4,664 papers in this slice of arXiv.
Nikhil Sanjay Borse, Amir Bralin, Sean Savage +1
This study examines Artificial Intelligence (AI)-generated physics solutions from two connected perspectives: how prompt design shapes these solutions and how students can be prepared to critique them. Using a rotational-mechanics problem, we adapted a problem-classification framework to examine prompt variations, evaluating OpenAI's o4-mini responses with the Minnesota Assessment of Problem Solving (MAPS) rubric. Well-specified prompts improved solution completeness; underspecified and multimodal prompts exposed weaknesses in physics reasoning and correctness. In the student-evaluation phase, 24 introductory physics lab groups evaluated an o4-mini solution to this problem after either independently solving a related problem or critiquing its AI-generated solution with MAPS-based reflection questions. Problem-solving-only groups exhibited uncritical or misconception-based critiques; MAPS-guided groups identified more expert-aligned issues, including skipped numerical procedures and undefined notation. Together, our findings contribute to physics education research by showing how AI-generated solutions can ground both model-reasoning benchmarks and improved student critique of that reasoning through MAPS-based reflection.
Lindsay Dawson, N. Sanjay Rebello
Problem posing, a pedagogical practice that asks students to generate novel problems or meaningful variations to problems encountered, supports transfer of learning and strengthens problem-solving skills in physics. However, generating physics problems can be challenging, particularly for novice learners. This study investigates students' perceptions of an approach to facilitate their use of Generative AI in ways that maximize their benefits and limit risks. Students were introduced to Generative AI-assisted problem posing as a self-study technique. This study utilizes a phenomenological approach to investigate students' perceptions on how training shaped AI interactions, attitudes towards problem posing with Generative AI, and how students view its incorporation into personal study practices. Results of this study suggest that students perceived a positive change in their interactions with Generative AI after receiving training on prompt engineering techniques. This study also reveals that students hold generally positive views towards the problem-posing technique, with a smaller subset of students showing hesitations towards using Generative AI. These results lay the foundation to introduce and employ training methods for Generative AI more widely and to continue to incorporate Generative AI into structured study techniques, like problem posing.
Dan-Adrian German, Rebekah Randall, Charles Pope +2
The convergence of AI and quantum computing requires a new approach to cybersecurity. Credible experts estimate that by 2030 a cryptographically relevant quantum computer will be capable of breaking the encryption that underpins all of our digital communication. One of the most disruptive technology in history is coming and it's expected to change everything. In this context academic institutions will contribute to their states' future economic prosperity by leveraging their strengths and resources, particularly talent development, in key and emerging areas prioritized across the state. We report on current efforts to develop a Pathways Plus to Quantum Information Science (QIS) degree, a set of dual credit courses for high-school (HS) students and incoming college students pursuing a minor or specialization in Quantum Computing (QC). As of today, in the US, quantum topics appear on the HS curriculum standards in just two states: OH (Computing) and TX (Physics). Reaching out to HS and even middle school students (and their teachers) presents obvious long-term benefits in terms of workforce development for the quantum industrial ecosystem. Standing in the way of successful, widespread introduction of QC and QIS topics in HS and middle school are three distinct obstacles: (a) lack of materials at the right level for students and instructors, (b) funding and support for professional development for teachers, and (c) lack of state standards. In this paper we address all three aspects with a special focus on the benefits of quantum virtual labs and ZX calculus in the classroom.
J. David Brown
Friction does work when two objects in contact slide past one another. This process is studied using a simple numerical model consisting of two flexible asperites, one for each object. The force of friction is modeled as a conservative electric force that can be either attractive or repulsive. The concept of pseudowork plays an important role throughout the analysis. The difference between work and pseudowork is equal to the change in internal energy. Because the asperites are not rigid, the work done in a typical interaction is greater than the psuedowork, leading to an increase in internal energy. For a real physical system, this is the source of thermal energy. The processes discussed here fall into two categories: forced sliding and free sliding. With forced sliding, applied forces keep the objects moving with constant velocities. With free sliding, the only force on one of the objects (in the direction of motion) is friction. For free sliding, friction not only increases the internal energy but also decreases or increases the object's translational kinetic energy. The change in translational kinetic energy is determined by the pseudowork done by friction.
