Papers

We show how software infrastructure that was mainly built to power AI applications also can be used to great benefit for other purposes – by demonstrating a fully self-contained JAX-based black hole raytracer in under 180+200 lines of Python code plus documentation. Major motivations for showcasing such a construction are (a) to show to professional physicists how Machine Learning (ML) accelerators can greatly simplify building numerical applications for GPU accelerator hardware with very modest coding effort and programming expertise – even if the problems at hand do not involve ML per se, (b) to make a meaningful part of General Relativity (GR) accessible to computer science professionals who might not have realized that mathematical background they developed on Deep Learning problems may have brought this theory within easy reach for them, and (c) to inspire students and educators to explore the potential of an approach that represents physical law, i.e. relations between local differential quantities, not in terms of differentiable symbolic expressions, but in terms of differentiable algorithms.

Traditional physics laboratory courses often focus on experiments with well-known results, limiting students' engagement in authentic scientific practices. Course-based undergraduate research experiences (CUREs), where students engage in real research with unknown outcomes, have been shown to support positive student outcomes, such as increased self-efficacy, persistence, and engagement in scientific practices. However, discipline-specific studies of CUREs in physics remain limited. We describe the development, structure, and initial implementation of a photovoltaics-focused CURE in a second-year undergraduate physics laboratory course at the University of Colorado Boulder. To examine how students experienced the course, we analyzed end-of-semester reflection assignments using the five CURE components (i.e., scientific practices, discovery, relevance, collaboration, and iteration), as well as established dimensions of research authenticity, as analytic frameworks. Students described experiences associated with all five CURE components, with collaboration, relevance, and scientific practices appearing most prominently in their reflections. Students also associated authentic research with meaningful scientific contribution, engagement in authentic scientific practices, and navigating the uncertainty and setbacks inherent in research, although fewer explicitly identified themselves as researchers or scientists. A subset of students additionally connected the course to their immediate thinking about future academic and professional pathways. This work contributes both a discipline-specific model for implementing CUREs in experimental physics laboratory courses and provides insight into how students interpret and experience authentic research within this course context.

In order to explain an elastic collision, Johann Bernoulli considered two bodies connected by a spring. Motion is defined as a succession of states of rest. Then, considering the spring to be a lever with a body at each extremity, the laws of equilibrium imply that `motion' is described by the time variation of the (common) quantity of motion of the bodies; the dynamic law is thus deduced. This inspires a teaching sequence to introduce the dynamic law (in one dimension) in introductory physics course; we call it “bernoullian sequence”.

Generative AI tools are being widely taken up by students in their physics courses and beyond, often before instructors and institutions can develop policies and effective approaches for the use of these tools. Building on a framework for change in the era of AI, we developed and implemented a faculty learning community to help a university physics department address these challenges collectively. Over six biweekly sessions, faculty worked through course policies, classroom conversations about AI, AI-integrated coursework tasks, and assessment. Each session shared a common structure: we presented local data and department-sourced materials, tested them in small groups, and discussed them together, emphasizing durable pedagogical approaches over specific tools and platforms, and leading with evidence of students' own AI use. The series produced a shared, evolving repository of resources for faculty to draw on. This workshop provides an adaptable, theoretically informed model for a faculty learning community that departments can build on.

Generative artificial intelligence (Gen AI) is increasingly shaping how students access information and engage with physics content, yet many instructional approaches still assume a pre-AI classroom. In this paper, we describe a practical framework for integrating Gen AI into physics instruction through the use of context prompts: instructor-designed prompts that define the role, task, and boundaries of the AI interaction. We focus on AI-supported exploration assignments implemented in an asynchronous introductory physics course enrolling approximately 70 students. In these activities, students used AI as a guided tutor to explore selected topics, ask follow-up questions, request explanations and analogies, and connect physics concepts to their personal interests and everyday experiences. We argue that this structure can help move students beyond answer-seeking and toward more dialogic and personally meaningful engagement with course content. We also discuss reflection as a natural extension of the same framework, while distinguishing it from the implemented exploration activities described here. Finally, we highlight practical considerations that emerged in use, including uneven access to AI platforms, grading workload, the need to teach students how to engage AI conversationally, and the growing importance of AI literacy in physics education.

