The growth of the quantum information science and engineering (QISE) industry has increased interest in how undergraduate programs prepare students for careers in this field. Prior research emphasizes the value of experiential learning as preparation for the quantum industry but lacks specificity regarding the experimental skills needed for positions available to bachelor’s degree graduates. In this study, we investigate the experimental skills associated with bachelor’s-level quantum industry positions through 44 semistructured interviews with quantum industry professionals. Guided by the American Association of Physics Teachers recommendations for the undergraduate physics laboratory curriculum, we characterize the experimental skills associated with positions described as requiring bachelor’s-level preparation and thematically synthesize them into four categories: instrumentation, computation and data analysis, experimental and project design, and communication and collaboration. We further examine how these skills cluster across role types and articulate them as learning goals to provide guidance for educators interested in aligning undergraduate instruction with the needs of students wanting to pursue a career in the quantum industry. Our findings suggest the need to emphasize the discussion of hardware in QISE theory courses, expand experimental training through instructional laboratories, and intentionally integrate professional skills in undergraduate QISE education.

We report results from a mixed-methods survey study ($N=81$) examining how undergraduate students in introductory physics courses use, trust, and prefer to integrate artificial intelligence (AI) tools into their learning. Among respondents, 91% (95% confidence interval (CI): [83%, 96%]) reported using AI for physics coursework, yet only 41% (95% CI: [30%, 52%]) trusted AI-generated physics explanations—a 50-percentage-point trust-utility gap consistent with domain-calibrated skepticism rather than uncritical adoption. Thematic analysis of open-ended responses ($n=67$; Cohen’s $\kappa =0.78$) identified eight themes; the most distinctly physics-specific finding was that 40% of qualitative respondents spontaneously articulated whereAI fails in physics—visual-spatial reasoning, circuit analysis, and abstract physical reasoning—aligning with physics education research on student difficulties with multirepresentational tasks. This skepticism is also consistent with Kortemeyer’s benchmarking results, which show that AI performance is weakest on precisely the multirepresentational task types that students identified as failure-prone, suggesting that student trust calibration may track underlying AI competence boundaries in physics. A majority (65%) preferred optional over mandatory AI integration. Because high-performing students were overrepresented by 13.9 percentage points, the estimated preference for optional integration (and/or resistance to mandatory) may be inflated in this sample. These findings highlight a critical unresolved priority—the verification gap between self-reported and actual verification behavior—and inform recommendations for AI-integrated physics pedagogy.

Performance in introductory courses, particularly physics, is often crucial for student success in science, technology, engineering, and mathematics majors and can impact an individual’s tendency to persist in their chosen field. To enhance students’ individual learning experiences, faculty at many universities have worked to develop open-access, self-study materials to help build conceptual understanding and problem-solving skills. Faculty at Texas A&M University have contributed to these efforts, creating more than 200 online video resources and a broad bank of prior exams available to students. This work explores and measures the impact that these resources can have on student course outcomes in an introductory, calculus-based electricity and magnetism course. Data were collected from three fall semesters, 2021–2023, including classroom performance, a conceptual assessment, and relevant university-level data to contextualize student background and preclass abilities. We present results from regression analyses examining the relative importance of multiple resources offered concurrently to students. We also discuss how the use of supplemental material affects course performance, alongside other potential predictors such as demographics and prior preparation. Similar to prior studies, we found that relevant prior preparation in mathematics was the strongest predictor of student performance, with prior physics knowledge being a weaker but still statistically significant predictor. Students’ utilization of supplemental prior exams was the second-highest predictor of student performance. First-generation students were observed to have a slightly lower average performance on exams. However, interaction terms in the regression models suggested that first-generation students using supplemental prior exams were able to close this gap. Through anonymous surveys, students reported favorable impressions of the materials, with over 80% of students sharing that they had a noticeable contribution to their learning outside of the classroom and 98% stating they would recommend them to their peers.

