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XR Design Framework for Early Childhood Education

Supriya Khadka, Sanchari Das

TL;DR

This paper introduces the Augmented Human Development (AHD) framework to model how XR technologies interact with young children’s development, defined by $AHD(t) = f(C(t), S(t), E(t), D(t))$, where cognitive load, sensory stimulation, environmental context, and developmental profile jointly influence outcomes. It grounds the framework with a Systematization of Knowledge (SoK) of 111 studies (ages 3–8) across eight databases, evaluating seven dimensions (Pedagogy, Privacy, Data Security, Medical/Health, Technical, Disability Access, Low-Resource Access) and computing Scholarly Attention and a Real-World Risk score. Findings reveal a pronounced risk–attention gap: high real-world risks in data security, privacy, and accessibility receive relatively little scholarly attention, while AR dominates deployments and many reported learning gains are short-lived novelty effects. The framework offers a diagnostic, child-centered XR-by-design approach to guide designers and educators toward aligning $C(t)$, $S(t)$, $E(t)$, and $D(t)$ to enhance safety, equity, and learning in early childhood contexts.

Abstract

Extended Reality in early childhood education presents high-risk challenges due to children's rapid developmental changes. While augmented and virtual reality offer immersive pedagogical benefits, they often impose excessive cognitive load or sensory conflict. We introduce the Augmented Human Development (AHD) framework to model these interactions through cognitive, sensory, environmental, and developmental parameters. To ground this framework, we conducted a Systematization of Knowledge (SoK) of 111 peer-reviewed studies involving children aged 3 - 8. Our findings, interpreted through the AHD lens, reveal a critical "risk vs. attention gap," where high-impact safety and security risks remain under-researched compared to short-term pedagogical gains.

XR Design Framework for Early Childhood Education

TL;DR

This paper introduces the Augmented Human Development (AHD) framework to model how XR technologies interact with young children’s development, defined by , where cognitive load, sensory stimulation, environmental context, and developmental profile jointly influence outcomes. It grounds the framework with a Systematization of Knowledge (SoK) of 111 studies (ages 3–8) across eight databases, evaluating seven dimensions (Pedagogy, Privacy, Data Security, Medical/Health, Technical, Disability Access, Low-Resource Access) and computing Scholarly Attention and a Real-World Risk score. Findings reveal a pronounced risk–attention gap: high real-world risks in data security, privacy, and accessibility receive relatively little scholarly attention, while AR dominates deployments and many reported learning gains are short-lived novelty effects. The framework offers a diagnostic, child-centered XR-by-design approach to guide designers and educators toward aligning , , , and to enhance safety, equity, and learning in early childhood contexts.

Abstract

Extended Reality in early childhood education presents high-risk challenges due to children's rapid developmental changes. While augmented and virtual reality offer immersive pedagogical benefits, they often impose excessive cognitive load or sensory conflict. We introduce the Augmented Human Development (AHD) framework to model these interactions through cognitive, sensory, environmental, and developmental parameters. To ground this framework, we conducted a Systematization of Knowledge (SoK) of 111 peer-reviewed studies involving children aged 3 - 8. Our findings, interpreted through the AHD lens, reveal a critical "risk vs. attention gap," where high-impact safety and security risks remain under-researched compared to short-term pedagogical gains.
Paper Structure (7 sections, 1 equation, 2 figures)

This paper contains 7 sections, 1 equation, 2 figures.

Figures (2)

  • Figure 1: Conceptual Definition of the AHD Framework
  • Figure 2: Risk Assessment Matrix Mapping Scholarly Attention Against Calculated Real-World Risk