Alessandro Amabile, Maria Bondani, Immacolata De Simone +3
In this paper we introduce the new version of QTris, a board game designed to teach and learn Quantum Mechanics within the framework of Quantum Information and Computation. The key idea behind the game is that every game sequence simulates a process on a system of qubits. Thus, QTris can be effectively integrated as a pedagogical tool to teach Quantum Mechanics at high-school level following a two-state approach. After arguing in support of this latter approach, we describe QTris' basic rules and some of its possible extensions, emphasizing how the game mechanics puts in clear light key quantum concepts such as incompatibility, probabilistic measurement and unitary transformation. Moreover, we report on the results of a QTris-based educational activity which involved about 150 high-school students and provided encouraging preliminary indications that QTris can be a useful pedagogical platform to promote an immediate understanding of some key concepts of Quantum Mechanics.
Syed Furqan Abbas Hashmi, N. Sanjay Rebello
Large language model (LLM) tutors are being deployed in introductory physics courses at a scale that produces transcript corpora far larger than traditional qualitative coding can absorb. A central question for physics education research (PER) is empirical and prior to any claim about effectiveness: what do students actually say to these tutors? We address this question for one Socratic AI tutor deployed in an introductory calculus-based mechanics course by building a bottom-up taxonomy of student discourse. Each student turn is assigned an emergent free-text label by an LLM coder using the surrounding conversational context; near-paraphrase labels are then consolidated into a smaller set of discourse categories using a similarity-based grouping procedure. The procedure is validated against a stratified human-coded sample. The resulting taxonomy of 357 categories is strikingly concentrated: the top 25 categories cover roughly half of all student turns, and two thematic bands: equation-handling and meta-procedural requests together dominate the head of the distribution. The substantive contribution is the taxonomy itself: a description of the discourse PER researchers can expect to encounter when students work with an AI tutor of this design, including a striking prevalence of meta-procedural turns in which students cede strategic control to the tutor
Sean Savage, Anand Shanker, Grace Michlitsch +1
As computation becomes more central to physics education, creating scalable methods to assess authentic computational thinking (CT) in students remains a critical challenge. While student-written responses capture nuanced reasoning, they are difficult to evaluate at scale. In this study, we investigated the use of Large Language Models (LLMs) to analyze students' written explanations of computational physics problems on a pre- and post- semester survey. By first establishing a human-coded baseline, grounded in CT literature, we identified significant growth in Data Practices and Computational Problem-Solving Practices. When given the same responses, an LLM successfully mirrored the human evaluations and scaled up the detection of these key trends across a large dataset. Notably, both human raters and the LLM struggled to reliably evaluate more complex constructs such as Systems Thinking. Overall, this study demonstrates that LLMs offer a viable method to scale the evaluation of students' CT in large-enrollment physics courses
Roger M. Hart
Portable smart telescopes can make introductory astronomy more observational, quantitative, and locally grounded by allowing students to acquire, share, and analyze astronomical images within a single class period. This article presents an eight-laboratory sequence for introductory Solar System astronomy using the Seestar S50 smart telescope and archived or student-collected images. The sequence begins with a galaxy-size investigation that introduces pixel scale, angular size, physical size, proportional reasoning, and measurement uncertainty before students apply the same image-to-evidence approach to Solar System objects. Subsequent activities examine the positions and apparent motion of Jupiter's Galilean moons, comparative planetology and telescope limitations, the apparent sizes of Mars, Jupiter, and Saturn, lunar phase and angular diameter, relative lunar surface history through crater-density comparisons, single-session solar activity, and multi-day sunspot tracking to estimate solar rotation. Each investigation follows a 5E-informed structure and produces a focused student product, such as a measurement table, graph, comparison chart, or claim-evidence-reasoning statement. Throughout the sequence, students must define transparent measurement rules, distinguish direct observations from interpretations, and evaluate how image scale, illumination, exposure, seeing, target visibility, and boundary selection constrain their conclusions. The activities are designed for high-school, dual-enrollment, community-college, and introductory university astronomy courses and may be implemented with live observations or prepared image sets when weather, scheduling, or local observing conditions prevent data collection.
