This study examined the structure of two preservice teachers’ understandings of educational software in mathematics using repertory grid techniques. Specifically, the study focused on how teacher educators can enable preservice teachers to discern the features of mathematics software, and develop pedagogical goals that reflect the affordances and constraints of available tools. Results showed a deepening of knowledge and a differentiation of knowledge following experiences of exploration, evaluation, and comparison of different types of software. Results imply that directing preservice teachers’ attention to utilizing the functional characteristics of software for developing plans and for the design of instruction may be fruitful for more effective integration of software in their future teaching. However, participants’ preexisting content knowledge and their pedagogical worldview tempered this effect.

Technological advances in computer vision and machine learning image and audio classification will continue to improve and evolve. Despite their prevalence, teachers feel ill-prepared to use these technologies to support their students’ learning. To address this, in-service middle school teachers participated in professional development, and middle school students participated in summer camp experiences that included the use of Google’s Teachable Machine, an easy-to-use interface for training machine learning classification models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained. Framed within Personal Construct Theory, an analysis of the impact of the professional development on middle school teachers’ perceptions (n = 17) of science lessons and activities is provided. Implications for future practice and future research are described.

In this article, the authors present a framework for guiding elementary preservice teachers in adapting mathematics word problems to better meet English language learners' (ELLs) needs. They analyze preservice teachers' ELL adaptations implemented in a one-on-one setting. Through qualitative methods, four themes regarding implemented adaptations are identified: language adaptations, mathematical adaptations, tool/visual adaptations, and structural adaptations. The authors conclude that the framework was successful in helping preservice teachers learn about adapting curriculum by interacting with ELLs. Implications for teacher education are discussed.

Artificial intelligence is impacting society on a very large scale and should be included in K-12 educational content in some capacity to provide meaningful STEM experiences. Computer vision (a field of research that heavily leverages artificial intelligence) was emphasized in professional development for in-service teachers. The teachers received two to three weeks of training across two states (Arizona and Georgia) that emphasized image processing, computer vision, and machine learning using visual media. Personal Construct Theory (Kelly, 1955) was used to map changes in thinking using hierarchical cluster analysis. The research question was: How did in-service teachers’ thinking regarding artificial intelligence change after partaking in remote professional development emphasizing computer vision? Dendrograms and descriptive statistics showed changes in thinking for in-service teachers in relation to artificial intelligence. There were four clusters in both the pre- and post-professional development dendrograms, but constructs shifted within clusters. Implications for practice and research are discussed.