Ethical AI decision-making in early childhood education: A conceptual framework for children's rights,well-being and educator accountability

Introduction

Generative artificial intelligence (AI) is increasingly entering early childhood education (ECE) through tools that support planning, communication, pedagogical documentation and decision-making. While these technologies may offer new possibilities for efficiency, accessibility and professional support, they also introduce significant ethical concerns. In early childhood contexts, these concerns are especially urgent because young children are developmentally vulnerable, have limited capacity to provide informed consent and rely on educators to mediate their encounters with digital technologies (Berson et al., 2025; Kurian, 2025). As a result, the integration of generative AI into ECE cannot be approached as a neutral or purely technical matter; it requires careful professional judgment, ethical responsibility and attention to children's rights and well-being.

Current approaches to AI in education often emphasize broad principles such as transparency, accountability, fairness and human oversight (Adams et al., 2023; Holmes et al., 2022). While these principles are important, they remain insufficient on their own for guiding everyday decision-making in early childhood settings, where relational care, children's rights, well-being and pedagogical responsiveness are central. Educators are increasingly expected to navigate AI use without clear, context-specific guidance, raising concerns about privacy, bias, over-reliance, misinformation and the possible displacement of human-centred pedagogical practices.

This article argues that ECE offers a distinctive orientation to AI ethics by foregrounding relationality, care, children's rights, pedagogical judgment and the non-substitutability of human relationships (Dahlberg et al., 2013; Moss, 2019; Rinaldi, 2006). Many AI ethics frameworks in education emphasize principles such as transparency, fairness, accountability and human oversight (Adams et al., 2023; Holmes et al., 2022). These principles remain important, but ECE requires additional considerations: How are children represented through data? How might technologies reshape educators’ ways of noticing, interpreting and responding to children? What happens when pedagogical documentation, communication or planning becomes mediated by commercial AI platforms (Lupton and Williamson, 2017; van Dijck et al., 2018; Williamson, 2017)? In this sense, ECE does not simply apply existing AI ethics principles to younger children, it reorients AI ethics toward the relationships, responsibilities and interpretive practices that shape children's everyday experiences in early learning environments.

This article asks how early childhood educators might use generative AI ethically while protecting children's rights, well-being and relational experiences. It argues that ethical AI use in ECE requires more than broad principles or individual caution, it requires a structured decision-making approach that can support educators in making informed, context-sensitive choices. In response, the article proposes a three-pronged framework grounded in three interconnected commitments: protecting children's rights, safeguarding well-being and relational pedagogy and strengthening educator AI literacy and accountability. The framework contributes by translating broader AI ethics principles into early-childhood-specific guidance that foregrounds care, responsibility, children's agency, institutional accountability and the ethical limits of automation in early learning environments.

The need for context-specific ethical AI guidance in ECE

The rapid integration of generative AI into ECE has outpaced the development of clear, context-specific ethical guidance, creating significant challenges for educators. Existing AI ethics frameworks often emphasize principles such as transparency, accountability, fairness and human oversight (Adams et al., 2023; Holmes et al., 2022). Although these principles are necessary, they can remain abstract and difficult to translate into everyday pedagogical decisions. As Holmes et al. (2022) suggest, there is an important distinction between intending to act ethically and doing so in practice; ethical action in AI-mediated educational environments requires structured guidance that account for the complexities of teaching, learning and care.

These challenges are particularly pronounced in ECE, where the ethical stakes of AI use are heightened. Young children cannot meaningfully consent to data collection, have limited capacity to critically interpret AI-generated content and depend on adults to mediate their interactions with technology (Berson et al., 2025; Kurian, 2025). Generative AI, therefore, raises serious concerns related to privacy, autonomy and well-being, particularly when children's rights are not adequately protected in educational contexts (Holmes, 2025). Tools that collect or process data may expose sensitive and private information about children, while conversational systems may simulate interactions in ways that risk displacing or diminishing human relationships. As Berson et al. (2025) and Schembri (2026) emphasize, educators need practical decision supports that help them evaluate AI tools in relation to children's rights, developmental appropriateness and holistic early learning goals.