Eugenio Tufino
Responding productively, in real time, to the reasoning students actually produce is among the most difficult and slowest to acquire of the skills physics teachers develop, and prospective teachers get few opportunities to practise it before entering a classroom. In this pilot study, we describe a simulated class of five AI students, each consistently enacting a distinct reasoning pattern drawn from the dual-process theory (DPT) "hazards" framework of physics education research, and report on its first use in a university course for prospective physics teachers. Fifteen graduate students worked through an instructional sequence on DPT and questioning strategies. They then diagnosed two parallel sets of written vignettes, in a crossover arrangement, before and after interacting in pairs with the simulated class on a static-friction problem. The data set is completed by individual written hypotheses and reflections, the session logs and a short feedback questionnaire. Diagnostic scores improved significantly from PRE to POST (n = 11 paired, Wilcoxon p = 0.014, r = 0.79; on a 0-18 scale). During the simulation itself, however, participants asked predominantly uniform guiding questions, and the DPT vocabulary they had been taught appeared in only 2 of 71 substantive teacher turns, while seven of thirteen POST sheets used it readily. We read this knowing-doing gap not as a failure but as a snapshot of where each participant stands on the developmental trajectory of responsiveness to student ideas, a trajectory the simulation makes visible with transcript-level granularity. We discuss design, findings and implications for physics teacher preparation.
Drew Watson, Matthew Pontius, Charles Torre
We present a derivation at a level suitable for undergraduates of the Ostrogradsky formalism for Lagrangians in classical mechanics that depend upon an arbitrary number of time derivatives of the configuration. From the boundary term in the first variation of the Lagrangian we derive the Ostrogradsky formulas that define the Hamiltonian formulation of mechanical systems. Worked examples, exercises, and applications to the literature are also provided. An accompanying computer program that implements the formalism is discussed in the Supplementary Materials, and code for computing Hamiltonians via the Ostrogradsky formalism is provided in the Supplementary Materials and in a GitHub repository.
Santiago Hernández-Díaz, Ángel S. Sanz
Gravitational lensing is often introduced through the bending of light by mass, but its geometrical nature can be difficult to visualize without invoking the full machinery of general relativity. We present a geometrical-optics analogy in which selected lensing-like image morphologies are generated by ray tracing through axicon-type lenses. In contrast with analogies based on a spatially varying refractive index, the present model uses optical elements with a constant refractive index; the redistribution of rays is produced by the geometry of the refracting surfaces. After deriving the ray-tracing equations for a general two-surface axicon, we apply the model to conical and exponential profiles. Axially symmetric configurations generate ring-like images, misaligned incidence produces partial arcs, and broken axial symmetry leads to four-image patterns reminiscent of an Einstein cross. The model is not intended as a physical substitute for a relativistic lens equation, but as a computational and pedagogical tool for exploring how surface geometry and symmetry control ray deflection, redistribution, and image multiplicity.
Tianlong Zu, N. Sanjay Rebello
Conceptual surveys of multiple choice format have been developed to test the effect of pedagogical interventions on students understanding of physics knowledge. Predominantly, they are administered in pre and posttest settings and analyzed to obtain a performance gain. However, focusing on the correct answers to each question alone ignores the incorrect options which could inform us the stability and coherency of the knowledge structure of students. According to the resource model framework, each specific answer from students could reflect a specific cognitive resource being activated and implemented in the context at hand. Consequently, conceptual surveys could demonstrate how instruction could affect the activation of different cognitive resources of students in a variety of contexts when administered before and after instruction. Guided by the resources framework, we propose a transition matrix analysis to analyze data collected through conceptual surveys to investigate how instruction affects the consistency of cognitive resources activation by students. To provide proof of concept, we demonstrated how to utilize this method by analyzing students responses to a subset of questions from the DIRECT survey in a classroom study.
Amogh Sirnoorkar, Omkar Mamidpalliwar
Evolving technologies have traditionally influenced pedagogical practices and Generative AI is one such technology that promises to transform higher education. In this study, we investigate the association between introductory students' preferences for chatbot behavior and their epistemological beliefs surrounding physics. Our context involves a custom built online module on waves containing simulations integrated with a chatbot. While students' chatbot preferences were captured through three provided options (guided-inquiry, direct answer, and a combination of inquiry and answer), their epistemological beliefs were captured through the standardized Epistemological Beliefs Assessment for Physical Sciences (EBAPS) survey. Results highlight that students who preferred chatbots that initially engage them in guided-inquiry but provide answers when explicitly sought (`Combination'), demonstrated sophisticated epistemological beliefs than those who preferred answer-providing chatbots. Notably, we did not observe any association between the EBAPS' total scores among students who preferred guided-inquiry and those who preferred answer-oriented chatbots. Furthermore, the observed differences did not remain statistically significant after applying a Bonferroni-adjusted significance level. Implications of these results for the design and instructional use of chatbots in physics education are discussed.
