In this paper, the focus has been on students’ application of special relativity theory after learning it. In order to determine students’ understanding and conceptualization of the theory of special relativity, paradoxes, which are contradictory problems, have been used in the study. How students solved paradoxes related to the relativity of simultaneity, length contraction, and time dilation has been investigated, and an answer has been sought to the question “What is the state of undergraduate students who have learnt special relativity theory in applying the theory to paradoxes?” The students who participated in the study had previously attended Physics I, General Physics 2, and General Physics 3 courses in which classical physics topics (force and motion, electricity and magnetism, and optics and waves) were taught. The data were analyzed using a phenomenographic approach to identify the categories emerging from students’ responses. Most students experience difficulty in understanding the nature of special relativity and applying the theory to other situations. For this reason, they did not use the concept of the reference frame in solving the paradoxes. From the students’ responses, it is understood that they mostly think within a single reference frame and experience difficulty in understanding the contradictions that arise when viewed from different reference frames.

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.

The theoretical framework knowledge in pieces (KiP), proposed by Andrea diSessa, holds that intuitive knowledge in physics is organized through small cognitive structures called p-prims (phenomenological primitives). One of the heuristic principles on which the KiP framework is built—the principle of redescription—suggests that the descriptive framework of p-prims can be fine-tuned. However, the theoretical development of p-prim descriptions has remained largely limited to informal linguistic expressions drawn from students’ explanations, without a clearly defined syntactic or semantic structure. This lack of systematicity has led to variations in the naming and conceptualization of p-prims, making their consistent identification and analytical use difficult. In response to this limitation, this paper proposes a formal schematization of p-prims based on Fillmore’s Frame Semantics, supported by tools such as FrameNet. Our proposal aims to make explicit the core and peripheral components of each p-prim and to clarify the conditions under which they are evoked. By providing p-prims with a well-defined linguistic structure, the KiP framework is strengthened, opening new possibilities for the automated analysis of students’ reasoning using artificial intelligence (AI), as well as for the design of visual representations to support the development of instructional materials. Theoretical, methodological, and technological implications of this proposal for future research in physics education research (PER) are also discussed.

This paper presents the first iteration of development and classroom testing of Stars and Elements, a web-based learning resource for Norwegian upper secondary physics students focused on how heavy elements are produced in stars and neutron star mergers. Combining the model of educational reconstruction (MER) with the pedagogical link-making framework and design-based research (DBR), we identified core ideas to be communicated, reviewed the physics curriculum and research on student understanding and motivation, and formulated design hypotheses that guided development of a 90-min learning resource with animations, videos, interactive tasks, peer discussion, and teacher consolidation. Six classes ($N$ = 120) participated; data comprised students’ written responses to three open tasks, brief evaluations, and classroom observations. Most students could describe central concepts and processes concerning red giant stars and neutron star mergers as sites for heavy element production, notably the slow and rapid neutron capture processes, and many benefited from animations and varied task formats. Some displayed uncertain grasp of concepts such as fusion and beta decay. Nature of science (NoS) aspects concerning how physics knowledge is developed were found to be insufficiently represented. From these findings, we propose design principles for research-based learning resources on similar topics, including emphasizing explicit link-making across scales and prior knowledge, scaffolding for challenging nuclear concepts, multimodal visualizations, and explicit NoS focus. We argue that combining MER, link-making, and DBR is a promising approach for developing learning resources that reflect the frontiers of science while having a solid grounding in the classroom.

Learning progressions (LPs) are widely used to describe the long-term development of students’ understanding of core scientific ideas. However, as increasing attention has been paid to the context-dependent nature of student learning, the limitations of traditional LP models in capturing the complexity and dynamics of cognitive development have become more apparent. This study integrates the partial credit model (PCM) and model analysis to systematically investigate students’ cognitive developmental characteristics within the Force and Motion Learning Progression (FM-LP). Using Force Concept Inventory data from 1619 Chinese students, we repeated the study by Fulmer et al. on U.S. students to examine the applicability of the established FM-LP across Chinese student populations. The results indicate that the established FM-LP does not exhibit satisfactory model fit in the Chinese sample, providing further evidence that science learning is highly context- and population-dependent. To characterize students’ intermediate cognitive states, this study introduces a model-state combination analysis framework. The findings show that most students do not stably occupy a single LP level; instead, they display thinking patterns spanning multiple levels, with clear differences observed between middle school and high school students. Further analyses based on PCM reveal that, as students’ ability increases, their model states shift from strongly mixed states to less mixed states and eventually toward the relatively consistent use of the scientific conception. The model-state-based approach offers a new perspective on capturing cognitive complexity in learning progressions and provides important implications for progression-oriented assessment.

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.

