Understanding the mechanisms of student sensemaking while navigating the physics problem-solving process can play an important role in developing approaches to helping students become proficient problem solvers. Since most prior studies on student sensemaking in physics have focused on introductory physics, this study sheds light on a relatively unexplored research area involving advanced physics students’ sensemaking. We conducted individual interviews with graduate students to investigate their sensemaking while solving upper-level undergraduate electrostatics problems in the context of the method of images. Analysis through the lens of the epistemic game framework proposed by Tuminaro and Redish shows that the central part of student sensemaking can be mapped onto the Pictorial Analysis game, with other epistemic games also playing a role, but the ontological components of some games require refinement. The study involves an in-depth comparison of sensemaking on related but distinct problems for individual graduate students and a comparison between multiple students solving the same problems. It provides insights into how graduate students transfer and translate strategies between different problems while sensemaking. Graduate students showed persistence in activating different knowledge resources and consistent use of reasoning primitives across different problems, such as a “balancing” primitive multiple times while sensemaking. The study investigated the impact of small nudging and scaffolding that resulted in significant improvements in graduate students’ problem-solving approaches. Another novel finding is that graduate students’ problem-solving approaches significantly improved with successive attempts and multiple drawings for the same problem and across problems. Often, graduate students initially drew more three-dimensional figures with conducting planes given in the problem statement to visualize and understand the problem; they also drew diagrams from different viewing angles, which helped in improving their physical understanding and comprehension of the symmetry of the underlying problem.

Generative artificial intelligence (GenAI) offers strong potential to transform teaching practices; however, empirical research on its classroom effectiveness remains limited. This study explores how GenAI can be integrated into preservice physics teacher training by replacing two traditional instructor roles in an instructional design course: providing evaluative feedback and facilitating collaboration. A GenAI-based assistant was developed to assess instructional designs according to predefined criteria and to provide personalized feedback. A total of 110 preservice physics teachers received feedback from one of the three sources: a teaching expert, a teaching assistant, or GenAI to revise their instructional designs. They subsequently collaborated with GenAI for further refinement. Results indicate that GenAI evaluations closely aligned with those of human experts and teaching assistants. In blinded evaluations, preservice physics teachers rated GenAI feedback as comparable to human feedback. Furthermore, collaboration with GenAI resulted in significant improvements in the overall quality of instructional design. This progress was particularly most evident in the structured, content-expandable components. While these findings demonstrate GenAI’s feasibility from a functional perspective, challenges related to preservice teachers’ perceptions also emerged. Although design quality improved through collaboration with GenAI, independent design skills, measured by a final paper-and-pencil test, showed no similar improvement. Preservice physics teachers acknowledged the need to filter and revise GenAI-generated content, but often directly adopted its suggestions due to the convenience of GenAI. Notably, feedback was rated lower once participants realized it was AI-generated, even if the actual source was not, revealing trust-related concerns. This study moves beyond technical evaluations of GenAI, offering insights into the pedagogical challenges and limitations that arise when GenAI assumes the roles that are traditionally performed by human educators.

In the world of science, technology, engineering, and mathematics education practice, alternative grading strategies have been implemented and disseminated with the goal of reinforcing student outcomes like productive reasoning, motivation to learn, and sense of identity. However, while PER has successfully improved these outcomes through reformed instructional strategies, much less work has investigated how grading practices influence these outcomes. This dearth leaves PER without a framework readily available to formulate or interpret studies of alternative grading practices. To help address this gap, we surveyed $\mathit{N}=94$ students from introductory algebra-based and calculus-based physics courses, asking free-response questions about their experiences with and perspectives on grading in physics. We thematically coded their responses to identify themes in what they said grades represent (such as their effort invested in the course or their mastery of course content) and what effects they said grades produce (such as stress or an adversarial relationship with the instructor). We conducted focus groups to member-check these themes with the students. For themes about what they said grades represent, we further identify whether the student framed this representation positively (“Grades should represent my effort”), negatively (“Grades should not represent the features of a particular assignment”), or neutrally or in mixed terms. We particularly attend to whether students indicated that grades produce an adversarial relationship with their instructor, finding that a greater percentage of these students positively spoke of grades as representing improvement in their performance than among students who did not mention an adversarial relationship. We discuss how these themes offer insights into how we might further research alternative grading strategies, how students’ perspectives on grades might influence the broader goals of PER, and implications for physics instruction.

