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This study examined the ways in which an equity analytics tool -- the SEET system -- supported middle school science teachers' reflections on the experiences of diverse students in their classrooms. The tool provides teachers with "equity visualizations" -- disaggregated classroom data by gender and race/ethnicity -- designed to support teachers to notice and reflect on inequitable patterns in student participation in classroom knowledge-building activities, as well as "whole class visualizations" that enable teachers to look at participation patterns. The visualizations were based on survey data collected from students reflecting on the day's lessons, responding to questions aligned with three theoretical constructs indicative of equitable participation in science classrooms: coherence, relevance, and contribution. The study involved 42 teachers, divided into two cohorts, participating in a two-month professional learning series. Diary studies and semi-structured interviews were used to probe teachers' perceptions of the visualizations' usability, usefulness, and utility for supporting their reflections on student experiences and instructional practices. A key result is that only the "equity visualizations" prompted teacher reflections on diverse student experiences. However, despite the support equity visualizations provided for this core task, the teachers consistently ranked the whole class visualizations as more usable and useful.

Technological tools that provide automated feedback on classroom teaching afford a unique opportunity for educators to engage in self-reflection and work towards improvement goals, in particular to ensure that their instructional environment is equitable and productive for students. More information is needed about how teachers experience automated professional learning tools, including what they perceive as relevant and impactful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers who used an AI-based tool that generates information about their discourse practices from classroom recordings. Findings indicate that teachers perceived the tool to have a high utility value, especially those who elected to use it over two school years. These teachers increased their use of talk moves over time, suggesting that they were making intentional changes due to their review and uptake of the personalized feedback. These results from this study speak to promising directions for developing AI-based professional learning tools that can support teacher learning and instructional improvement, particularly tools with robust perceived utility.

This special issue brings together perspectives from universities and publishers working on new media learning technologies. We begin by describing the way these organizations came to work together, before introducing the articles in this special issue. We then proceed to highlight the important issues that are emerging from their individual and collective efforts within the Group, and most recently from the author-reviewer debate in this issue. We now invite you to build on these discussions with your own contributions.