The incorporation of AI into the assessment of physics laboratory reports offers opportunities to support the review of student work and the provision of feedback. This article revisits an experience involving the use of ChatGPT models to analyze laboratory reports and examines the conditions that influence their usefulness in teaching practice. The analysis considers the evolution of AI models, document-processing procedures, and different modes of interaction, with particular attention to the effective availability of evidence contained in student reports. The findings indicate that differences between AI-generated and teacher assessments may be related not only to model capabilities but also to difficulties in retrieving and interpreting equations, graphs, tables, units, and other visual elements. The possibilities of systematic processing of sets of reports and conversational review of cases requiring more focused analysis are also examined. Based on these observations, a working strategy is proposed in which AI serves as a complementary resource for organizing assessment, supporting feedback, and identifying recurring difficulties in student work. The analysis highlights the importance of document-processing conditions and evidence availability, as well as the teacher's role in interpreting AI-generated information and making evaluative and pedagogical decisions.

Learning is a social act and requires communication between interlocutors. It is a process that takes place in a socially constructed neurotypical-normative system. These neurotypical-normative systems can marginalize neurodivergent students, who oftentimes have non-normative socio-cognitive differences. We investigate 3 neurodivergent physics students' experiences solving physics problems and navigating physics culture. We use a Double-Empathy Problem lens to uncover how communication breakdowns can manifest for neurodivergent students in physics. We find that the 3 neurodivergent students experience communication breakdowns in their interactions with peers and instructors and in interpreting problem statements. Furthermore, we find evidence that neurotypical-normative communication expectations in physics culture could be the source of negative effects present in participants' interviews, such as participants' fear of being misinterpreted or feeling as though they are viewed as stupid.

The recent emergence of large language models (LLMs) has produced research focusing on the ability of these tools to solve physics problems, evaluate student work, or otherwise impact the problem-solving process of students. However, studies exploring how physics students perceive LLMs (in terms of capabilities, educational impacts, usage, and role in problem solving) remain limited. This study evaluates the first-year physics students' perceptions toward LLMs and further explores how these perceptions change after practicing problem-solving with and without LLMs and engaging in a reflective lesson on the functioning and educational impacts of LLMs. We find that student opinions toward LLMs vary, with generally favorable perceptions of their capabilities but greater skepticism regarding their value for learning. Despite this skepticism, a majority of students self-report regularly using LLMs to obtain help, commonly reporting deadlines and convenience as motivating factors. Following the lesson, students expressed greater skepticism toward LLMs in several areas, with 88% of students believing that LLMs can leave them with a false sense of confidence about their understanding, up from 58% before the lesson.

Historical post-16 data for UK A-Level Physics, Maths and Further Maths are analysed to determine the relative popularity of A-Level exam boards. A curriculum mapping of the exam board specifications is implemented and compared against first year syllabi of undergraduate physics courses at high-ranked UK universities. Gaps in curricula are identified and recommendations are made to both university module designers and A-level exam board curriculum setters, with regards to remedial action and a reconsideration of included content.

The problem of a crewed mission to Mars provides a compelling and realistic context for teaching introductory physics. In this work, we present a coherent set of calculations describing a round-trip journey between Earth and Mars, using only mathematical tools accessible at the high-school level. The treatment integrates key topics that are typically taught separately -such as Newtonian gravitation, energy, circular motion, and basic astrodynamics- into a single coherent framework. The analysis includes Hohmann transfer orbits, travel times, launch conditions, and mission constraints, illustrating how a complex real-world problem can be transformed into an effective classroom resource. This work aims to support teachers and students in exploring interplanetary travel as an accessible and motivating application of fundamental physics.

