BackgroundThis paper offers a retrospective analysis of three iterations of a Social Design-Based Experiment (SDBE) focused on refining a learning ecology for undergraduate men of color by centering their needs, experiences, identities, and interests through data-driven inquiry cycles.MethodsWe applied principles of SDBE to guide the re-design of the learning ecology and to analyze the change process retrospectively. The voices of men of color served as a central source of data, informing successive refinements to program structures and practices.FindingsThrough iterative inquiry cycles, we refined the program's design by re-mediating two categories of activity structures: (1) those shaping participants' relationships to institutional structures, and (2) those fostering relationships with themselves and other program participants. These changes aimed to make institutional navigation more relational and responsive while strengthening community-building and identity-centered practices that support belonging and agency.ContributionWe offer a model of how the principles of SDBE can guide the re-design of a learning ecology by centering the voices of men of color. By examining a multi-year, holistic initiative, we illustrate how iterative refinements to various activity structures can lead to the design of learning environments that are responsive to the communities that they serve.
BackgroundWhile children interact with AI applications, they are not often given opportunities to develop a sociotechnical understanding of machine learning systems. Research is needed to understand how children make sense of AI, including algorithmic bias. We draw on sociocultural perspectives to examine how two children, aged 10 and 11, used symbolic and material tools to critique issues of bias and create personally valuable machine learning designs.MethodsThe research design is a descriptive single case study with embedded units. After purposeful sampling of the data, we inductively coded interviews, discussions, and digital artifacts to examine how children understood machine learning concepts and critically engaged with algorithmic bias.FindingsFindings suggest using dialogue alongside model design helps children connect concepts to personal experiences and think critically about AI. Guided discussions deepened their understanding of algorithmic bias and its real-world impacts, fostering visions for more equitable technologies. However, both children developed their understanding in distinct ways.ContributionWe conclude that a sociocultural approach promoting open dialogic spaces, tool appropriation, and the personal creation of machine learning applications fosters children's understanding of sociotechnical machine learning concepts through multiple pathways, ultimately empowering them to emerge as thoughtful, responsible AI technology users and creators.
Background:The learning science has intensified its focus on the sociopolitical dimensions of learning, yet creating environments where learners can productively engage with uncomfortable truths regarding power and privilege remains a significant pedagogical challenge. While the field often draws on critical pedagogues like Paulo Freire, the humanistic, person-centered psychology of Carl Rogers has remained largely unacknowledged despite its potential to support these goals. Methods: This article proposes a conceptual dialogue between current sociopolitical perspectives in the learning sciences and Rogerian person-centered psychology. This paper synthesizes core tenets-unconditional positive regard, empathy, and congruence-and examines the here-and-now encounter group modality as a site for relational and sociopolitical learning. Findings: This article frames the two fields as intellectual cousins with synergistic toolkits. It argues that a person-centered approach provides how to critical pedagogy, offering a methodology to foster the psychological safety and vulnerability required for transformative dialogue. Contribution: This paper contributes a critically updated Rogerian framework that enriches the learning sciences pedagogical toolkit for social design. By integrating relational wisdom with structural analysis, it offers a path toward designing learning environments that are simultaneously safe, brave, and humane.