Although privacy legislation differs across jurisdictions, local policy contexts illustrate how educators’ ethical responsibilities are shaped by legal obligations concerning the collection, use, storage and disclosure of children's personal information. In Alberta, Canada, for example, obligations related to privacy and the protection of personal information intensify questions about whether generative AI tools align with educational and institutional responsibilities (Protection of Privacy Act, 2024).

In the global context, expectations regarding children's data protection, consent, platform governance and institutional accountability vary across jurisdictions. This variation reinforces the need for an adaptable ethical orientation rather than a jurisdiction-specific checklist. In ECE settings, the key issue is not only whether a tool complies with a particular privacy law but whether its use protects children's dignity, agency, relationships and rights across diverse cultural and institutional contexts. Many generative AI platforms operate through external systems that may store, process or reuse user data in ways that are not transparent to educators. For early childhood educators responsible for safeguarding children's privacy, these conditions can be difficult to navigate without clear institutional guidance.

Beyond privacy and consent, generative AI introduces broader risks that further justify a multi-dimensional approach. AI systems can reproduce and amplify social biases, raising concerns about fairness and equity in educational contexts (Holmes et al., 2022). They may also generate misinformation that requires careful human evaluation. Recent reviews suggest that generative AI in education raises interconnected ethical and regulatory concerns related to privacy, inequality, governance and pedagogical quality (García-López and Trujillo-Liñán, 2025). These concerns are intensified by the growing accessibility of AI tools, which may encourage over-reliance and weaken professional judgment when outputs are accepted uncritically. Generative AI can also reflect and intensify existing social inequalities, making it essential for educators to approach its use with caution and critical awareness (Heaven, 2023).

These risks are not only individual or technical, they are also structural and sociopolitical. Generative AI tools often enter educational settings through commercial platforms that shape how educators plan, document, communicate and account for children's learning. In early childhood contexts, this raises concerns about platformization, surveillance, datafication and the transformation of children's everyday experiences into digital traces (Lupton and Williamson, 2017; van Dijck et al., 2018; Williamson, 2017). These concerns are addressed in greater detail in the following section and show why ethical AI guidance in ECE cannot focus only on individual educators’ choices or technical compliance.

The labour implications for educators also require attention (Selwyn, 2016; Williamson, 2017). AI tools may be promoted as time-saving supports, but they can introduce new expectations around documentation, communication, personalization and accountability. A critical ethical framework must therefore ask how institutions, platforms, policies and market pressures shape the conditions under which AI is adopted in ECE.

These concerns are compounded by gaps in educator preparedness. Alfarwan (2025) shows that generative AI use in K–12 education is expanding and highlights the need for stronger frameworks and resources to guide implementation. Yet as AI tools become more widely available, professional learning and institutional support have not kept pace. Educators are often expected to navigate AI integration without sufficient preparation in bias detection, data ethics or the critical evaluation of AI outputs (Annapureddy et al., 2025; Nabi et al., 2025). Effective technology integration requires more than technical skill, it depends on pedagogical judgment, ethical awareness and sustained professional learning opportunities (Nabi et al., 2025). AI literacy must therefore be supported systemically through coordinated guidance and broader educational strategy rather than left to individual experimentation alone (Estaiteyeh, 2025).

These ethical stakes, privacy obligations, systemic risks, structural concerns and gaps in educator preparedness demonstrate that a simple checklist or set of abstract principles is insufficient. A multi-pronged orientation can support informed, context-responsive decisions by connecting children's rights, well-being, relational pedagogy, AI literacy and institutional accountability. It also keeps attention on the developmental, ethical, political and pedagogical priorities of ECE.

Relational ethics, children's rights and the early childhood contribution to AI ethics

Ethical decision-making in education is not simply a matter of applying written rules. It is a reflective, context-dependent practice shaped by professional judgment, relational responsibilities and competing values. Mathur and Corley (2014) argue that educators routinely encounter complex ethical dilemmas that cannot be resolved through a single principle or perspective. Instead, ethical decision-making requires attention to multiple orientations, including care, justice, critique, professional responsibility and community. These orientations often come into tension, highlighting that ethical practice involves ongoing reflection rather than definitive solutions.