This work explores the use of generative artificial intelligence (AI) as a source of synthetic experimental content for introductory physics courses in engineering education. Using PixVerse.ai, Grok Imagine, and Pippit, three video scenarios were generated to represent distinct resistive force regimes: constant friction, linear drag, and quadratic drag. Kinematic data were extracted from the generated videos using Tracker, an open-source video analysis tool, and subsequently fitted to the corresponding analytical models through non-linear least-squares regression in Microsoft Excel. The results show good agreement between the synthetic data and the classical kinematic equations derived from Newton's Second Law. From the fitted parameters, physically meaningful quantities were recovered in each case, with values broadly consistent with those reported in the literature under the assumed conditions. A recurring observation is that the physical plausibility of the generated motion depends on the level of detail included in the text description used to generate the videos. More specific descriptions tend to produce more coherent dynamical behaviour, suggesting that the formulation of input prompts plays a relevant role in shaping the resulting physical consistency and can be regarded as an integral component of the experimental design process. The integration of generative AI, video-based motion tracking, and curve fitting provides a complete workflow that mirrors key stages of experimental practice, from model construction to quantitative validation. The proposed methodology engages students in experimental design, data acquisition, parameter estimation, and model evaluation, promoting key engineering competencies in modelling of physical models using widely available tools. Overall, the proposed methodology demonstrates the potential of generative AI to support physics education

Astronomy students are often asked to reason about systems that cannot be handled in a laboratory, evolve over inaccessible timescales, or reveal themselves only through indirect measurements. Gravitas is a free, browser-based astrophysics environment designed to turn those limitations into opportunities for experimentation. Its version 1.0.0 release combines a configurable gravitational sandbox with 59 ready-to-run scenarios and 22 guided investigations comprising 636 steps and roughly 15-19 hours of structured work. Students can build and alter systems, change reference frames, measure distances, angles, periods, energies, forces, and orbital elements, and observe simulated systems through transit, radial-velocity, astrometric, rotation-curve, stellar-evolution, and gravitational-wave displays. Guided activities use a predict-test-measure-revise-explain cycle, retain the student's evidence, and export a report for submission. Instructor guides, answer keys, short classroom routes, lecture and embed modes, reproducible share links, Spanish localization, keyboard-accessible navigation, and offline operation lower the practical barrier to adopting the simulation in introductory astronomy and physics courses. A public dashboard reports 243 scientific checks across 25 areas and names the sources and tolerances behind them. This article describes the learning experiences available in Gravitas, illustrates several ways it can be used from a brief demonstration to a full laboratory exercise, and states the scientific scope and limitations of the models. The aim is not to replace quantitative calculation or observation, but to give students a place where they can make a prediction, change one thing, measure what follows, and build an explanation from their own evidence.

Physics Education has played a prominent role in advancing physics education research since its establishment in 1966. However, a comprehensive investigation of its publication trends, impact, and thematic evolution over six decades remains limited. This study examines the journal development through a bibliometric analysis of publications over six decades. Following a PRISMA-based screening process, 6,311 documents were retained from an initial dataset of 7,709 records. Publication trends, citation impact, leading countries and institutions, topic modeling, and topic evolution were analyzed to examine the journal's development and thematic structure. The results indicate sustained growth in publication output, significantly after the digital and information era. The UK and the US emerged as the most productive contributors and accumulated the highest total citations, whereas Germany and Norway achieved the highest citation impact in terms of citations per paper. This pattern was further reinforced by increasing contributions from countries in Asia and the Global South, reflecting the growing internationalization of the journal. Major research themes include force and motion experiments, energy and light experiments, laboratory-based learning, conceptual understanding, and physics teacher education. Topic prevalence analysis indicates that, while experimental and laboratory-based studies have remained a defining feature of Physics Education, increasing attention has been devoted to conceptual understanding, assessment, technology-supported instruction, and pedagogical innovation in recent decades. Overall, Physics Education has evolved from a journal primarily centered on laboratory experiments and instructional demonstrations into a multidisciplinary venue emphasizing conceptual understanding, educational technology, and contemporary innovations in physics teaching and learning.

We present an analytical and experimental study of a variable-mass oscillator with constant mass loss. Starting from the equation of motion of a spring-mass system whose mass decreases linearly with time, analytical solutions are obtained in terms of Bessel functions. Simple asymptotic expressions are then derived, providing explicit descriptions of the evolution of the oscillation amplitude and frequency. The analysis is extended to include viscous damping, leading to a model suitable for direct comparison with experimental observations. Experimental measurements of the oscillator acceleration were performed using a simple laboratory setup consisting of a spring-mounted container that continuously loses mass at an approximately constant rate. The theoretical predictions accurately reproduce the measured dynamics over the entire duration of the oscillation, and the massloss rates obtained from the fits are in good agreement with independent measurements. The results provide an accessible example of variable-mass dynamics and illustrate how special-function methods can yield experimentally testable predictions in advanced undergraduate mechanics.

