The rapid integration of AI into early childhood education presents both transformative opportunities and significant ethical challenges that require immediate attention to ensure the well-being of young learners.

Objective: The main goal of this study was to examine the ethical challenges associated with integrating artificial intelligence (AI) into early childhood education (ECE), focusing on four key domains: data privacy, impacts on child development, algorithmic bias, and regulatory frameworks.

Methods: The researchers conducted a scoping review examining 42 studies published between 2015 and 2024, following the framework proposed by Arksey and O'Malley. The review systematically analyzed literature from major academic databases including Scopus, Web of Science, ERIC, IEEE Xplore, and PubMed, as well as relevant gray literature. Studies were included if they addressed AI applications in ECE (children aged 3-8) and explored ethical considerations related to data privacy, developmental impacts, or algorithmic bias. The included articles represented diverse geographical contexts and methodologies, comprising empirical research, theoretical explorations, and policy evaluations.

Key Findings:

• Data Privacy: Significant gaps exist in safeguarding children's sensitive data, with inadequate protections against breaches, profiling, and misuse. Young children lack the cognitive capacity to understand privacy concepts, making them particularly vulnerable to data exploitation.
• Developmental Impacts: Emotional AI tools like social robots and emotion-recognition technologies offer novel learning opportunities but risk undermining relational learning and fostering overreliance, manipulation, or loss of autonomy. Current AI designs often fail to align with the unique developmental needs of young learners, particularly their need for exploratory play, sensory experiences, and social interactions.
• Algorithmic Bias: AI systems trained on non-representative datasets perpetuate systemic inequities, disproportionately affecting marginalized communities. Cultural contexts, linguistic variations, and socioeconomic differences are often overlooked in algorithm development, creating barriers for equitable learning.
• Regulatory Frameworks: Current regulatory approaches are fragmented and inconsistent across jurisdictions, often lacking provisions tailored to the vulnerabilities of children or mechanisms for global enforcement. There is an urgent need for child-centric frameworks that prioritize transparency, data minimization, and accountability.

Implications: The findings underscore the need for immediate and sustained efforts to ensure that AI systems in ECE foster equitable and ethical learning environments. The study highlights the urgency of establishing global frameworks that prioritize transparency, data minimization, and cultural inclusivity. Engaging educators, parents, and children in participatory governance is essential to align AI design with developmental needs and uphold children's rights. The research contributes to the broader discourse on AI ethics by offering insights specific to early childhood contexts, where children's heightened vulnerabilities require special consideration.

Limitations: The study acknowledges several limitations. First, it included only English-language publications, potentially restricting the breadth of findings despite including global samples from diverse geographic locations. Second, the rapidly evolving nature of AI in educational settings means that peer-reviewed journals may not fully reflect the most recent innovations. Additionally, only a limited number of relevant gray literature sources specific to ECE were identified, potentially missing valuable insights from non-academic sources.

Future Directions: The researchers suggest that future studies should include non-English literature to offer a more comprehensive understanding of how AI is applied across different cultural and linguistic contexts in ECE. Additionally, further research should incorporate more gray literature to capture the latest developments and applications of AI in ECE. The study also emphasizes the need for investigating the long-term impacts of AI governance models, as there is currently insufficient data on their effectiveness in addressing systemic inequities and ensuring equitable access for marginalized groups.

Title and Authors: "Innovating responsibly: ethical considerations for AI in early childhood education" by Ilene R. Berson, Michael J. Berson, and Wenwei Luo.

Published On: March 4, 2025

Published By: AI, Brain and Child (journal)