Scientific knowledge does not necessarily replace intuitive misconceptions; successful physics reasoning may therefore require learners to control responses that remain cognitively available. However, inhibitory-control accounts have yet to explain whether the same conceptual conflict elicits a stable amount of control across representational contexts or whether control mobilization varies with contextual typicality and physics training. This study addressed this issue using a negative-priming (NP) paradigm in the domain of static friction. High-school students and undergraduate physics majors made static-friction judgments in a prototypical context and a nonprototypical context. Within the inhibitory account adopted here, NP magnitude was interpreted as a behavioral index of the control mobilized during the preceding incongruent prime. Probe-phase reaction times were analyzed on the log scale using a linear mixed-effects model with Group, Context, and Prime Type and their interactions. The analysis revealed a context-dependent pattern of NP, with the magnitude of this contextual modulation differing between the two groups. Specifically, NP was negligible in the prototypical context for both high-school students and physics majors. By contrast, reliable NP emerged in the nonprototypical context. This increase was substantially larger among high-school students than among physics majors. When translated back to the original reaction-time scale, NP in the nonprototypical context corresponded to an approximately 20.77% increase for high-school students, compared with a 5.65% increase for physics majors. Taken together, these findings show that the inhibitory control mobilized by the same conceptual conflict is not fixed across representations. The nonprototypical representation amplified inhibitory-control demand, whereas physics training attenuated this amplification. The training-related advantage is therefore better characterized as reduced contextual sensitivity of inhibitory mobilization than as a context-invariant absence of conceptual conflict. The results advance process-based accounts of conceptual change by linking problem representation, domain knowledge, and task-evoked inhibitory control.

In a previous quantitative retrospective study, we showed that different demographic groups of students leave different numbers of problems blank on physics exams, leading to inequities in course outcomes. In that work, we argued that there were good reasons to treat these blanks as missing data, rather than indicators of a lack of understanding. In this paper, we refine this analysis and show more detailed breakdowns of uncollected test item responses by race and ethnicity and first-generation college student status, coming to the same conclusion: test item responses are uncollected for students with different ethnic and racial backgrounds at different rates, and these patterns are not exclusive to low-performing students. We also correct an error from our previous work, finding here that there is no significant gender difference in uncollected test item responses. Finally, we provide a more robust analysis of course level data illustrating that blanks are a variable controlled at the course level rather than the student level, providing more evidence for the use of a course deficit model (rather than a student deficit model) when examining equity disparities, and also suggesting that there are plausible means for instructors to minimize uncollected test item responses, and therefore reduce or even eliminate the bias associated with this missing data. We provide a couple suggestions for faculty who want to minimize the impacts of blanks.

The controlled-not (cnot) gate is essential to algorithms in quantum computing for its ability to entangle qubits. As such, it is important to understand how students learning quantum computing reason around the function and use of this critical quantum gate. To investigate this, we conducted think-aloud interviews in which students solved problems involving the cnot gate to understand students’ “cnot toolbox”—the strategies and cognitive resources students use when reasoning about the effect of the cnot gate. We identify three cognitive resources related to the cnot gate: (1) the procedural resource of applying cnot to specific states, (2) a qualitative description of cnot’s effect on the target qubit given the control qubit, and (3) the idea that the control qubit is not changed when cnot is applied to computational basis states. We find that students’ use of the first resource is foundational to their understanding of the second and third, that the second and third resources can sometimes lead students to incorrect conclusions, and that students can use each of these resources separately or in tandem. We also explore how students use these resources in conjunction with Dirac notation, superposition states, and entanglement to reason both productively and unproductively about quantum computing problems.

Despite daily observations of the Sun, many students continue to struggle with the mechanisms explaining its apparent motion. Even after formal instruction, students often develop only fragmentary understandings and construct alternative mental models. This study investigates these models in depth to gain genuine insight into the knowledge structures students develop regarding the Sun’s apparent motion. We conducted individual task-based think-aloud interviews with 16–18-year-olds in which they articulated their mental models verbally, through drawings and by using three-dimensional materials. These explanations were then analyzed to determine whether the mental models included the essential elements, characteristics, and connections consistent with scientific understanding. The analysis showed that students often described individual aspects of the phenomenon correctly, but rarely provided adequate allocentric explanations. Missing components in their models were frequently supplemented with alternative ideas that clustered around five themes: cardinal directions, distance, orientation of Earth’s axis, lines of sight and sunrays, and the orientation of the Sun’s orbital plane. These themes, the variety within each, and the implications of our findings are discussed in detail.

Motivated by the question of how the design of online team-working activities affects learning experiences, we present an analysis of three such activities studied by advanced undergraduate and postgraduate distance learning students in astrophysics and space sciences. The activities incorporate periods of team-working ranging from 1 to 9 weeks and have varied assessment strategies. Our study focuses on participation rates, the nature of student interactions, and the reported student experience and is informed by the content of asynchronous discussion forums and interviews with students. We find that variation in student participation differs between the three activities, and we suggest reasons for this related to activity design. We identify major themes of student interaction, these being related to learning and development, group building, and team self-organization. The incidence of communications in these thematic areas appears similar across all three activities. Our analysis framework includes subcategories of these themes, and interactions indicative of peer learning are found to be a major component of student communications. Such interactions are enhanced at the times at which teams are required to make major decisions to progress their joint work. Interviews with students reveal anxieties about participation in team-work and unequal contributions to group outputs. They also present a range of views about the different assessment models used. Overall, interviewees from all three activities report having positive experiences in their teams and that they see the activities as valuable for their individual learning as well as being useful preparation for future employment.

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.