Many of the students enrolled in the calculus-based physics sequence are pursuing engineering degrees. Lab activities that have a direct connection to engineering, analyzed with the physics they have been studying, can grab students' interest and provide a real-world connection. All the better if the equipment is inexpensive and partly self-built. In addition, we wanted to use smartphones as data collection devices. Smartphones have become valuable tools for physics education because they provide an accessible and inexpensive means of experimental data acquisition through their built-in sensors. Several free applications, such as Physics Toolbox and phyphox, facilitate sensor-based data collection and analysis, making it possible to perform meaningful laboratory activities without expensive equipment. We chose to use a simple crank-slider mechanism for our activity. The crank-slider converts rotary motion into linear motion and has common applications in such things as internal combustion engines and compressors. Educational studies have shown that slider-crank mechanisms and related mechanical devices provide effective contexts for introducing engineering concepts and connecting theoretical models with practical applications. In our implementation, a rotary motor drives a disk connected by a rod to a cart moving along an air track. Students investigate the resulting motion using smartphone data acquisition and video analysis. Experimental measurements are then compared with the predictions of a mathematical model derived from the geometry of the system.

This study investigates the influence of frame rate, expressed in frames per second (FPS), on the experimental estimation of the acceleration due to gravity, g, using video analysis with Tracker. Two different dynamical systems were analysed: free fall and a simple pendulum. Starting from original recordings acquired at 120 FPS, equivalent 60, 30, and 10 FPS series were generated by uniform temporal decimation, preserving the same physical trajectory within each realisation. The analysis included ten independent realisations for free fall and eight for the pendulum. For each condition, the mean estimate of g, the standard deviation across realisations, the relative error with respect to g_ref = 9.81 m s^-2, and the RMSE were calculated. For free fall, the 120 FPS condition exhibited the lowest dispersion and RMSE, whereas the mean estimates obtained at 10 and 30 FPS were closer to the reference value. For the simple pendulum, by contrast, the mean value of g, dispersion, relative error, and RMSE remained essentially unchanged between 10 and 120 FPS. An additional analysis of the 12 possible subsampling phases at 10 FPS showed that this stability does not critically depend on a particular choice of decimation phase and that the variability associated with phase is small compared with the dispersion observed across realisations. The results show that a higher temporal sampling rate does not necessarily provide both greater precision and closer agreement with the reference value. The influence of FPS depends on the temporal characteristics of the system and on the procedure used to estimate the physical parameter. In particular, a higher temporal sampling density may reduce dispersion or increase robustness to sampling without necessarily yielding an estimate closer to the expected value.

We present a new version of the classic specific heat of water experiment that emphasizes scaling analysis and data collapse as tools for model construction in contrast to the usual approach of using a previously-established formula. The activity combines structured experimental and analytical guidance with instructor-mediated questioning; the corresponding thermodynamic theory is deliberately postponed. Students collect temperature-time data under various conditions, producing multiple data sets that initially appear unrelated. Through successive rescalings, they reduce the dimensionality of the variable space and achieve data collapse onto a single master curve, from which they formulate an empirical model relating energy input, mass, and temperature change. That the effect of the calorimeter cannot be eliminated in the analysis leads to a non-identifiability of the subsystem contributions. The conventional thermodynamic description is introduced a posteriori as a boundary-setting framework for interpreting the empirical model and clarifying its applicability. The central contribution of this work is to use data-driven modeling both to construct models and to reveal their limitations.

The Force Concept Inventory (FCI) is one of the most widely used research-based assessments in physics education, yet the assumption that its underlying cognitive structure is transferable across educational contexts remains largely untested. This study investigates the transferability of FCI Q-matrices using the Generalized Deterministic Inputs, Noisy "And" Gate (G-DINA) cognitive diagnostic applied to two large cohorts: students from the Learning About STEM Student Outcomes (LASSO) online system database in the United States (N = 4,750) and introductory physics students at the University of Johannesburg, South Africa (N = 1,016). Rather than treating the analysis as a local model calibration exercise, we frame the problem as one of cross-context cognitive invariance. Differential Item Functioning (DIF) analyses revealed substantial cross-cultural differences, with 14 of 30 items exhibiting high DIF after controlling for latent skill mastery. These differences were concentrated in force dynamics and contact-force reasoning and remained invariant under alternative Q-matrix specifications. The findings suggest that observed differences reflect genuine variations in students' conceptual reasoning rather than psychometric artifacts, highlighting the importance of validating Q-matrix structures before deploying cognitive diagnostic and adaptive assessments across diverse non-local educational settings.