This understanding of ethics as practice is reinforced by research on academic integrity, which suggests that ethical behaviour develops through experience, habit and environment rather than rules alone (Guerrero-Dib et al., 2020). When unethical practices occur without reflection or consequence, they may become normalized over time. Conversely, when ethical reasoning is embedded in everyday decision-making, individuals are more likely to act responsibly across contexts. Applied to AI use, this suggests that guidelines alone are insufficient; educators must also develop the capacity to critically evaluate appropriate and inappropriate uses of AI.

Within AI in education, scholars have similarly argued that ethical considerations must extend beyond technical concerns, such as data privacy and algorithmic bias, to encompass the broader purposes and values of education itself (Holmes et al., 2022). AI systems embody assumptions about learning, knowledge, assessment and decision-making, and can significantly shape educational experiences. In ECE, these concerns are especially important because decisions about technology are also decisions about children's relationships, agency, safety and well-being. Chen and Li (2025) argue that AI use must be considered in relation to children's development, safety and long-term well-being, rather than only its efficiency or novelty. Ethical engagement with AI, therefore, requires educators to consider not only how systems function but also how they shape pedagogical relationships, learner agency and the broader purposes of education.

ECE deepens this ethical conversation because early childhood pedagogy is not organized only around the delivery of content or the measurement of outcomes, it is grounded in relationships, care, interpretation and responsiveness to children's meaning-making. Foundational work in early childhood scholarship has challenged universalized and technocratic accounts of quality, development and assessment, arguing instead for pedagogical approaches that be situated, democratic, relational and ethically responsive (Dahlberg et al., 2013; Moss, 2019; Rinaldi, 2006). From this perspective, pedagogical judgment cannot be separated from the relationships through which educators come to know children, families, materials, histories and contexts.

Relational pedagogy, therefore, changes the terms of AI ethics. The question is not only whether an AI tool is accurate, efficient, fair or transparent, but how its use might reshape educators’ ways of noticing, listening, interpreting, documenting and responding. In ECE, documentation and planning are not only administrative tasks to be optimized, they are pedagogical and ethical processes that make children's ideas, relationships and worlds visible. When AI mediates these processes, educators must ask what forms of attention are being supported, what forms of interpretation may be flattened and whose meanings may be lost or misrepresented.

A rights-based ECE perspective also requires that children be understood not only as vulnerable subjects in need of protection but also as rights-bearing participants whose agency, dignity and voice matter. This distinction is important for AI ethics because young children's limited capacity for formal consent should not justify treating them as passive data subjects. Instead, educators and institutions must consider how AI-mediated practices represent children, whether children's experiences are interpreted with care and how children's participation and dignity can be protected even when adults make decisions on their behalf.

These perspectives position the use of ethical AI in education as a relational and interpretive practice grounded in professional judgment. Rather than treating ethics as a checklist of rules, this approach emphasizes the need for decision-making supports that help educators navigate uncertainty, weigh competing considerations and make contextually appropriate choices. The early childhood contribution to AI ethics, then, is not simply the addition of a younger age group to existing frameworks. It is a reorientation of AI ethics around relational responsibility, children's rights, pedagogical interpretation and the ethical limits of automation. This relational and rights-based orientation must also be situated within the broader technological and political conditions through which AI enters ECE.

Datafication, platformization and the politics of AI in ECE

A critical approach to generative AI in ECE must attend to the social, political and economic conditions through which AI tools enter educational practice. AI technologies are often embedded within commercial platforms, institutional procurement decisions, data infrastructures and policy expectations that shape educational practice in uneven and often opaque ways (Selwyn, 2016; van Dijck et al., 2018; Williamson, 2017). The ethical questions surrounding AI are therefore not limited to individual educator choice, they also involve systems of power that shape what is made visible, measured, automated and prioritized.