Collaborative mathematical modeling of real-world problems requires learners not only to engage in multiple epistemic practices but also to remain aligned around shared physical models as assumptions and solution paths evolve. However, few empirical studies have traced how such coordination unfolds moment by moment during authentic work. This study examined the in vivo discourse of a group of preservice physics teachers solving an ill-structured mechanics problem by tracking their collaborative reasoning turn by turn. We analyzed the group’s work by coding their epistemic modeling phases and the operational physical models in use, alongside a time-resolved measure of discourse model coherence (DMC) that indexes alignment around shared model assumptions. By combining turn-level qualitative coding with time-resolved quantitative visualizations, we traced how the group’s shared problem space evolved over time and identified moments of convergence and divergence. The analysis revealed that the group progressed through six epistemic phases of modeling while iterating across multiple operational physics models of increasing complexity. Periods of heightened complexity were marked by slower progression and frequent fluctuations between epistemic phases. Cross-model comparisons functioned as a key evaluative resource, in which simpler models served as benchmarks for judging more complex ones. Instructor prompts played a significant role: moves that narrowed the modeling scope tended to stabilize coherence, whereas prompts that reopened assumptions often increased epistemic uncertainty and temporarily disrupted alignment that produced measurable drops in DMC. These findings provide a time-resolved account of collaborative modeling dynamics and offer methodological and instructional insights for supporting modeling-rich physics learning.

We present three versions of a pendulum lab activity to explore how theoretical commitments, motivations, and aspirations are reflected in curriculum design. In earlier work, Boudreaux and Elby [How curriculum developers’ cognitive theories influence curriculum development, Phys. Rev. Phys. Educ. Res. 16, 020144 (2020)] discussed how different theoretical perspectives led them to different designs of tutorials. Here, we discuss our finding that even starting from the same theoretical perspective and having similar goals for students can lead to differences in design. We give three interacting reasons why our labs diverge: We have different expectations of students at our respective institutions; we have different ancillary goals; and we draw different implications from our shared theoretical commitments. Our account demonstrates the complexity of the relationship between theory, goals, and curriculum design. In this way, it adds to prior arguments for the importance of designers’ articulating the reasoning for their choices as well as for the possible value of instructors’ responsive adaptations to curricula.

Since 2022, artificial intelligence (AI) tools have become publicly available, thereby enabling physics teachers to employ them in a multitude of ways. The aim of this research is to evaluate the potential of AI in assisting physics teachers in lesson planning. The objective is to identify which cognitive demands of lesson planning can be carried out by the AI, therefore reducing the workload for teachers. To compare teachers with different backgrounds, we examine three groups: prospective, out-of-field, and experienced physics teachers. The participants planned a lesson on Newton’s third law using an AI tool. To analyze the participants’ lesson planning process, we employed evaluative qualitative content analysis. Each of the groups presented a different approach to lesson planning using AI. For example, out-of-field physics teachers focused on the physics content, while experienced physics teachers were more prone to asking how to adapt the learning materials to the students’ cognitive level. One of the key findings was that the AI tools considered the clarity of the learning objectives and phasing of the lessons without necessarily being prompted, in all three groups. Moreover, in general, the experienced physics teachers were more critical about using AI for lesson planning than the other two groups. The findings of this study might inform teacher professional development courses focused on the use of AI in physics education.

The current work aims to better understand student course experiences for those who reported negative perceptions in introductory physics. We conducted semistructured interviews with 24 students who reported negative perceptions of their class on a screening survey. Participants were asked to share general reflections on challenges and successes they experienced, as well as their reflections on specific aspects of the course (e.g., experiences with instructors and peers). Interview transcripts were then coded to identify the types of experiences students reported, whether they were experienced as positive or negative, as well as the themes and features associated with those experiences. Experiences with the classroom, course structure, instructors, and exams were most frequently reported as negative. Experiences with peers, help-seeking, course curriculum, and specific learning activities were the most positive, although only experiences with peers had more positive than negative reports. We then used a resources vs demands framework [M. D. Seery, The biopsychosocial model of challenge and threat: Using the heart to measure the mind, Soc. Pers. Psychol. Compass. 7, 637 (2013)] to interpret the common instructional, cognitive, and motivational themes and features reported across multiple contexts. We discuss the implications of the results for theory and practice.

Learners often struggle to develop expandable mental models of polarization due to its abstract nature and the multitude of models used to describe it. A closer examination of the structure of mental models for linear polarization is therefore worthwhile in order to gain insight into mental abstraction processes. Existing research has largely documented misconceptions, leaving the underlying cognitive structures of mental models insufficiently explored. To address this gap, we employed the Fidelities Model of Conceptual Development (FMCD) to investigate learners’ cognitions of linear polarization. The framework, distinguishes two dimensions of students’ mental models: Gestalt, which refers to visual and structural representations, and Functionality, which captures how well a model explains underlying processes. The degrees to which these dimensions align with reality allow for a typification of learners’ reasoning patterns. We developed a test instrument based on established students’ mental models and validated it through expert review and think-aloud studies. In a cross-sectional survey with $N=178$ German secondary school and university students, confirmatory factor analysis confirmed a robust two-factor structure ($\mathrm{CFI}=0.96$, $\mathrm{RMSEA}=0.03$, $\mathrm{SRMR}=0.07$) with solid factor-reliability (${\omega }_{\text{FF}}=0.71$, ${\omega }_{\text{FG}}=0.81$). Descriptive results showed developmental trends: while secondary students often exhibited “dual” models combining both FG and FF, university students increasingly relied on functionally oriented reasoning. The findings validate the FMCD in the domain of polarization and provide a diagnostic tool that informs instructional design for supporting students’ conceptual development in optics.

