Christian Dieter Schunn

This qualitative case study explored how four high school English Language Arts teachers engaged with a Community Cultural Wealth (CCW) pedagogical framework originally developed for mathematics instruction. Teachers participated in a workshop series that introduced them to an empirically‐derived CCW pedagogical framework and explored its application to ELA contexts. Through semi‐structured interviews, we analyzed teachers' descriptions of their implementation of specific CCW practices and shifts in their reported pedagogical beliefs. Teachers reported leveraging multiple forms of capital, particularly social, linguistic, familial, and navigational, through practices like peer collaboration, heritage projects, and student‐generated learning tools. Teachers also reported shifts from deficit‐oriented, teacher‐centered instruction toward asset‐based, student‐led pedagogy. Teachers valued the CCW framework for providing concrete practices that legitimized asset‐based approaches. These findings suggest that the CCW pedagogical framework may serve as a practical guide for bridging theory to practice in assets‐based pedagogy across disciplines.

Students' domain‐specific mindsets and their beliefs about their capacity to improve through effort play a crucial role in shaping their experiences and decisions to persist in STEM disciplines. Physics is generally seen as a field requiring innate brilliance, which can reinforce fixed mindsets, particularly after initial setbacks in performance that are common in introductory university courses. In this study, we examine changes in fixed mindsets and potential gender differences in an introductory calculus‐based physics course. Our sample consisted of 508 students with an average age of 18, predominantly White, with men comprising the majority. Based upon survey response distributions, three distinct mindset categories were identified: Hesitant, Hopeful, and Confident, describing how strongly students rejected a fixed mindset in physics. The results suggested large gender differences in distributions at the high and low‐end groups. We also found an overall decline toward fixed mindsets across the course, and logistic regressions controlling for initial mindsets showed that women were significantly more likely than men to shift away from the Confident category. While the majority of men tended to stay within the Confident category, the majority of women moved away from it. Particularly, this differential shift was seen among students receiving Bs or Cs, the most commonly awarded grades in this course. Furthermore, there were relatively small differences in the probability of change within men as a function of grades received, whereas women showed marked declines toward fixed beliefs with either a B or C. Our findings provide empirical evidence for the dynamic, grade‐sensitive nature of students' mindsets in a calculus‐based physics course. In particular, gendered differences in the probability of retaining confidence in the face of commonly awarded lower grades have implications for improving instructional strategies and highlight the need for targeted interventions that help promote resilience among students at higher risks of adopting a fixed mindset.

The design of learning tasks represents a fundamental pedagogical process that directly shapes educational effectiveness, but this process is time-consuming and knowledgeintensive for teachers. The rapid emergence of generative artificial intelligence (GenAI) technologies has created unprecedented opportunities for supporting teachers' teaching practice. However, despite growing interest in teacher-GenAI collaboration, current research primarily focuses on pre-service teachers and overall lesson plan design, with limited attention to challenges inherent in teacher-GenAI co-design of learning tasks or what teachers learn from such collaboration. To address this gap, we employed a mixed-methods approach involving 28 inservice teachers who collaborated with GenAI tools to co-develop learning tasks across three weeks. Qualitative analyses identified four overarching interaction challenges: inefficient interactions, low-quality tasks, lack of distributed cognition, and negative human-GenAI feedback loops. These overarching challenges emerged from bilateral deficiencies-three teacher-related shortcomings (providing too little context information, providing too little pedagogical guidance, lacking GenAI skills) and six GenAI-related shortcomings (accuracy issues, limited prompt understanding, weak knowledge of content sequences, limited context knowledge, incomplete instructional solutions, usability issues). Quantitative analyses of changes from pre to post showed significant improvements in teachers' technical knowledge and AI literacy, with no changes in self-efficacy and pedagogical knowledge. Finally, teachers expressed professional development needs primarily in two areas: AI Foundations & Applications and AI Pedagogy. This study provides empirical evidence of bidirectional challenges in teacher-GenAI collaboration in designing learning tasks and offers a framework for understanding teacher professional growth through AI collaboration and further needs.

As quantum information science advances and the need for pre-college engagement grows, a critical question remains: How can young learners be prepared to participate in a field so radically different from what they have encountered before? This paper argues that meeting this challenge will require strong interdisciplinary collaboration with the Learning Sciences (LS), a field dedicated to understanding how people learn and designing theory-guided environments to support learning. Drawing on lessons from previous STEM education efforts, we discuss two key contributions of the learning sciences to quantum information science (QIS) education. The first is design-based research, the signature methodology of learning sciences, which can inform the development, refinement, and scaling of effective QIS learning experiences. The second is a framework for reshaping how learners reason about, learn and participate in QIS practices through shifts in knowledge representations that provide new forms of engagement and associated learning. We call for a two-way partnership between quantum information science and the learning sciences, one that not only supports learning in quantum concepts and practices but also improves our understanding of how to teach and support learning in highly complex domains. We also consider potential questions involved in bridging these disciplinary communities and argue that the theoretical and practical benefits justify the effort.