The ability of ChatGPT, especially its latest versions, attracts significant attention from educators and researchers in fields such as physics. Several studies on this topic explore the performance of ChatGPT in answering physics questions across different versions of ChatGPT. In this study, we evaluate the development of ChatGPT in answering physics questions using the Mechanical Waves Conceptual Survey (MWCS) to examine whether its capabilities improve across different versions. A total of 75 responses per item are generated using ChatGPT versions 4, 4.o, and 5.2, resulting in 4950 responses for comprehensive analysis. Based on the comparison between the three versions of ChatGPT, we find that some difficult types of questions that are answered incorrectly by previous versions, as reported in earlier studies, can now be answered correctly, although there are still minor mistakes, especially when examining the reasoning. A generalized linear mixed model (GLMM) analysis, together with post hoc comparisons, indicates that ChatGPT 5.2 significantly outperforms both ChatGPT 4 and 4.o, suggesting that the capabilities of the latest version included in this study significantly improve. These findings highlight a future direction for this type of research, as our study shows that the latest version of ChatGPT can correctly answer more types of questions in MWCS.

Physics students’ use of ChatGPT or other generative AI tools is a topic broadly discussed in physics faculties around the globe, with discussion often outpacing evidence. What remains unclear is how physics students truly engage with these tools and by what perspectives their choices are shaped. This study develops a data-driven typology of how students perceive the tool’s usefulness and its risks. Through a qualitative content analysis of 1189 survey responses, followed by latent class analysis (LCA), we identified two distinct user profiles. The majority (70%) are “Pragmatic Users” who, while aware of the tool’s inaccuracies, use it for scaffolding tasks like conceptual clarification and finding starting points for problem-set questions. The minority (30%) are “Skeptical Non-Users,” who avoid the tool over concerns about overreliance and its potential to hinder independent problem-solving. These profiles show students making a calculated trade-off between usefulness and risk, suggesting one-size-fits-all policies on AI are inadequate. Thus, rather than promoting uniform adoption, a differentiated pedagogy could create space for students to make informed, self-determined choices—supporting reflective AI use for those who opt in, while equally validating and enabling the learning practices of those who choose not to use generative AI.

The present study describes the development and field testing of the nature of heat inventory (NOHI) that examines high school and undergraduate students’ understanding of heat transfer processes in the terms of kinetic molecular theory. NOHI comprises 10 items. Based on preliminary study and the history of science, the 30 distractors of NOHI were designed to reflect the following three categories of possible misconceptions regarding heat: (a) materialistic views, (b) hot atoms, and (c) expanding atoms. Field testing, carried out on 81 students for science teaching, showed the materialistic misconceptions of heat still to be the most popular. NOHI has been proven to be a valid and reliable tool that could be used to determine students’ thinking regarding classical heat processes.

This paper is the second in a two-part series that investigates students’ reasoning on astronomical phenomena. In this work, we examined how (if at all) students rely on spatial scale assumptions when reasoning about Moon phases. To that end, we conducted $N=25$ semistructured interviews with last-year high school students. A personalized, physical scale model of the Sun-Earth-Moon system was constructed for every participant individually and placed on the interview table. Students were encouraged to clarify their thoughts by interacting with the scale model or by making a drawing. The scale estimates underpinning the scale model on the table were assessed through an interactive online survey, which was administered several days to weeks prior to the interviews. Only students with severely underestimated values for the relevant sizes and distances were selected for an interview. This selection procedure was motivated by both pragmatic and empirical considerations. We observed a wide variety of explanations for Moon phases, some of which were not found in the existing literature. Several students used their (inaccurate) scale ideas to reason about Moon phases. However, occasionally an interviewed student expressed arguments inconsistent with the scale model on the table. In these cases, scale assumptions seemed to be not taken into account when reasoning about Moon phases. The implications of these findings are discussed.

This paper is the first in a two-part series that investigates students’ reasoning on astronomical phenomena. We interviewed $N=25$ last-year high school students to uncover their reasoning on three observable phenomena, two of which will be discussed in this paper. The first phenomenon was the apparent sizes of the Moon and the Sun (as seen from Earth) and potential causes for their variation. The second phenomenon regarded the event of a solar eclipse and the difference between total and annular eclipses. The interviews were held in light of the students’ understanding of spatial scales in the Earth-Moon-Sun system. Based on their answers on a prior online survey assessing students’ estimates of these scales, a personal to-scale model of the Earth-Moon-Sun system was provided for every student. The students selected for interviews were those who showed a substantial underestimation of these scales. Students were repetitively encouraged to illustrate their reasoning on both phenomena by using the scale model or by providing a drawing. For both discussed astronomical phenomena, we encountered several alternative explanations that—as far as we know—were not previously documented in the research literature. Many of these relied on an inaccurate comprehension of the involved scales. Although for some students these explanations were in line with the scale model on the interview table representing their substantial underestimations, others did not seem to take spatial scales into account when making suggestions on the causes for these astronomical phenomena. The implications of these findings are discussed.