Research indicates that students learn effectively when they actively engage in planning, monitoring, and reflecting on their learning—a process known as self-regulated learning (SRL). This mixed-methods study examined the impact of a semester-long SRL intervention in an undergraduate physics course. In the first year, the instructor taught the course under standard conditions. The following year, after brief training sessions, the same instructor added digital prompts targeting various aspects of SRL to their course assignments. Results showed that students in this intervention group performed better on the final exam after controlling for preexisting self-efficacy, metacognitive awareness, and GPA. However, SRL outcomes did not increase in the intervention condition. Analysis of the prompts revealed that the instructor disproportionally focused on content- and management-related prompts and underutilized motivation, metacognition, and cognitive strategy prompts. Interview with the faculty indicated that the intervention was straightforward to implement, contributed to humanizing the course, and that additional training would have been desirable to help design prompts. Provided appropriate training and support structures are available, these findings show that integrating SRL strategies through digital prompts can be a practical and effective approach for supporting instructors in fostering students’ SRL.

The interplay of mathematics within physics—termed here as physmatics—presents unique cognitive challenges for students, requiring coordination between mathematical reasoning and physical sensemaking. While previous studies have documented common physmatic difficulties, capturing these challenges as they emerge in classroom instruction is rare. This study analyzed 273 teacher–student interactions from 23 video-recorded physics lessons taught across a diverse range of classrooms, aiming to identify and categorize physmatic difficulties as they naturally arose during instruction. Twenty-four codes of difficulties were identified through content analysis and grouped into five overarching clusters: (i) questions about model components, (ii) questions about the model, (iii) difficulties applying mathematics in a physical context, (iv) interrepresentational fluency, and (v) unit-related difficulties. A key finding of this study was the identification of systematic associations between the modeling skills teachers aimed to develop (i.e., mathematization, manipulation, interpretation, and validation) and the corresponding student difficulties. These findings underscore the centrality of modeling as a pedagogical framework for physics instruction and suggest that teachers who are aware of the links between skills and difficulties may be better prepared to anticipate student struggles, respond to them in real time, and even leverage them as part of their instructional practice.

Think-aloud interviews were used to investigate college students’ cross-disciplinary understanding of energy concepts. Interview subjects were enrolled in a set of four science content courses (physics, biology, geology, and chemistry) for preservice K-8 teachers. These courses, built around the common theme of the flow of energy and matter through interacting systems, use similar vocabulary, modeling tools, and pedagogy to teach energy concepts. The interview tasks were designed to probe how students apply energy concepts originally learned in the physics course in less familiar contexts in geology, biology, and chemistry. Transcripts were analyzed using a coding scheme based on conceptual resources. Thematic analysis was then conducted to identify common patterns in students’ cross-disciplinary reasoning, particularly with regard to potential energy. The constructs of disciplinary and situated productivity were used to guide the development of themes. We find that students engage in productive reasoning about energy across disciplines. Students activate ideas related to energy forms and the objects to which these forms are attached with relatively high frequency, and ideas related to physical indicators and mechanisms for energy transfers and transformations with relatively lower frequency. Students apply potential energy concepts across disciplines, but often struggle to identify the physical manifestations of potential energy changes in less familiar contexts. While this study was conducted in a set of courses for preservice teachers, the findings have broader implications for introductory physics courses that serve as prerequisites for other disciplinary coursework.

This study examines how the Minnesota Assessment of Problem-Solving rubric can be used to diagnose learning needs in secondary physics education. In total, 51 students (grades 9 and 11) from 5 German secondary schools solved a physics problem-solving task and completed an energy concept test. Rubric-guided analysis of their written problem solutions revealed seven characteristic performance patterns, ranging from expertlike reasoning to incomplete or failed approaches. While students generally managed descriptions and basic calculations, many struggled to apply the conservation principle and to structure their reasoning coherently. The combination of rubric scores with conceptual test results highlighted whether difficulties stemmed from missing knowledge or from challenges in applying it. These findings demonstrate that this combination of assessment tools, adapted to the secondary level, offers a practical approach for formative assessment and targeted support in physics problem solving. However, difficulties arise when students submit incomplete problem solutions. Future developments could focus on atomized approaches or the integration of AI technologies for automatic assessment.