Critical scholarship on educational technology has shown that digital tools can reorganize educational practice around data, efficiency, prediction and accountability (Selwyn, 2016; Williamson, 2017). In early childhood contexts, these pressures are especially significant because children's play, communication, relationships and learning are complex, emergent and difficult to reduce to standardized categories. When AI-supported systems are used to plan, document, assess or communicate children's learning, everyday pedagogical practices may become increasingly datafied. Datafication refers not only to collecting information but to transforming children's experiences into data points, records, outputs or profiles that can be stored, compared, circulated and acted upon (Lupton and Williamson, 2017; Williamson, 2017).

These processes raise concerns about surveillance. Tools that promise efficiency, personalization, safety or improved communication may also expand the monitoring of children, educators and families (Lupton and Williamson, 2017; Williamson, 2017). In ECE, surveillance may be normalized through digital documentation, automated reporting, learning analytics or AI-supported communication. These practices can make children increasingly visible to institutions and platforms while obscuring the processes of data collection, interpretation and reuse for educators and families (Lupton & Williamson, 2017; van Dijck et al., 2018). For young children, who cannot meaningfully consent to such systems, this raises serious questions about autonomy, dignity and children's rights (Holmes et al., 2022; Lupton and Williamson, 2017).

Commercialization and platformization further complicate the ethical use of AI. Platformization refers to the organization of social and educational practices through digital platforms, often owned and governed by private companies (van Dijck et al., 2018). In ECE, AI tools may be adopted because they promise to reduce educators’ workload, improve communication or generate documentation more efficiently. Yet these same platforms may shape pedagogical language, influence what counts as evidence of learning and encourage documentation that is easier to process, categorize or share. The risk is that pedagogical documentation may become aligned with platform logics of speed, visibility, standardization and productivity rather than remaining a relational and interpretive practice.

The labour implications for educators are also important. AI tools are often presented as time-saving supports, but they may create new expectations for more frequent documentation, faster family communication, personalized outputs and polished justifications of pedagogical decisions. Without institutional support, ethical AI use may become another responsibility placed on educators, who are already navigating complex demands. Educator AI literacy, therefore, should not be framed only as an individual competency, it also requires institutional conditions such as time, professional learning, policy guidance and access to approved tools that align with children's rights and relational pedagogy.

These structural concerns deepen the need for a context-specific ethical AI framework in ECE. If AI ethics is understood only as privacy compliance, bias detection or responsible individual use, then broader questions about commercialization, surveillance, platform governance, educator labour and the datafication of childhood remain underexamined. A relational and rights-based approach must ask not only whether an AI tool works but what kinds of childhood, pedagogy, educator professionalism and institutional accountability it helps produce.

A multi-pronged ethical AI decision-making framework for early childhood educators

Building on the need for structured ethical guidance, relational and rights-based theory and critical attention to the politics of AI adoption, this conceptual article proposes a multi-pronged approach for early childhood educators. The framework is grounded in three interconnected commitments: protecting children's rights, safeguarding well-being and relational pedagogy and strengthening educator AI literacy and accountability.

This three-pronged framework positions ethical AI use as an ongoing process of professional judgment rather than a one-time compliance decision. Each prong invites educators to pause before adopting or using AI tools and to consider how their decisions affect children, families, pedagogical relationships and institutional responsibilities. In this way, the framework translates broader AI ethics principles into early-childhood-specific guidance that foregrounds care, rights, accountability and the non-substitutability of human relationships.

The framework's overlapping structure is conceptually important because ethical questions about AI in ECE rarely arise in isolation. A generative AI tool may seem useful for educator planning while also raising concerns about children’s data, family trust, educator labour, and the interpretation of children’s experiences. Similarly, protecting privacy is not only a legal or technical obligation, it is also connected to children's dignity, agency, participation and relationships. The overlap, therefore, reflects the relational nature of ethical decision-making in ECE, where children's rights, well-being and educator accountability are mutually implicated.