Students interpret grades as signals of their strengths, and grades inform students’ decisions about majors and courses of study. Differences in grades that are not related to learning can impact this judgment and have real-world impact on course-taking and careers. Existing work has examined how an overemphasis on high-stakes exams can create equity gaps where female students and Black, Hispanic, and Native American students earn lower grades than male students and Asian and white students, respectively. Yet, minimal work has examined how the weighting of individual midterm exam scores can also contribute to equity gaps. In this study, we examine how three midterm exam score aggregation methods for final grades affect equity gaps. We collected midterm exam data from approximately 6000 students in an introductory physics course over 6 years at a large, research-intensive university. Using this dataset, we applied common midterm exam score aggregation methods to determine their impact on aggregated midterm exam grades: dropping the lowest midterm exam score, replacing the lowest midterm exam score with the final exam score if higher, and counting the highest midterm exam score more in the final grade calculation than the lowest midterm exam score. We find that dropping the lowest midterm exam score resulted in the largest increase in final grades, with students with lower grades benefiting the most. However, we find no evidence that these alternative midterm exam aggregation methods close equity gaps. Instead, we find some evidence that such practices could increase equity gaps between male and female students, though the effect is very small in relation to the overall grade increase. Implementing the alternative midterm exam aggregation methods examined here may be useful for instructors wanting to raise course grades or give lower-scoring students a boost. However, they do not appear to be effective in reducing equity gaps.

Accurate diagnosis of student misconceptions is crucial for effective science instruction. Traditional diagnostic tools, however, often rely on unreliable self-reported confidence levels. This study developed and validated a two-dimensional diagnostic model that integrates objective eye tracking data with test responses to more robustly identify misconceptions in Newtonian mechanics. The study involved three phases: developing a four-tier diagnostic test, conducting an eye tracking experiment to analyze visual attention patterns (fixation duration, revisits, scan paths) among high school students, and constructing the final model by correlating eye movements with confidence judgments. Consistent with previous research, results showed that students with misconceptions had shorter fixation durations on correct options and displayed inefficient scan paths. Conversely, those with scientific conceptions showed focused attention. As a novel contribution of this study, a “reversed U-shaped” pattern was observed between confidence and revisits, indicating that revisits can serve as an objective measure of cognitive uncertainty. Based on these findings, we propose a two-dimensional “eye tracking-test” model. This model offers a more precise method for diagnosing student understanding, providing a foundation for targeted instructional interventions and fostering a dynamic “teach-learn-assess” feedback loop for personalized physics education.

Peer discussion (PD) is widely used as an active-learning strategy in physics education, yet most prior research has focused on conceptual multiple-choice questions. Much less is known about how peer discussion functions when applied to mathematically structured problems typical of calculus-based mechanics courses. To address this gap, we compared the effects of PD and instructor explanation (IE) during short in-class exercise sessions in a calculus-based introductory mechanics course ($N=87$). Using a cluster-randomized crossover design with two course sections, instruction alternated between PD and IE across weekly exercise topics. After each lesson, students completed a 16-item post-test; 12 of these items were also administered on the final exam. Across the 16 post-test items, we found no statistically significant evidence that PD outperformed IE on any single item. In unadjusted item-level analyses, four items that required greater abstraction and multistep mathematical reasoning showed a trend favoring IE, although none of these differences remained statistically significant after Holm correction for multiple comparisons. On the final exam, no statistically significant differences were observed between conditions. These results suggest that, within the instructional conditions of this study, PD did not produce consistent performance gains relative to IE, and any item-level differences may depend on task characteristics such as mathematical abstraction, novelty, and multistep reasoning demands.

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.

This qualitative study explores how undergraduate women define what it means to be a physics person alongside how they describe their own physics identity. Our team analyzed 120 survey responses (97%, or 116 of the 120 respondents, identified as women or non-binary) to understand what factors are associated with an individual’s physics identity. Those surveyed were attendees of Conferences for Undergraduate Women in Physics, who attended 88 unique institutions in 30 states. Our participants’ conceptions of what it means to be a physics person fell into two themes: (1) people who are skilled academically and in research and (2) those who have an interest in physics ranging from an enjoyable interest to a life-devoting passion for the subject. When asked to explain their own physics identities, students responded through a personal and social lens more frequently than through an academic lens. Our analysis revealed three tensions in the development of undergraduate women’s physics identity: (1) the tension between identifying entirely as a physics person and having other identities that are important to who they are, (2) the tension between the need for belonging in the physics community and their view of themselves as a physics person, and (3) the tension between their expectations of a physics person and their beliefs about their own abilities or interest.