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.

Physics programs are continually evolving to better support student learning and meet the diverse needs of their students. Achieving many of these goals requires not only structural adjustments but also fundamental shifts in departmental culture. Recognizing this, disciplinary organizations in physics have placed systemic change and equity at the center of reform efforts, identifying them as essential pillars of meaningful and sustainable change. Yet, tools for assessing departmental culture around educational change remain limited. In this study, we introduce the Culture around Systemic Change Survey (CSCS), designed to assess departmental culture around pursuing any program-level change (undergraduate and/or graduate) aimed at improving student experience or outcomes (e.g., curriculum revisions, instructional practices, and efforts to strengthen departmental climate). This new instrument measures faculty and staff perceptions of their department’s “current” and “ideal” states with respect to their typical approaches to leading change efforts. Using responses from the “current” scale only ($N=111$ participants across 33 departments), we conducted a psychometric evaluation of the CSCS. Exploratory factor analysis supported a five-factor structure, including open-mindedness (OM), student involvement (SI), collective interpretation of evidence (CIE), sustainability (S), and disruption of systemic injustices (DSI). As survey development is an iterative process, future work will focus on refinement and confirmatory analysis. This work sets the foundation for conducting population studies that assess the state of progress of the physics community along an equitable and systemic culture to pursuing educational change.

In introductory physics courses, which are foundational for many science, technology, engineering, and mathematics (STEM) disciplines, students’ motivational beliefs, such as self-efficacy, interest, and identity, are critical for their success and persistence. However, the high academic demands of physics courses can lead to learning burnout, a detrimental psychological syndrome comprising exhaustion, cynicism, and inadequacy that threatens student development. Despite the recognized importance of a supportive learning environment, there is a lack of research on the role of learning burnout in physics. More specifically, the pathways linking the learning environment, burnout, and motivational beliefs, as well as the generalizability of these pathways across genders, have not been systematically investigated. This study addresses these gaps by testing a model where learning burnout mediates the relationship between students’ perceived learning environment and their motivational beliefs and physics identity. The research employed a cross-sectional survey design with a sample of undergraduate students ($N=1574$) from three universities in China. Data were analyzed using structural equation modeling (SEM) to test the hypothesized associations and multigroup SEM to examine gender invariance. The results revealed that a positive perceived learning environment, particularly a strong sense of belonging, was significantly associated with lower learning burnout. Furthermore, the study confirmed that learning burnout, specifically the dimensions of cynicism and inadequacy, partially mediated the relationship between the learning environment and students’ motivational beliefs. Students’ physics identity was most strongly and directly associated with perceived recognition, interest, and self-efficacy. Crucially, the structural model demonstrated invariance across genders, indicating that the identified psychological pathways are equivalent for male and female students. This study makes a significant contribution by integrating the concept of learning burnout into the physics education research framework and identifying its role as a key mediator. It provides a new perspective on gender equity, suggesting that disparities may arise from differing perceptions of the learning environment rather than from fundamentally different psychological processes.

In this article, we report the development and validation of a new two-tier concept inventory, the Basic Electricity Assessment of Students’ Thinking (BEAST), designed to assess common conceptual difficulties (“student ideas,” such as current consumption and sequential reasoning) and the understanding of fundamental electric circuit concepts (e.g., current, voltage, and resistance) among undergraduate students. The final version of the BEAST covers six student ideas, each linked to a distinct physical concept. Each student idea is assessed using three two-tier single-choice items. To refine the items and evaluate their content validity, including the correctness of answers and the intended categorization of student ideas based on response patterns, feedback and ratings from five experts were incorporated. The test was administered to a sample of $N=176$ students enrolled in introductory physics courses across six different German universities. Quantitative evaluation using classical test theory and structural analyses demonstrated strong evidence supporting the test’s content and construct validity. Confirmatory factor analysis utilizing parsimonious bifactor models showed a good fit of the model to the data.

