Teaching complex AI concepts to children presents significant challenges, as educators often struggle to both grasp nuanced mechanisms and translate them into engaging learning experiences. Drawing on embodied cognition and analogical reasoning theories, this work aims to create embodied metaphors and analogies that simplify the mechanisms and ethical issues of AI recommendation systems for middle-school students. We employed a multi-phase design pipeline grounded in the Design-Based Research (DBR) methodology to integrate feedback from key stakeholders, including domain experts, students, and teachers, for linking abstract AI concepts to concrete and familiar experiences. This process began with collaborative brainstorming with seven AI experts to generate diverse embodied representations. The resulting designs underwent mixed-method evaluations with both middle-school students and teachers, which revealed positive shifts in student attitudes and provided insights for further iteration. The design cycles ultimately uncovered key challenges, such as balancing feature fidelity with cognitive overload, and yielded design implications for developing effective embodied and analogical representations for children's AI literacy.

Peer critique is essential for collaborative learning of science but presents challenges for students.Drawing on distributed and synergistic scaffolding, we designed multiple supports within a CSCL modeling environment, including integrated evidence and peer-feedback mechanisms, to help 6 th -grade students develop peer critique practices.Through analysis of students' comments and engagement patterns during critique activities, we found that students demonstrated progressive uptake of peer critique practices that the scaffolds were designed to promote, evident in more balanced, specific critiques over time.These findings inform the design of distributed scaffolds for supporting peer critique in science classrooms.

The nurturing of learners’ ways of knowing is vital for supporting their intellectual growth and their participation in democratic knowledge societies. This paper traces the development of two interrelated theoretical frameworks that describe the nature of learners’ epistemic thinking and performance and how education can support epistemic growth: the AIR and Apt-AIR frameworks. After briefly reviewing these frameworks, we discuss seven reflections on educational theory development that stem from our experiences working on the frameworks. First, we describe how our frameworks were motivated by the goal of addressing meaningful educational challenges. Subsequently, we explain why and how we infused philosophical insights into our frameworks, and we also discuss the steps we took to increase the coherence of the frameworks with ideas from other educational psychology theories. Next, we reflect on the important role of the design of instruction and learning environments in testing and elaborating the frameworks. Equally important, we describe how our frameworks have been supported by empirical evidence and have provided an organizing structure for understanding epistemic performance exhibited in studies across diverse contexts. Finally, we discuss how the development of the frameworks has been spurred by dialogue within the research community and by the need to address emerging and pressing real-world challenges. To conclude, we highlight several important directions for future research. A common thread running through our work is the commitment to creating robust and dynamic theoretical frameworks that support the growth of learners’ epistemic performance in diverse educational contexts. © The Author(s) 2024.