Figure 1 illustrates the interconnected structure of the proposed decision-making approach. Children's rights protection and the safeguarding of their well-being along with relational pedagogy and educator AI literacy and accountability are presented as overlapping commitments rather than separate or sequential steps. The surrounding arrows in Figure 1 emphasize that ethical AI decision-making in ECE is interconnected and relational, context-dependent and iterative and reflective. This visual organization reinforces the idea that educators must continually return to questions of children's rights, well-being, relational pedagogy and accountability as they make situated decisions about AI use.

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Figure 1.

Multi-pronged ethical AI decision-making framework for early childhood educators.

Applying the framework: A pedagogical documentation vignette

To demonstrate how the three-pronged framework can be applied in practice, consider a common ECE scenario involving pedagogical documentation. An educator observes a child building a block structure, negotiating roles with peers and explaining how the structure might remain balanced. The educator wants to use a generative AI tool to help draft a learning story for families based on observational notes. At first glance, this may appear low risk because AI is supporting writing rather than interacting directly with children. However, the three-pronged framework makes several ethical considerations visible.

From the perspective of protecting children's rights, the educator must avoid entering a child's name, photograph, location or other identifying information into a public AI platform. The educator must also consider whether the AI-generated description represents the child respectfully and whether family consent and programme policies allow such use. From the perspective of well-being and relational pedagogy, the key question is whether the text preserves the child's meaning-making, relationships and context, or turns a situated moment of play into a generic developmental statement. From the perspective of educator AI literacy and accountability, the educator must verify the output's accuracy, revise any biased or reductive language and remain responsible for the final documentation.

This example shows that the framework is not intended to prohibit all AI-supported documentation or approve it automatically. Rather, it supports educators in slowing down decision-making and asking what pedagogical, ethical and relational work is being delegated to AI. In some cases, an educator may use AI only to organize non-identifying notes or generate possible headings while retaining full responsibility for interpretation and final writing. In other cases, the educator or institution may decide that public AI tools are inappropriate for documentation involving children's experiences. The value of the framework lies in making these decisions explicit, discussable and accountable.

Implications for practice and institutional responsibility

The implementation of an ethical AI decision-making approach in ECE requires coordinated responsibility at both individual and institutional levels. While the proposed framework is designed to support educators’ professional judgment, its usefulness depends on clear policies, ongoing professional learning and access to tools that align with ethical, legal and pedagogical standards.

The framework can be used across multiple levels of ECE practice. At the individual level, it can support educators in considering whether and how to use AI tools for planning, documentation, communication or family engagement. At the institutional level, it can inform centre policies, approved tool lists, family communication protocols, documentation guidelines and data governance procedures. In teacher education and professional development, it can serve as a discussion tool to examine ethical dilemmas and develop rights-based and relational AI literacy.

At the classroom level, implementation begins with embedding reflective decision-making into everyday practice. Educators can apply the three-pronged guide by using guiding questions related to children's rights, well-being, relational pedagogy and ethical responsibility when considering whether and how to use generative AI tools. For example, ‘does this tool protect children's privacy? Does it support my role as an educator without replacing relational interactions? Do I understand its limitations and potential risks?’ Embedding these prompts into planning, documentation and communication practices can help ensure that AI use remain intentional, transparent and ethically grounded.

Professional development is also critical. Educators require sustained opportunities to develop AI literacy, including understanding how generative AI systems operate, recognizing bias and misinformation and navigating privacy considerations regarding personal information (Protection of Privacy Act, 2024). Research suggests that short-term or purely technical training is insufficient; professional learning should instead be ongoing, collaborative and connected to educators’ pedagogical contexts (Nabi et al., 2025). This need for coordinated support also reflects broader calls for system-level AI literacy development rather than leaving educators to navigate these issues individually (Estaiteyeh, 2025).

At the institutional level, implementation requires clear guidance and supportive policy structures. Recent governance research suggests that responsible adoption of generative AI in education depends on institutional accountability, human oversight, stakeholder involvement and transparent policy structures (Alfiras et al., 2026). School boards, childcare centres and educational organizations play a key role in identifying approved tools, establishing expectations for ethical AI use and providing guidance on data governance. Institutions can further support ethical implementation by fostering cultures of reflection in which educators are encouraged to question, discuss and critically evaluate the use of AI rather than adopting tools by default.