Physics education research (PER) has shown substantial growth in recent years, yet comprehensive reviews mapping the field’s overall trajectory remain scarce. The leading journal, Physical Review Physics Education Research (PRPER), has been a cornerstone of the discipline since its launch in 2005. Marking its 20th anniversary, this study presents a comprehensive bibliometric analysis of PRPER publications from July 2005 to June 2025. Using data extracted from the Web of Science and Scopus, this study examines key indicators, including publication/citation trends, contributor demographics, prolific author characteristics, and collaborations, alongside the field’s intellectual structures and their evolution. The results reveal sustained growth in publication volume and a concentration of authorship within U.S.-based institutions. The analysis finds that PRPER publications group well into seven major research clusters: Understanding and Supporting Student Learning; Identity and Equity; Conceptual Inventory; Physics Laboratory; Physics Problem Solving; Attitude and Epistemology; and Quantum Physics Education. Notably, Quantum Physics Education emerges in this analysis as one of the seven distinct major research clusters identified through bibliographic coupling. Timeline analysis reveals a shift in thematic emphasis from foundational work on conceptual assessment toward increasing focus on identity, equity, and quantum education. This bibliometric overview not only traces the intellectual trajectory of PRPER but also offers insights into the broader evolution of PER, highlighting existing gaps, such as the need to integrate equity research into quantum education, and suggesting future directions for the field.

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.

For several years, we have been investigating student conceptual understanding of radioactivity, particularly regarding half-life. Although it is considered to be random when an individual nucleus undergoes the sudden process of radioactive decay, the duration of time within which half of a radioactive sample decays (i.e., the half-life) is a predictable quantity. Many students generally struggle with the idea that random behavior of individual agents can give rise to predictable patterns in the collective; specific to radioactivity, many students have said both in interviews and surveys that, if you are looking at an individual nucleus, half of the nucleus will have decayed after one half-life. Our findings have indicated, however, that this idea (of individual nuclei decaying in a predictably continuous manner) is often not a robust and intact mental structure; rather, in other contexts, the same students correctly discuss radioactive decay as being instantaneous and unpredictable. We developed a survey to measure this context dependency and administered it both to high school students in Austria and to college students at the University of Alaska Fairbanks. Using both cluster analysis and alluvial diagrams to organize our data, we find a great variety in answering patterns across the survey prompts, indicating that student ideas have not yet crystallized. Our paper thus provides experimental evidence in support of a “knowledge in pieces” model of student cognition about radioactivity as opposed to a “misconceptions” model.

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.

Upper-level, undergraduate quantum mechanics (QM) is widely considered a difficult subject with many varied approaches to teaching it and considerable variation in content coverage. For example, two common approaches to undergraduate QM instruction are a spins-first approach, which focuses on the postulates of QM in spin systems before discussing wave functions, and a wave functions-first approach, which focuses on the Schrödinger equation and its solutions for continuous functions in various potentials before discussing spin. These different approaches, along with the content variability in the textbooks used by instructors, may mean students learn different things in QM classes across the United States (U.S.). In this paper, we offer a characterization of QM courses based on survey responses from instructors at institutions across the U.S. With the responses of 76 instructors teaching QM courses (or sequences), we present results detailing their teaching methodologies, use of pedagogical resources, and coverage of QM topics. We find that the plurality of instructors in our sample are using traditional lecture, but many instructors are using interactive lecture or another nontraditional method. Additionally, instructors are using a wide variety of pedagogical tools (e.g., clicker questions). Many instructors in our sample reported teaching single-semester (or single-quarter) QM courses; these instructors report similar content coverage to instructors teaching first-semester (or first-quarter) QM courses, though their responses showed greater variability. We additionally report a comparison of content coverage between instructors using wave functions-first vs spins-first approaches, finding a large degree of overlap with differences in coverage for a few specific topics. These findings can help inform both future research and instructional efforts in QM education.

Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students’ mechanistic sensemaking, working from a coding scheme that is aligned with previous work in physics education research (PER) and that is amenable to recently developed ML classification strategies using language encoders. We describe pilot tests of the tool, in three versions with different language encoders, to analyze sensemaking evident in college students’ written responses to brief conceptual questions. The results show, first, that the tool’s measurements of sensemaking can achieve useful agreement with a human coder, and, second, that encoder design choices entail a tradeoff between accuracy and computational expense. We discuss the promise and limitations of this approach, providing examples of how this measurement scheme may serve PER in the future. We conclude with reflections on the use of ML to support PER research, offering cautious optimism for strategies of codesign between PER and ML.

In the rapidly evolving interdisciplinary field of quantum information science and technology, a major obstacle is the need to understand advanced mathematics to solve complex problems. Current findings in educational research suggest that incorporating visualizations into problem-solving settings can have beneficial effects on students’ performance and cognitive load compared to relying solely on symbolic problem-solving content. Visualizations like the (dimensional) circle notation enable us to represent not only single-qubit but also more complex multiqubit states, entanglement, and quantum algorithms. In this pilot study, we aim to take an initial step toward identifying the contexts in which students benefit from the presentation of visualizations of single- and multiqubit systems in addition to mathematical formalism. For this purpose, we propose a set of test items and a comprehensive methodology to assess students’ performance and cognitive load when solving problems. This is a pilot investigation with a large breadth of questions intended to generate hypotheses and guide larger-scale, more focused studies in the future. Specifically, we compare two approaches: using the mathematical-symbolic Dirac notation alone and using it in combination with the (dimensional) circle notation. In surveys in one-, two- and three-qubit systems, we gather qualitative data from five, five and two think-aloud interviews, identifying problems that students encounter and their problem-solving strategies. In addition, we analyze quantitative data (performance and cognitive load) from 23, 27, and 17 participants in surveys on one-, two-, and three-qubit systems recruited mainly from our quantum computing lectures. We find that most of the test items are appropriate for a heterogeneous target group, as they can differentiate between participants in terms of performance and time taken. In general, the A-B crossover structure of the study is suitable for investigating the benefits of visualization for problem solving with regard to length and feasibility. Future studies should, however, be narrower in scope, given the observable dependence on student characteristics and context, with particular interest lying in the further investigation of the Hadamard gate, the cnot gate, and entanglement in multiqubit systems.

In a data-driven world, the ability to critically assess empirical data is a core aspect of scientific literacy and a major goal of physics education. Yet, students often fall back on heuristics that conflict with statistical reasoning. A common example is the “the more measurements, the better the quality of the data” (MMB) heuristic, which naively equates quantity with quality, ignoring variance. According to conceptual change theory, such heuristics or misconceptions coexist with formal scientific knowledge and are automatically triggered during reasoning, resulting in conflicts and requiring active suppression. Inhibitory control, an executive function that overrides automatic responses, may be the central key to resolving these conflicts; however, its role in procedural domains, such as data literacy, is understudied. This study examines whether and how inhibitory control facilitates students to overcome the MMB heuristic in different representational formats (tabular vs pictorial). Using a modified Stroop paradigm, undergraduate physics students ($N=30$) evaluated data quality presented in tabular (numerical tables) and pictorial (dot plots) formats across the congruent, incongruent, and neutral stimulus types. Results showed significant accuracy reductions in incongruent trials compared to both neutral and congruent trials across both formats, indicating significant Stroop effects. For the response times, we found a significant Stroop effect (longer response time in the incongruent than congruent trials) in the pictorial format, while a similar but non-significant trend in the tabular format. These findings first indicated the involvement of inhibitory control when the heuristic activation occurred in both formats. However, the pictorial format may evoke stronger or more persistent heuristic conflict, or the potential higher cognitive processing demands in the tabular format may mask the subtle RT-based Stroop effects. These findings extend the conceptual change research by identifying inhibitory control as crucial for resolving heuristic interference in procedural reasoning, and they stress the need to consider how representational formats modulate both heuristic activation and cognitive processing demands when designing instruction in physics.