Conceptual assessments are widely used in physics education research to evaluate changes in student understanding, yet class average measures can obscure how those changes are distributed across students with different levels of initial achievement. We introduce a Learning Contribution Framework that characterizes this distribution through the Learning Contribution Curve (LCC) and Learning Contribution Profile (LCP). For a general contribution measure G, the LCC represents cumulative contribution across students ranked by initial achievement, whereas the LCP describes the local mean contribution relative to the population mean of the individual G values. We apply the framework to the individual Hake normalized gain and develop a statistical model linking LCC and LCP behavior to the joint structure of pretest and posttest scores. Under the central assumption that the conditional mean of the subsequent score is linear in the initial score, we identify a score structure parameter $\beta$ and a critical score structure parameter $\beta_c$. Whether $\beta$ is greater than, less than, or equal to $\beta_c$ determines whether the expected LCP increases, decreases, or remains constant across initial achievement. Given the pretest distribution, the model further yields analytical predictions for the complete LCP and LCC that closely reproduce simulation results. Application to classroom concept-assessment data illustrates how the framework reveals local and cumulative patterns of learning contribution that are not evident from an overall class average revealed by the traditional Hake's normalized gain.

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

We present GW Explorer: A Beginner's Guide, an outreach curriculum designed to introduce high school students to gravitational-wave (GW) astrophysics through interactive Python Jupyter notebooks. Most existing GW resources target beginning audiences and advanced students, leaving a gap at the pre-college level that we directly address. The curriculum integrates foundational physics with hands-on computation implemented through both self-directed and workshop-based instructional formats. In the self-directed format, students completed the curriculum independently on cloud-based platforms such as Google Colab. During the workshop format, students worked through the same activities under the guidance of University of Nevada, Las Vegas graduate student mentors. Topics span gravity, spacetime, GW sources, interferometric detection, and data analysis. An implementation in local high school classrooms informed the content and pacing, and survey results demonstrate gains in conceptual understanding and coding confidence. GW Explorer offers a scalable, open-access framework for authentic astrophysics research in the high school classroom.

Physics courses use research-based assessments (RBAs) such as the Force Concept Inventory (FCI), Force and Motion Conceptual Evaluation (FMCE), and Energy and Momentum Conceptual Survey (EMCS) to measure learning in introductory mechanics, but their fixed-length, pretest-posttest design makes them retrospective: posttest scores summarize completed instruction and arrive after a course ends. We are developing the Mechanics Cognitive Diagnostic (MCD), a cognitive diagnostic computerized adaptive test that reports students' mastery of fine-grained learning objectives (LOs) throughout instruction. Using evidence-centered design, we defined 14 LOs from introductory mechanics textbooks and AP Physics standards, mapped FCI, FMCE, and EMCS items onto them with a Q-matrix, and refined the mapping with the deterministic inputs, noisy "and" gate (DINA) model, using posttest responses from 24,394 students in 807 courses across 79 institutions through LASSO. The FCI and EMCS achieved good DINA model fit; the FMCE showed marginal fit. Classification accuracy for most LOs met or exceeded benchmarks for low-stakes formative assessment. RBA items, though not developed for LO-level diagnosis, support it reliably, giving the MCD a working 14-LO item bank built from RBAs that physics courses already use. As data accumulate, we can revise or retire weak LOs and items and add new items through online calibration without interrupting testing. We plan to expand the MCD to 35 LOs, two per week, to cover a typical introductory mechanics course.

