Tailoring AI Agents for Early Learning: The Creative Project Approach

Synthesis: Yang, Li, and Lee (2025) offer a conceptual roadmap for tailoring generative AI agents and robotics to early childhood learners, arguing that physical (rather than screen-based) agents are developmentally more appropriate for young children and proposing a "Creative Project Approach" — a five-step pedagogical framework that integrates AI agents into the Project Approach to foster children's creative learning. The paper distinguishes two complementary robotics paradigms rooted in distinct learning theories: coding robots grounded in Papert's constructionism (teaching computational thinking through tangible programming) and generative-AI-powered social robots grounded in Vygotsky's social constructivism (acting as conversational peers or tutors who scaffold cognition and social-emotional development). Teachers remain central as facilitators who scaffold child–robot interaction and preserve child Learner Agency.

Key Findings

1. Physical agents over screens for young children. To mitigate excessive screen time and early screen exposure, Yang, Li, and Lee argue that physical, embodied robots — both coding robots and social robots — are more appropriate than screen-based virtual agents for early childhood education, because they create unique physical- and social-interactive learning experiences.
2. A dual theoretical grounding. The framework is anchored in two learning theories. Constructionism (Papert, LOGO lineage: Bee-Bot, KIBO, Matatalab) underpins coding robots, where children "learn by making" and develop computational thinking — sequencing, loops, conditionals, debugging — through tangible programming. Social constructivism (Vygotsky) underpins generative-AI social robots, which offer Scaffolding within a child's Zone of Proximal Development, acting as a peer or tutor who supports both cognitive and socio-emotional development. Coding robots foreground Problem Solving and STEM; social robots foreground relational interaction and social-emotional growth.
3. The "Creative Project Approach" framework. A five-step model aligned with the Project Approach (Katz & Chard) guides integration of AI agents into early-childhood projects: (1) identify learning needs, (2) facilitate child–robot interaction with teacher guidance, (3) situate AI and robot use in various learning contexts, (4) determine the appropriate level of automation and Creativity, and (5) evaluate learning outcomes. Two illustrative cases show the approach in practice — a story-inspired Matatalab coding project (sequencing and debugging embedded in a picture-book narrative) and a play-based GenAI project where a kindergarten class co-created a birthday song using the Doubao AI voice agent, amplifying Learner Agency and social-emotional growth through collaborative music-making.
4. Roles of generative AI agents. The authors synthesize several agent roles for young children: personalized conversational partners and on-call facilitators that adapt content to each child's pace and emotional state; creative collaborators in co-creating narratives (supporting imagination, language, and decision-making); and social-emotional supporters that simulate social interaction — including robots that scaffold toilet training or support children with autism through safe practice of social skills. Examples span commercial tools (Moxie, BubblePal, Heeyo.ai, Doubao, UBTECH robots).
5. Risks and equity caveats. The paper flags key risks: coding robots' technical fragility and complexity can frustrate young learners and demand teacher training and resources; generative social robots risk hallucination — fabricating content that preoperational children may uncritically accept as truth — and often lack the nuance to calibrate Feedback developmentally, potentially hindering self-regulation and critical thinking. Cost and access concerns widen the Digital Divide and inequity, and data Privacy is a live concern. Teachers need training in both the technological and pedagogical sides, and should infuse cultural relevance so technology aligns with local sociocultural contexts.

Connected Concepts

• Early Childhood Education — the primary domain; developmentally specific AI and robotics use for young children
• Robots in Education — coding robots and generative social robots as the two embodied paradigms
• Project-Based Learning — the Project Approach into which AI agents are integrated
• Generative AI — the engine powering social robots and virtual agents (GPT, Doubao, etc.)
• Agentic AI — generative agents that act as conversational partners and collaborators
• Constructivism — Papert's constructionism grounding coding-robot pedagogy
• Sociocultural Learning — Vygotsky's scaffolding and ZPD grounding generative social robots
• Creativity — creative learning as the central goal of the framework
• Computational Thinking — fostered by coding robots through tangible programming
• Social-Emotional Learning — supported by generative social robots and co-creation activities
• Embodied Learning — why physical agents are preferred over screens for young children
• AI Literacy — teachers and children need critical AI literacy to evaluate tools
• Parents and Families

Connected Articles

• AI-Play: A Framework for Teaching Artificial Intelligence Concepts through Unplugged Activities in Early Childhood Education — an AI-Play framework teaching early-childhood AI concepts through unplugged play
• Play With AI (PL-AI): A Play-Centered, Design-Based Curriculum for AI Literacy in Pre-K and Kindergarten — Play With AI: a play-centered pre-K/kindergarten AI literacy curriculum blending unplugged play, tangible coding, and a social AI robot
• Artificial Intelligence in Toys: Implications for Child Development and Play — AI-enabled toys and child development (commercial playthings children meet at home)
• Fostering Computational Thinking Through Robotics in Kindergarten: A Systematic Review of Learning Strategies, Frameworks, and Assessment — robot-mediated computational thinking in kindergarten
• Would You Let a Humanoid Play Storytelling With Your Child? A Usability Study on LLM-Powered Narrative Human-Robot Interaction — LLM-powered humanoid storytelling with young children
• Pathways to Learning: Exploring High School Students' Learning of AI-Powered Educational Robotics — project-based robotics+AI learning (constructionist lens)
• Bots and Blocks: Presenting a Project-Based Approach for Robotics Education — project-based robotics education in CS
• Knowledge-Based Design Requirements for Generative Social Robots in Higher Education — knowledge-based design for generative social robots (addressing the same hallucination/feedback risks)
• Exploring Factors Influencing Preschool Teachers' Behavioral Intention to Use AI Technologies in Early Childhood Settings — preschool teachers' behavioral intention to use AI

Citation

Yang, W., Li, H., & Lee, J. C.-K. (2025). Tailoring AI agents for early learning: The Creative Project Approach. Computers and Education: Artificial Intelligence, 9, 100473.