This symposium presents scaffolds that help learners critically deconstruct and evaluate AI/ML systems rather than treat them as black boxes. It draws on participatory design and case studies to foster equitable, contextually relevant practices for examining how such systems work and affect users.
This symposium reports on the Institute of Student-AI Teaming (iSAT), which designs AI partners as social collaborators that both facilitate group learning and become objects of inquiry so students can look under the hood of AI systems. Six posters spanning AI design, infrastructure, curriculum, and teacher learning illustrate a multi-state co-design process embedding AI within classroom collaboration to build AI literacy.
This interactive poster symposium connects research on AI's role in creativity with research on control, contexts often placed in opposition. Studies span formal education, after-school and summer programs, community workshops, and performing arts, fostering a cautious but hopeful conversation about AI's ethics and potential.
This symposium argues that CSCL needs more robust theory built through formalized models, causal mechanisms, and generative AI to close the gap between theoretical positions and data. Presenters discuss computational modeling of collaborative scripts, temporal processes, and multimodal, multi-level collaboration as pathways to generalizable theory.