Insufficient prior knowledge in mathematics and physics is seen as a key factor for high attrition rates in physics degree programs. To investigate trends in students’ academic preparedness, this study examines how the mathematics and physics prior knowledge of first-year physics students in Germany has changed between 2013 and 2023. Using data from standardized diagnostic tests administered at multiple universities in both cohorts, we compared performance across cohorts and institutions. Results indicate small but meaningful increases in average performance from 2013 to 2023: mathematics knowledge improved slightly (Cohen’s $d=0.10$), and physics knowledge even more (Cohen’s $d=0.21$). Despite this improvement, students’ average performance remains only modest—67% correct in mathematics and only 38% correct in physics. Moreover, our findings indicate a growing heterogeneity in students’ preparation: while mathematics and physics performance improved at some universities, mathematics performance declined at others. Overall, the results underscore the importance of continued monitoring of incoming students’ prior knowledge and support the use of diagnostic tools to inform targeted instructional strategies transition to university-level physics.

This study investigates the impact of a novel application of generative artificial intelligence (AI) in physics instruction: engaging students in prompting, refining, and validating AI-constructed simulations of physical phenomena. In a second-semester physics course for life science majors, we conducted a comparative study of three instructional approaches in a laboratory focused on electric potentials: (i) students using physical equipment, (ii) students using a prebuilt simulator, and (iii) students using AI to generate a simulation. Among the groups, we found significant differences in performance on conceptual assessments of the laboratory content (${\eta }^2=0.359$). Post hoc analysis showed that students in both the AI-generated and prebuilt simulation conditions scored significantly higher on the conceptual assessments than students in the physical equipment condition. Students in these groups also reported more favorable perceptions of the learning experience. Finally, this preliminary study highlights opportunities for developing students’ modeling skills through the processes of designing, refining, and validating AI-generated simulations.

Computation has become an integral part of physics research. However, little is known about how students learn to productively use computation as a tool beyond the introductory level, especially as they transition into physics research. In this study, we apply the theory of physics computational literacy and the novice-expert framework to describe the development of expertise in computational physics, as students transition from novice to journeyman computational physicists. We base this description on a thematic analysis of interviews with 13 computational physics master’s students with extensive experience using computation. We first describe the most important elements driving the development of computational physics expertise, identifying two distinct transitions of competence during their studies, driven by experience with large computational projects and professional research. We then present an overview of the various skills students attain on this path toward the journeyman level of computational physics expertise. Based on these results, we argue for the need to assist students in collaborative coding and in the learning of new tools, as well as for the importance of large, scaffolded, computational projects in helping students develop the advanced skills needed for computational research.

The rapid growth of quantum information science and technology (QIST) in the 21st century has created both excitement and uncertainty about the field’s trajectory. This qualitative study presents perspectives from leading quantum researchers, who are educators, on fundamental questions frequently posed by students, the public, and the media regarding QIST. Through in-depth interviews, we explored several issues related to QIST including the following key areas: the current state of quantum computing in the noisy intermediate-scale quantum (NISQ) era and timelines for fault-tolerant quantum computers, including a possible timeline for quantum advantage on Shor’s factoring algorithm, the feasibility of personal quantum computers in our pockets, and promising qubit architectures for future development. Our findings reveal diverse yet convergent perspectives on these critical issues. While experts agree that the current machines with physical qubits that are being built in the NISQ era should be called quantum computers, most estimated that it will take a decade to build a small fault-tolerant quantum computer, and several decades to achieve scalable systems capable of running Shor’s factoring algorithm with quantum advantage and some stated that they would find it exciting if some law of physics prevents achieving those ultimate goals. Regarding carrying a quantum computer in the pocket, experts viewed quantum computers as specialized tools that will remain in central locations such as data centers and can be accessed remotely for applications for which they are particularly effective compared to classical computers. Quantum researchers suggested that multiple platforms show promise, including neutral atoms, superconducting circuits, semiconducting qubits, and photonic systems, with no clear winner emerging. These insights provide valuable guidance for educators, policymakers, and the broader community in establishing realistic expectations for developments in this exciting field. Our findings can provide valuable information for educators to clarify student doubts about these important yet confusing issues related to quantum technologies at a time we are celebrating the International Year of Quantum Science and Technology.