Climate change poses a major scientific, environmental, and educational challenge, requiring approaches that connect physical concepts, empirical evidence, and socially relevant environmental issues. This study presents and analyzes a low-cost experimental investigation developed within the "Physics and the Environment" course of a Physics teacher training program at the State University of the Tocantina Region of Maranhão (UEMASUL), Brazil. The experiment involved constructing a simplified thermal simulator using accessible materials and comparing a confined system with an open control system, both exposed simultaneously to solar radiation. The results showed that the average temperature inside the simulator was $45.91^{\circ}\mathrm{C}$, compared to $38.58^{\circ}\mathrm{C}$ in the external control system, corresponding to a mean temperature difference of $7.33^{\circ}\mathrm{C}$ (among other analyses). These results demonstrate that the experimental setup was capable of producing and measuring a significant thermal difference between the two systems. However, the observed temperature rise should not be interpreted as a direct measure of the atmospheric greenhouse effect, given that the experimental apparatus involves both radiative and non-radiative heat transfer mechanisms. The study emphasizes the educational potential of low-cost experimentation as a strategy to integrate electromagnetic radiation, energy balance, temperature, heat transfer, and climate science into the initial training of physics teachers. It also highlights the importance of explicitly discussing the limitations of experimental models and distinguishing local experimental evidence from conclusions regarding global climate processes.

In introductory physics laboratories, a central instructional goal is to help students construct, evaluate, and revise mathematical representations from experimental data rather than apply given formulas. We present a guided data-driven activity in which an RC circuit is treated as a black box defined through observable input-output relations. Experimental and graphical procedures are provided, while standard RC circuit theory is initially withheld. Students first examine discharging data obtained under different nominal values of R and C. They rescale time using T=t/RC and compare the normalized voltage v=V/E across circuit conditions, bringing the discharging data closer to a common trajectory. Graphical linearization through a plot of lnv versus T provides a basis for proposing an empirical representation and allows RC to be interpreted as a time scale rather than being assigned that meaning a priori. When the representation proposed for discharging is tested with charging data, logarithmic linearity is lost despite the continued organization of the data by T. Replacing v with the distance from the steady state, 1-v, restores linearity, and the resulting linear relation provides a basis for proposing a revised representation for charging. The activity thus uses a familiar physical system to make representation construction, breakdown, and reconstruction experimentally explicit. It also emphasizes that an empirical representation must be accompanied by a specified domain of validity and that standard circuit theory can be introduced a posteriori to interpret the physical meanings that emerge from representational constraints.

Generative AI can produce perfect solutions to standard physics homework, weakening the connection between submitted work and knowledge a student can use independently. We report a redesign of the introductory nuclear and particle physics course at Ruhr University Bochum, in which AI was permitted and encouraged alongside traditional tools on unusually difficult, research-shaped assignments. We found that most students remained engaged with the assignments throughout the course and produced ambitious work, while the course did not reliably secure its foundational objective. We offer this mixed experience as a reference point for discussion.

Generative AI and coding agents can accelerate research software development, but they also increase the need for efficient software discovery and maintenance. We developed a repository catalog during a three-day hackathon and subsequently examined the engineering required to make it suitable for public deployment, including adversarial review, data-quality checks, browser-level validation, and publication safeguards. We then explored whether the lessons learned from this prototype could be transferred to a much larger, human-curated portal, through a retrieval agent under development for MateriApps that combines curated portal metadata, external documentation, vector search, and local language-model generation. Implementation with coding agents was rapid, but achieving reliable operation required substantial additional engineering: the most consequential problems were not crashes but silent failures that produced plausible yet incomplete or incorrect outputs, arising from incomplete data acquisition, misleading assessments, and retrieval or preprocessing failures. These observations suggest that AI-assisted software portals require explicit validation, monitoring, and repeated review, and that curated metadata and maintained documentation remain essential. The MateriApps work is exploratory and remains under active development, so the observations reported for it are preliminary; a comparable combination of curated metadata, automatically collected documentation, and retrieval-based assistance may nevertheless be useful for extending other research-software portals.