Institutional responsibility is especially important because educators cannot be expected to carry the ethical burden of AI adoption alone (Alfiras et al., 2026; Selwyn, 2016). Centres, schools and governing bodies should provide time for professional learning, clear direction about which tools are permitted, guidance for communicating with families and procedures for evaluating risks before AI tools are introduced. These supports are necessary to ensure that educator accountability be matched by institutional accountability.

Through reflective practice, professional learning and institutional support, the proposed approach can be adopted in responsive ways that address the complexities of ECE. Ethical AI use is not a one-time decision or a matter of individual compliance alone, it is an ongoing, supported process embedded within educators’ everyday work and shaped by broader institutional conditions.

Limitations and future directions

This article proposes a guide for ethical AI decision-making in ECE; however, it does not report empirical findings from classroom implementation. The proposed framework is therefore intended as a starting point for reflection, dialogue and further enquiry rather than a finalized or universally applicable solution. Its usefulness will depend on how it is interpreted and adapted within diverse early childhood settings, policy contexts, cultural communities and institutional conditions.

Future research could examine how early childhood educators use the three-pronged framework when making decisions about generative AI in planning, communication, pedagogical documentation and family engagement. Such work could explore the tensions educators experience as they balance efficiency, privacy, relational pedagogy, children's rights and institutional expectations. It could also examine how educators negotiate the pressures of platform adoption, documentation demands, family communication and AI literacy expectations within everyday ECE labour (Selwyn, 2016; Williamson, 2017).

Further inquiry should include the perspectives of families, administrators, policymakers and children's rights advocates to better understand how ethical AI decision-making can be supported as a shared responsibility rather than placed solely on individual educators. Where ethically and developmentally appropriate, research should also consider how young children's perspectives, expressions and experiences might be attended to in discussions of AI-mediated environments (Holmes, 2025). This does not mean making children responsible for decisions about AI systems; rather, it means refusing to position them only as passive subjects of adult protection.

Because generative AI technologies are rapidly changing, ongoing research is also needed to examine how emerging tools reshape questions of data protection, bias, accountability, platform governance, surveillance and relational practice in ECE. Comparative and international research would be particularly valuable, as legal requirements, cultural understandings of childhood, institutional responsibilities and expectations of technology use differ across contexts. Further work could develop practical resources, professional learning models and institution-level policies that support educators in using AI critically and responsibly. In this sense, the proposed framework should be understood as provisional and responsive: a conceptual tool that invites continued revision as technologies, pedagogical practices and ethical concerns evolve.

Conclusion

The integration of generative AI into ECE presents both possibilities and significant ethical challenges. As this article has argued, the use of AI in early learning contexts cannot be treated as neutral or purely technical. It must be considered in relation to children's rights, privacy, well-being, relational experiences, educators’ professional responsibilities and the broader conditions through which AI enters ECE, including platform governance, datafication, commercialization, surveillance and labour expectations (Lupton and Williamson, 2017; Selwyn, 2016; van Dijck et al., 2018; Williamson, 2017). The rapid expansion of AI tools underscores the need for structured approaches that support ethical and informed decision-making.

This article proposed a multi-pronged ethical AI decision-making framework grounded in three interconnected commitments: protecting children's rights, safeguarding well-being and relational pedagogy and strengthening educator AI literacy and accountability. By emphasizing relational care, critical awareness, privacy responsibilities and institutional support, the framework offers a context-responsive guide for educators navigating the use of AI in early childhood settings. Its value lies not in providing universal answers but in making visible the ethical tensions that arise when technologies intersect with children's rights, pedagogical relationships and institutional responsibilities.

Ethical use of AI in ECE is not achieved through compliance alone, it requires reflection, professional judgment and shared responsibility. The task is not simply to manage AI but to ask how educational technologies can be engaged, questioned or refused in ways that protect the relational and ethical commitments at the heart of ECE. As AI becomes increasingly embedded in educational systems, early childhood policy and educator preparation must move beyond tool adoption toward sustained ethical, relational and rights-based approaches.