The Disciplinary Learning Companion (DLC) is an online reflection tool designed to enhance students’ engagement in metacognitive activities during physics problem solving. It achieves this through providing reflective discipline- and topic-specific prompts on the solving process. This study focuses on evaluation strategies and aims to understand whether, how, and when students use evaluation strategies during physics problem solving, whether the DLC’s reflective prompts trigger the use of evaluation strategies and the extent to which this use is transferred to future problem solving. A total of twelve first-year bioscience engineering students each participated in four think-aloud interviews over a four weeks period. In these interviews, the students first solved a physics problem and then reflected on it supported by the DLC. This study focuses on students’ use of evaluation activities as part of their metacognitive process. To assess students’ current metacognitive levels, we examined the evaluation strategies they used spontaneously. In addition, we evaluated the impact of the DLC by studying the evaluation strategies students used after being prompted and those transferred to subsequent problem-solving attempts. The analysis revealed that students spontaneously applied evaluation strategies to numerical problems but rarely to symbolic problems. The DLC’s reflective prompts successfully triggered the use of evaluation strategies, though infrequently. Similarly, there were few instances of transferring evaluation strategies from one think-aloud interview to another.

Generative artificial intelligence (GenAI) has shown growing potential in supporting teacher education, yet little is known about how preservice teachers collaborate with AI during instructional design (ID). This study examined the effects of DeepSeek, a Chinese large language model, on preservice physics teachers’ ID performance and cognitive processes. A quasiexperimental design was conducted with 220 participants completing a 45-min ID on Newton’s third law, either with or without DeepSeek assistance. Data from IDs, interaction logs, and interviews were analyzed through statistical tests, epistemic network analysis (ENA), and thematic coding. Results indicated that the DeepSeek-assisted group outperformed the control group in analytical and structural aspects of ID, such as teaching content analysis and learner analysis. ENA revealed that high-performing participants exhibited proactive “problem representation–AI interaction–monitoring” patterns, while low-performing participants relied on reactive prompting. Interviews further confirmed that AI was helpful in contextual introductions and experiment design but also revealed issues such as outdated curriculum references and overly absolute expressions that lacked sensitivity to teaching contexts. The findings suggest that GenAI can serve as a cognitive scaffold when used strategically and highlight the need to integrate AI literacy and metacognitive training into teacher education.

Student experiences in physics beyond the classroom and their role in supporting student development have been the subject of increasing research attention in recent years. Results, typically from small studies at single institutions, have illustrated that facilitating informal physics experiences for nonscientists can enhance student disciplinary identity, learning, sense of belonging, and career skills. However, it is essential to examine whether these impacts are the sole provenance of institutions with well-developed outreach programs or if they may be shared by institutions anywhere. This work reports on the analysis and findings of responses to three open-ended questions presented to students who indicated they had engaged in facilitating outreach programs as part of a national survey distributed through the Society of Physics Students network in spring 2023. Employing a network analysis with Girvan-Newman clusters revealed six core themes of student experiences: community participation, resilience, transformation, audience dialogue, disciplinary development, and disciplinary connectedness. The first four of these clusters were observed to be highly interconnected, providing evidence that the impacts and experiences within them are interrelated with other clusters, particularly interactions with the audience, which is a central feature of informal physics programs. In particular, student experiences highlighted that facilitating informal physics programs enhanced their resilience and belonging, grew their physics identity, provided opportunities to develop essential career skills, and cultivated a growth mindset.

Models are commonly used in astronomy education to help students understand and discuss difficult or abstract concepts. From a physics education research perspective, metaphors and analogies are often used to support reasoning about abstract phenomena. This study investigates how students draw on classroom experiments as physical analogies (a multimodal instance of conceptual metaphor) when reasoning about exoplanetary atmospheres. We conducted two lessons: one about cloud formation on Earth and exoplanets and another about lightning on Earth and exoplanets, both including a hands-on experiment related to the topic. The students were then split into peer-led focus groups and tasked with discussing the experiment and its relation to weather in planetary atmospheres. We audio-recorded these group discussions and extracted relevant excerpts. We analyzed these excerpts sequentially using thematic analysis, network analysis, and conceptual blend analysis. We identified four input spaces used by students (relevant physics, the experiment, Earth conditions, and exoplanet conditions) and three ways in which students use analogies (mapping between domains, identifying limitations, and extending the analogy). Across these analyses, we found that students integrate these input spaces to construct blend spaces that mediate their reasoning about processes and concepts in Earth and exoplanetary weather phenomena. We also discuss implications for future research and teaching.