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This study aims to explore how middle-grade girls from predominantly underrepresented and underserved racially and ethnically minoritized (UUREM) backgrounds developed critical data literacy (CDL) through participation in a week-long residential science, technology, engineering and mathematics (STEM) camp. Given the increasing importance of data science education in a data-driven world, this research examines how informal learning environments can support CDL development among youth from historically marginalized groups. The study draws on qualitative interview data from 11 participants and their group-produced artifacts to investigate how the camp experience supported engagement with data and the development of CDL. Interviews explored participants’ experiences with data collection, organization, analysis and interpretation within the context of real-world, community-relevant topics. The findings demonstrate that participants engaged with data by collecting, organizing and analyzing information relevant to authentic problems. Meaning-making was facilitated through social interactions with peers and mentors, enabling participants to collaboratively interpret and critique data. Girls demonstrated critical examination of data by questioning sources, identifying limitations and considering potential biases. Through presentations, participants applied and communicated their findings, connecting data analyses to community-relevant concerns. Reflection and iteration emerged as participants reconsidered their approaches and interpretations over time. This study contributes to research on informal learning, equity in STEM and data literacy by illustrating how informal STEM programs can intentionally foster CDL among UUREM girls. The findings highlight the role of collaborative meaning-making, critical reflection and community relevance in supporting youth to engage with data as a tool for understanding and addressing societal challenges. 

Recent advances in generative AI (GenAI) allow mathematics teachers to personalize tasks to their students’ out-of-school interests in areas like sports or music. This can allow teachers to better integrate their students’ unique experiences into the mathematics classroom. It also capitalizes on affordances of generative AI to create fine-grained mathematical tasks about specific topics. We examine a group of seven 7th-grade mathematics teachers designing personalized tasks using ChatGPT while engaging in practices of problem-posing. The teachers then gave these tasks to their students, received student feedback, and iterated on their approach. We examine qualitative and quantitative data to explore what teachers learned from the process of personalizing problems with generative AI, how this approach fit into their workflow and curriculum, and what they perceived as the quality and usability of the problems ChatGPT helped them write. We found our qualitative data suggested teachers learned about using generative AI, about problem-posing, and about creating a classroom culture that incorporated students’ interests, showing growth on multiple dimensions of AI-TPACK. The personalization process was seen by teachers as reasonably fitting into their workflow, although issues with time efficiency, the diversity and variability of students’ interests, and the difficulty of learning to use GenAI, were barriers. Finally, we found the generated problems could match students’ interests at a fine-grained level and teachers saw their potential to increase engagement; however, teachers cited issues with the realism and authenticity of the problems. 

This study aimed to explore the implementation of project-based learning (PBL) principles in informal STEM education, focusing on the experiences of Underrepresented Racially Minoritized (UUREM) girls during a week-long residential STEM summer camp. Utilizing a single case study design, the researchers investigated how PBL facilitates engagement and understanding of STEM concepts through podcasting activities. Participants engaged in immersive exploration, integrating their research on podcasting and interviewing women in STEM fields. Data were collected from participants’ podcast recordings, scripts, and collaborative chat logs. The study revealed insights into collaboration, creativity, scriptwriting, recording, and editing among student groups. Overall, the findings underscore the importance of fostering teamwork and enhancing students’ creative and technical capabilities in project-based learning environments. Integrating PBL within informal learning environments is crucial for supporting UUREM girls in STEM education. This approach not only fosters engagement but also promotes an inclusive atmosphere where diverse perspectives are valued. The study advocates for the expansion of PBL initiatives in informal learning contexts, emphasizing the importance of diversity and inclusion. By empowering UUREM girls through targeted STEM programs, educators can cultivate a generation of confident and capable learners prepared to thrive in STEM fields. 

Interest theory states that triggering students’ interest in a learning task can have beneficial effects for their resulting interest in a content domain, as well as their conceptual learning. In context personalization, an instructional approach that leverages interest theory, tasks are modified in educational settings to correspond to interests students hold. In this chapter, we first give a theoretical overview of context personalization, and discuss both positive and negative evidence of its effectiveness. We then shift to talking about the possibilities of generative AI for implementing context personalization, and then give two cases of generative AI being used to personalize instruction in which middle school students and teachers drive the personalization. We close with recommendations for research, design, and practice related to context personalization. 

Generative artificial intelligence has become prevalent in discussions of educational technology, particularly in the context of mathematics education. These AI models can engage in human‐like conversation and generate answers to complex questions in real‐time, with education reports accentuating their potential to make teachers' work more efficient and improve student learning. This paper provides a review of the current literature on generative AI in mathematics education, focusing on four areas: generative AI for mathematics problem‐solving, generative AI for mathematics tutoring and feedback, generative AI to adapt mathematical tasks, and generative AI to assist mathematics teachers in planning. The paper discusses ethical and logistical issues that arise with the application of generative AI in mathematics education, and closes with some observations, recommendations, and future directions. 

This study explores middle-grade girls' understanding of Critical Data Literacy (CDL) after participating in a week-long residential STEM camp. This research examines how informal learning settings can foster CDL among predominantly underrepresented minoritized (URM) girls. Through qualitative interviews, the study reveals how participants engaged with CDL components: formulating questions, gathering relevant data, employing analytical tools, and interpreting findings. The insights aim to inform strategies for enhancing CDL in informal environments, contributing to broader educational goals in data science and supporting the development of critical thinking skills for addressing societal issues. 

The generation of illustrative diagrams to accompany mathematics word problems has the potential to provide adaptive support for students who struggle with mathematics. However, typical approaches to AI-generated illus-trative diagrams have not been successful, with image either being visually ap-pealing or mathematically accurate, and not both. Here we present a novel method for generating illustrative diagrams using generative AI – ControlNets. We demonstrate how ControlNets can be used with open educational resources to create mathematically precise, visually compelling illustrative diagrams to ac-company mathematics story problems. 

Context personalization, or matching instructional tasks to students’ personal interests, has been found to be a beneficial approach within digital learn-ing platforms. Advances in large language models allow for personalized tasks to be generated with increased efficiency. While best practices for using genera-tive AI in K-12 settings accentuate the importance of a human-in-the-loop, little is known about how teachers can use AI to personalize instructional tasks for their students. In the present study, we explore 7th grade teachers using ChatGPT to create personalized versions of mathematics problems, which were subse-quently assigned to 348 students in the ASSISTments platform. We found that teachers’ efficiency for personalization varied widely, and that they often used prompts to swap out contextual or popular culture references in problems or to correct contextual inaccuracies in the problems ChatGPT posed. Further, we found that students may respond more positively to problems that teachers spent more time personalizing with ChatGPT, and that students were sometimes at-tuned to different personalized elements of problems than teachers. 

This study explores STEM identity among Underrepresented and Underserved Racially and Ethnically Minoritized (UUREM) middle school girls within informal learning settings. Focusing on micro-level interactions, we explored a single-gendered STEM summer camp where UUREM middle school girls comprised 81% of the participants (N=59). Guided by ecological systems theory as a methodological approach to developing well-designed informal STEM activities, we sought to positively shape UUREM middle school girls’ STEM identity. STEM identity is complex, multi-layered, and inseparable from the intersectionality of their racial and gender identities. This approach is particularly salient in affective factors such as self-efficacy, ability-belief, and a sense of belonging during their pivotal middle school years. Critical implications include (a) single-gender spaces, like STEM camps, provide affirming, safe environments for authentic discussion and belonging in STEM, and (b) role models of similar racial and gender backgrounds support positive STEM identity formation for UUREM middle school girls.