Writing is a critical skill for modern science, enabling collaboration, scientific discourse, public outreach, and more. Accordingly, it is important to consider how physicists and astronomers are trained to write. This study aims to understand the landscape of science writing education, specifically in physics and astronomy, in higher education in the United States. An online survey probing various aspects of their writing training in both undergraduate and graduate school was administered to 515 participants who have obtained training in physics and/or astronomy, or related fields, at the level equal to or beyond upper-division undergraduate study. Humanities and writing requirement courses appear to have a key role in general writing education, while laboratory courses and feedback from mentors are the dominant modes of science writing education in undergraduate and graduate school, respectively. There is substantial variation in the quality of writing education in physics and astronomy, often dependent on the student’s institution and/or mentor. Some participants also report that their success in disciplinary writing was a result of a solid foundation from K-12 education and/or self-direction toward resources; such reliance on past experiences and student background may contribute to inequality in the field. Many participants also stated a clear desire for more structured writing training to be available in the field. We provide suggestions for how to implement such training to meet the needs of the community identified in the survey.

With the rise of quantum computing, interest has grown in using two-state quantum systems (qubits) at the secondary level to foster students’ conceptual understanding. Quantum measurement, in particular, is central to quantum theory and its accurate conceptualization by students is crucial for grasping fundamental quantum principles. However, instructional methods typically make use of different contexts (i.e., different two-state systems), significantly affecting students’ conceptual development in quantum physics. In this paper, we report findings from a cluster-randomized field trial involving 181 students taught through three inquiry-based, two-state approaches: the which-path-encoded single-photon, the polarization, and the double-well potential approach. All three approaches supported students’ conceptual development, yet students taught using photon polarization and the double-well potential significantly outperformed those participating in a course following the which-path-encoded single-photon approach. Our findings indicate that students participating in the which-path-encoded single-photon approach often retain mixed-thinking frameworks, whereas those taught with photon polarization or the double-well potential approaches were more likely to develop toward quantum thinking. Thus, our findings underpin how influential the choice of (experimental) context is on students’ conceptual development (also) in quantum physics.

Science textbooks often include text and diagrams to aid learner understanding. Yet traditional written instructions in textbooks often separate them, which can increase cognitive load and hinder effective learning. This research proposes and tests a novel presentational format for written instruction that incorporates spatial contiguity, numbered reading sequences, and color matching, aiming to enhance science learning. The effectiveness of the novel format was assessed through two randomized experimental studies. The first study involved 349 high school students in a popular science context, and the second included 187 students in a complex physics course. The results revealed statistically significant improvements in students’ learning outcomes and self-perceived understanding when exposed to the novel instructional format. The improvements were particularly notable in the study focused on complex scientific laws in physics. Furthermore, students reported lower levels of mental effort with the novel format compared to traditional formats, particularly in contexts involving more challenging materials. These results align with cognitive load theory in multimedia learning. The novel presentational format can significantly enhance student learning outcomes and understanding level. It offers a practical and more efficient alternative for instructional material design.

Embodied forms of representation like gesture have been shown to play an important role in how learners conceptualize phenomena in physics; however, we know little about how students use gesture to capture the idea of instantaneity. Drawing on multimodal microanalysis of interaction, we examine how undergraduate physics students use representational gesture to make sense of instants or single moments in time while modeling energy dynamics. Our analysis demonstrates how students use four different forms of representational gesture to capture instantaneity: These include (i) looping and replaying a physical action, (ii) showing a tiny subinterval of time, (iii) freezing a frame of an action, and (iv) marking a boundary location on an imaginary timeline. Implications for physics learning are discussed.

In this paper, we describe our extension of the action, process, object, schema (APOS) theory to capture the complex interplay of mathematics and physics. We do this in the context of the 2D heat equation. We describe a hypothetical learning trajectory of the 2D heat equation, a preliminary genetic decomposition that stresses the conceptual understanding of its mathematical formulation. In our extension of APOS to physics education, we highlight how our approach precisely captures the interplay between mathematics and physics in different mental constructions of the framework. In addition to this core focus, we consider other layers when designing the preliminary genetic decomposition: the key concepts and the necessary depth of students’ prerequisite knowledge of the chosen context, the challenges students commonly face with these concepts, and the use of multiple external representations. We conclude by giving an overview of future research to be done after the design of the preliminary genetic decomposition.

