Exploring parental AI literacy in the context of early childhood learning

This paper explores the growing use of artificial intelligence (AI) in early childhood education (ECE), emphasizing the crucial yet often disregarded role of parents and families. With AI technologies increasingly integrated into educational environments, including households, understanding parental AI literacy is essential for shaping children's learning experiences and developmental outcomes. The review commences by analyzing the current AI applications in ECE, classifying them into six main categories: interactive AI, generative AI, AI prediction, AI literacy, AI-driven personalized learning, and affective AI. Despite the various advantages these advancements offer, they also pose challenges such as potentially worsening digital disparities, concerns regarding data privacy, and ethical dilemmas. The concept of parental mediation in the context of AI technologies is central to the discussion. The review underscores the influence of parents' socioeconomic status, cultural background, age, and educational level on their mediation strategies, which may vary from restrictive to supportive approaches. The effectiveness of parental mediation is intricately linked to parents' AI literacy levels, underscoring the importance of improving parental knowledge and skills in this area. Then this paper presents a novel conceptual framework for parental AI literacy, which encompasses three core dimensions: AI knowledge (evaluation), AI skills (use and guidance), and AI attitudes (understanding). This framework establishes a theoretical foundation for future research by providing a comprehensive model designed explicitly for the family context within early childhood education (ECE). This review synthesizes existing literature and proposes new conceptual models, thereby offering valuable insights to guide policy development, educational practices, and parental training within the rapidly evolving landscape of AI in ECE. It emphasizes the significance of family-centered approaches in leveraging the advantages of AI while effectively mitigating potential risks. Furthermore, it outlines directions for future research and innovation in this critical field.