Education

Educational governance must establish new methods to handle the technical aspects and intellectual consequences of AI systems when integrating them into educational infrastructure. The current ethical governance system, which relies on abstract principles and delayed monitoring, no longer meets present needs. A new governance model is required—one that anticipates problems and engages stakeholders through principles of cognitive integrity. AI systems in Society 5.0 educational ecosystems should operate as collaborative meaning-makers instead of independent authorities while maintaining responsibility to human teachers, students, and their communities. CAIF functions as a base model to demonstrate how AI affects knowledge through its three distinct processes: epistemic anchoring, cognitive mediation systems, and synthetic knowledge environments. Ensuring knowledge integrity in AI-driven education requires distinct governance approaches for each modality.

Transparency Protocols and Epistemic Labeling: All educational institutions must establish mandatory transparency standards that apply to all AI systems, which include content generation and assessment, and recommendation systems. These requirements align with international policy directives, such as the European Commission’s guidelines for trustworthy AI in education, which emphasize algorithmic transparency, accountability, and explainability in educational tools [21]. Educational platforms must label AI-generated content while maintaining records of algorithmic decisions that impact learner outcomes and developing standards for epistemic metadata that display authorship information together with source credibility and interpretive scope. Such practices are essential for maintaining epistemic anchoring and preventing the normalization of synthetic content as factually equivalent to human-authored knowledge.

Human-AI Co-Education Policies: Policy implementation must ensure that educators retain the authority to interpret the outputs of AI systems. The following rights belong to educational staff members: (a) the educational system must allow human instructors to reject AI-generated curriculum and assessment recommendations; (b) the system should enable educators to stop automated feedback systems that lead to student misdirection or demotivation; and (c) educators must apply their judgment to place AI-generated insights in a proper context. Cognitive mediation systems need constant monitoring by educators who must learn both AI system operation and output evaluation methods. The implementation of AI systems should prevent educational inequalities from becoming more pronounced. Public institutions and national organizations need to establish policies that provide AI tools to students regardless of their location or financial status or language abilities. AI systems require training data that include diverse collections encompassing multiple epistemological perspectives, as well as various linguistic and cultural knowledge domains. Synthetic knowledge environments will maintain dominant narratives unless deliberate steps are implemented to incorporate diverse epistemic content. The verification process and external auditing function need to be implemented. The establishment of effective AI governance requires verification systems that operate through independent third-party verification structures. Third-party audit mechanisms should assess educational AI systems based on their fairness level as well as their transparency and epistemic reliability. The audits should evaluate system performance data beyond performance metrics by assessing interpretive fidelity and cognitive bias elimination, and learner agency safeguards. Blockchain technology enables the verification of educational credentials, safeguarding student achievements through secure, transferable, and tamper-proof systems.

Policy Literacy and Participatory Design: The governance process needs to become more democratic. Educational systems require the collaboration of stakeholders such as educators, learners, parents, and community leaders in the design, implementation, and assessment of AI tools. Educational institutions should integrate AI policy literacy into training programs for teachers and school leadership programs, and curriculum development to establish AI policy literacy. Co-design approaches enhance the relevance of AI tools while building institutional trust and protecting interpretive autonomy. Society 5.0 governance serves a purpose that goes beyond technical control because it enables the collective development of knowledge systems. Through the CAIF framework, institutions can shift from regulatory responses to design-oriented approaches, which transform AI into an educational partner rather than an authority seeking control.

5.2. Strategic Outputs for Educational Futures

The successful implementation of the Cognitive-AI Interaction Framework (CAIF) in educational institutions demands both practical implementation tools and a clear conceptual understanding from educational professionals and policymakers. Strategic outputs help maintain the principles of epistemic integrity and human-centered equitable learning during AI implementation. This section introduces two key initiatives: a Global Declaration for value alignment and a Policy Briefing Series for local implementation.

5.2.1. Declaration for Human-Centered AI in Education

A worldwide endorsement of a declaration would define essential ethical guidelines for incorporating artificial intelligence in educational settings. The declaration should incorporate ethical principles that resemble the UNESCO Recommendation on the Ethics of Artificial Intelligence [21] and the AI4People guidelines [22]. This declaration should ensure: (a) the protection of cognitive integrity requires maintaining epistemic transparency while allowing learner autonomy to maintain knowledge provenance; (b) the declaration should embrace epistemic diversity through Ubuntu along with Kaitiakitanga and Buen Vivir principles [10,12,16]; (c) the development of inclusive governance models needs to include teachers alongside learners and their communities (Figure 5). The declaration serves as a normative framework that enables educational institutions to reform their curriculum and platforms as well as develop policies aligned with AI integration. Figure 4 contains a draft declaration that institutions can adapt.

5.2.2. AI in Education Policy Briefing Series

A modular Policy Briefing Series (Figure 6) is proposed to assist decision-making at both national ministry and educational leadership levels. These briefs transform CAIF into governance-friendly tools by analyzing major policy domains and using ethical frameworks to provide recommendations. Each brief is designed to assist both pilot programs and national implementations. Proposed briefs include:

The briefs provide adaptable structures for different regions and systems, including templates, checklists, and policy metrics. The transformation of CAIF into an inclusive governance system and educational foresight framework occurs through these briefs.