We explore Transformative Agency (TA) as a theoretical approach for advancing the Protagonist Perspective in Child-Computer Interaction (CCI) research.The Protagonist Perspective is one among several recent contributions that engage with the issue of children's empowerment and agency in relation to digital technology.Yet, the Protagonist Perspective has mostly focused on the overarching agenda, practical initiatives, and examples, with less attention paid to the underlying theoretical grounding.Based on a scoping review, we unfold how TA theoretical insights can support and expand the Protagonist Perspective.Specifically, our findings point to the concept of 'double stimulation', bridging the Protagonist Perspective to learning and building robust outcome measures as potential theoretical contributions of TA.This paper contributes to CCI in two ways: it theoretically advances the role of children as protagonists, and more broadly, it answers the call for theoretical development in CCI.

This paper presents the Radical Constructivist Cooperative Inquiry (RCCI) framework, synthesized from the theoretical foundations of Collaborative Inquiry and Radical Constructivism, to reveal the process of children's learning from and within design activities. The RCCI framework is based on six pillars, as we consider learning in design to be: Child-Centered, Dynamic, Iterative, and Collaborative, with Representations and Outcomes. We provide examples for each pillar from an empirical study we conducted in collaboration with a public library to support children in learning design. This paper responds to calls from the field to increase the theoretical grounding of learning and provides a novel synthesized framework that design educators can use to understand children's learning in design experiences.

We introduce ARMath, a mobile Augmented Reality (AR) system that allows ch ildren to discover mathematical concepts in familiar, ord inary objects and engage with math problems in meaningful contexts. Leveraging advanced computer vision, ARMath recognizes everyday objects, visualizes their mathematical attributes, and turns them into tangible or virtual manipulatives. Using the manipulatives, children can solve problems that situate math operations or concepts in specific everyday contexts. Informed by four participatory design sessions with teachers and children, we developed five ARMath modules to support basic arithmetic and 2D geometry. We also conducted an exploratory evaluation of ARMath with 27 children (ages 5-8) at a local children's museum. Our findings demonstrate how ARMath engages children in math learning, how failures in AI can be used as learning opportunities, and challenges that children face when using ARMath.