Quantum information science and engineering (QISE) is rapidly gaining interest from those within many disciplines, and higher education needs to adapt to the changing landscape. Although QISE education still has a strong presence and roots in physics, the field is becoming increasingly interdisciplinary. There is a need to understand the presence of QISE instruction and quantum-related instruction across all disciplines in order to figure out where QISE education is already happening and where it could be expanded. Although there is recent work that characterizes introductory QISE courses, there is no holistic picture of the landscape of QISE and quantum-related education in the United States. To understand how QISE education is evolving, we analyzed course catalogs from 1456 U.S. institutions. We found 61 institutions offering QISE degree programs, mostly at Ph.D.-granting schools, with interdisciplinary programs being the most common. Physics, electrical and computer engineering, and computer science are the primary contributors to these programs. Across all institutions, we identified over 8000 courses mentioning “quantum,” mainly in physics and chemistry departments, but about one-third of institutions in our study had none. We also found over 500 dedicated QISE courses, concentrated in Ph.D.-granting institutions, primarily in physics, electrical and computer engineering, and computer science. Physics leads in offering both general quantum-related courses ($\sim 4700$) and QISE-specific courses ($\sim 200$). While quantum knowledge is often a prerequisite for QISE, there are efforts to make QISE education more accessible to students with less background in quantum mechanics. Across multiple disciplines, we also see that QISE topics are being introduced in courses not fully dedicated to QISE, which may be a productive strategy for increasing students’ access to QISE education. Our dataset and analysis provide the most comprehensive overview to date of quantum education across U.S. higher education. Additionally, this work is the first to address, in detail, the landscape of quantum content in two-year and community colleges and examines the largest set of MSIs in this context. We hope these findings will support and guide future efforts in curriculum design, workforce development, and education policy across the quantum ecosystem.

This research was a qualitative transcendental phenomenological exploration of astronomy identity formation among astronomy majors and physics majors engaged in astronomy research. Participants ($N=10$), all of whom identified with traditionally marginalized groups in astronomy, were recruited from two large universities in New York state at different stages in their undergraduate careers. Social cognitive career theory and several science, technology, engineering, and mathematics identity frameworks conceptually guided the analysis of astronomy identity for undergraduate majors and undergraduate astronomy researchers by exploring participants’ interest in, choice to study, and persistence in astronomy. Themes related to astronomy interest were popular culture and directly observing astronomical phenomena, while astronomy choice and persistence were facilitated by experiences in introductory coursework, recognition from faculty, and socializing with peers. The emergent astronomy identity framework was characterized by six distinct yet interrelated constructs: (1) interest in astronomy, typically rooted in observing naturally occurring phenomena and engaging with popular culture; (2) recognition from peers, experts, and families; (3) peer socialization about shared astronomy experiences; (4) competence or the belief one can do the work of an astronomer; (5) sense of belonging within the astronomical community; and (6) astronomy career expectations. Findings from this research provide insights on factors that influence undergraduates in four-year colleges to study astronomy, and how students’ past experiences may lead to a natural interest in astronomy that may be fostered in secondary and postsecondary contexts. Implications suggest departments and institutions may facilitate the accessibility of astronomy at the collegiate level by promoting a more inclusive astronomy community, fostering interactions with astronomy faculty and graduate students, providing opportunities for undergraduate research, and communicating expectancy for astronomy-related future careers.

Examining teaching practices in university physics is crucial for addressing the lack of student achievement in and engagement with the subject. Research-based instructional strategies (RBIS) offer evidence-based methods to enhance student engagement and learning outcomes, but face adoption challenges due to institutional, contextual, and individual barriers, particularly in higher-year courses. This case study investigates how second-year physics instructors at a research-intensive university within a trimester system engaged with RBIS, using the EPIC-implementation model to guide analysis. A case-study approach, using semistructured interviews and secondary data (e.g., course evaluations and materials), explored instructors’ decision making through the EPIC-implementation model (exposure, persuasion, identification, commitment, and implementation). The study reveals how individual motivation, peer influence, institutional constraints, and discipline-specific challenges shape RBIS adoption. While some instructors maintained commitment, others reverted to traditional methods due to systemic barriers. Co-teaching dynamics played a key role in influencing adoption, yielding mixed results. The findings contribute to the growing literature on instructional change by highlighting the importance of institutional support, peer mentorship, and alignment with teaching philosophies in sustaining RBIS adoption in major courses beyond the first year. They highlight the need for supportive institutional frameworks, discipline-specific strategies, and the alignment of teaching methods with instructors’ values. This study provides practical insights for educators, institutional leaders, and policymakers to encourage innovative teaching practices in higher education.