When alternating current flows through a conductor, induced internal eddy currents suppress current flow in the core and restrict it to a thin surface layer. Although this effect, known as the skin effect, is covered in virtually every undergraduate electrodynamics course, it is rarely explored experimentally without specialized and expensive equipment, and is sometimes even considered a mere high-frequency phenomenon. Here, we present a readily accessible, quantitative demonstration of the skin effect at line frequency using only standard laboratory supplies: an AC/DC power supply, basic multimeters, and laboratory support rods as cylindrical conductors. Based on a precise four-point Kelvin sensing scheme capable of resolving milliohm-range resistances, we exploit relative magnetic permeability rather than high frequency as the governing parameter to control skin depth. For non-magnetic stainless steel, line frequency represents an electrodynamically low frequency, resulting in identical AC and DC behaviour; whereas for ferromagnetic structural steel, high permeability compresses the skin depth to the millimeter scale, causing a sharp surge in AC impedance. This produces a counterintuitive outcome where the statically superior conductor suffers a marked relative impedance increase, whereas the poorer conductor remains unaffected, challenging common student expectations and catalyzing cognitive activation. Depending on the instructional setting, the approach can be implemented either as a quick lecture demonstration or as a quantitative experiment within an undergraduate laboratory course.

Even in the era of modern physics, understanding and applying Newton's classical Laws of Motion are key skills for physicists and form a core component of instructional programmes. Students arrive in the classroom with intuitive ideas about force and motion that are often misaligned with Newtonian thinking. Prior research has shown that these incorrect intuitive ideas are remarkably persistent, and can stay with students throughout their education and beyond. Reliable instruments, that can accurately evaluate student understanding of Newtonian mechanics, are required to track student progress and evaluate the efficacy of instructional programmes. The Force Concept Inventory (FCI), a conceptual quiz composed of multiple-choice questions, is a well-established instrument, developed to address these challenges. The FCI, and how students respond to its questions, has been widely investigated and discussed in the literature. Notably, research has shown that: correct responses to questions do not always correspond to correct application of Newtonian concepts; and the limited response options given for multiple-choice questions often do not capture the full range of student thinking. There is therefore a burgeoning need to develop new instruments that probe students' understanding at a deeper level, by interrogating conceptual understanding from multiple perspectives and incorporating different question formats that require students to construct and consider their responses. We introduce the Newtonian Mechanics Quiz (NMQ), a modified version of the FCI that incorporates follow-up questions in a variety of formats, each with different diagnostic functions. This article describes the development and validation of the NMQ instrument, and illustrates several novel analyses enabled by the student response dataset that can offer deep insight into students' conceptual understanding of Newton's Laws.

Concept inventories are widely used in Physics Education Research to uncover student misconceptions and measure the effectiveness of instruction. Two questions in the widely used multiple-choice Force Concept Inventory concern a car that is pushing a truck, as the car is accelerating and then when a steady speed has been reached. We investigate the premise that correct answering of these questions is because of good conceptual understanding of Newton's Third Law, by analyzing responses to a modified inventory that randomly offers students multiple-choice or free-text versions of the questions and follows these with questions in which students select the physical principle(s) they used. The results from 674 attempts from students at six universities show a similar pattern of results for multiple-choice and free-text versions, also comparable with Force Concept Inventory results from one of the universities in the preceding five years, but with marked differences relative to early published Force Concept Inventory data. Free-text responses and the law-selection sub-questions can reveal more about conceptual understanding than can be seen in multiple-choice responses alone. For the accelerating system (Question 15), correct responses are usually attributed to Newton's Third Law and incorrect responses are usually attributed to Newton's Second Law. When speed is constant (Question 16), around 90% of responses are correct, but a significant number of these are attributed to Newton's First Law or the Superposition Principle rather than Newton's Third Law, especially pre-instruction. We conclude that students are confusing balanced forces on a single object moving at constant speed with the correct equal-and-opposite interaction pair between two objects, and correct responses to Question 16 may be concealing an underlying misconception.

Magnetic levitation is one of the most captivating phenomena in physics, combining a striking visual appeal with the rich underlying physics of forces and interactions in equilibrium. Bringing it into undergraduate laboratories in a quantitative way is nevertheless often hindered by the intrinsic instability of static magnetic systems and by the high cost of active control systems. Here we present a simple, safe, and very low-cost experiment that uses ring-shaped ferrite magnets together with a mechanical stabilization system fabricated by 3D printing in a male–female configuration. The apparatus lets undergraduate students explore directly and quantitatively the relationship between the magnitude of the magnetic force and the separation distance. We assess the robustness of the device through experimental tests, whose results show excellent quantitative agreement with the equivalent current-loop model. Furthermore, we validate this model through a complementary, independent first-principles-based methodology based on axial magnetic field profiling. To support reproducibility, the 3D design files are made available in a public, open-access repository.