Fabio Falchi, Riccardo Furgoni, Paolo Gattillo +1
350 years from the 1676 announcement of the Roemer's discovery that light propagates with finite speed, we present our observations using eyes with telescopes having similar resolution compared to those in the late 17th century. We confirmed that Roemer's method is valid and gives reasonable values for the speed of light c, within about 10 per cent of the modern value for our measurements, even with the simplest modelling technique, using uniform circular motions. We found that increasing the complexity of the model, e.g., by taking into account the elliptical orbit of Jupiter, does not necessarily bring the results closer to the value of c due to the influence of other perturbations. Using modern ephemerides yields a noticeably accurate result of c=(298200+-1900) km/s. This experience can have great didactic value by showing the interconnections between formulation of hypotheses and the consequent predictions, making observations, reducing data, and searching for alternative explanations for the same phenomenon. Lastly, we also found, in the correspondence between Roemer and Huygens, that Roemer in 1677 searched for an independent confirmation of what he found during previous years observing Io's eclipses by making observations and reducing the data of the meridian transits of the Great Red Spot on Jupiter.
Shams El-Adawy, A. R. Piña, Benjamin M. Zwickl +1
Career preparation for participation in the quantum industry is often framed in terms of formal educational pipelines, workforce projections, and knowledge and skills needed for various roles. Less is known about how quantum industry professionals themselves characterize their preparation for participation in the field. In this paper, we analyze interviews with quantum industry professionals working across a range of positions and company types to examine the experiences that enable entry into the quantum industry. Using thematic analysis, we identify four trajectories: (1) continuity of research practice from academia to industry, (2) reframing of prior expertise for quantum applications, (3) incremental engagement through various professional opportunities, and (4) network-enabled entry. These trajectories often co-occur within individual narratives, showing that preparation emerges from a combination of educational, professional, and relational experiences. Our findings demonstrate that supporting preparation for quantum industry careers requires more than the design of formal coursework and degree programs. Our results highlight the importance of experiential learning opportunities that allow students to apply their knowledge and skills and develop professional connections that facilitate entry into quantum careers.
Josep Ll. Suñer, Francisco M. Muñoz-Pérez, Juan C. Castro-Palacio +4
Smartphones have become a standard measurement instrument in the physics laboratory. Theirbuilt-in accelerometers, gyroscopes, magnetometers, and cameras have been used to investigate a wide range of phenomena in mechanics, and rotational motion in particular has proven especially well-suited to smartphone-based experiments. A recurring limitation, however, is that most experiments rely on precompiled sensor apps whose interfaces cannot be tailored to a specific activity, and until recently creating customized smartphone laboratories required programming knowledge beyond what most physics teachers possess. Following the approach, we recently introduced for acoustic experiments,10 in this paper we show that a fully customized, browser-based rotation laboratory can be generated entirely through natural-language prompting of an AI assistant, with no manual coding. We use it, together with a simple rotating platform, to characterize both uniform circular motion (UCM) and uniformly accelerated circular motion (UACM), and we validate the sensor measurements against independent video analysis with Tracker.
Markus S. Feser, Paul L. Tschisgale
Explaining physical phenomena is central to physics learning, because students' explanations provide evidence of their conceptual understanding. Because conceptual understanding can only be inferred through language rather than observed directly, distinguishing conceptual understanding from linguistic quality represents a fundamental challenge for assessment. In this study, we examined whether AI-based scoring approaches can assess students' conceptual understanding independently of the linguistic quality of their text-based explanations in physics. We compared conceptual understanding scores generated by nine machine learning-based scoring approaches and two large language model-based scoring approaches against human-expert-assigned scores for 116 secondary-school students' explanations. Despite generally good agreement with expert-assigned scores, explanations of lower linguistic quality were systematically more likely to be underestimated, i.e. receiving lower AI-generated conceptual understanding scores than experts assigned - a language bias that emerged across every AI-based scoring approach. Higher linguistic quality showed no comparable link to overestimation. Notably, this language bias closely resembles that previously reported for physics teachers, suggesting the difficulty lies less in any particular assessor than in the nature of inferring conceptual understanding from text-based explanations itself. The stakes fall hardest on multilingual learners, whose language proficiency may be misread as weaker understanding, and grow as AI-based scoring takes on higher-stakes decisions.
Jaimie A. Greasley, Thomas E. Baker
In recent years, quantum computing has gained strong traction on the global stage. Pioneering developments in quantum engineering, algorithms and software reinforce the view that quantum computation will eventually catalyze transformative technological advancements across diverse sectors. In anticipation of the future impact of quantum computing, many governments, universities and tech organizations have invested heavily into advancing quantum science, boosting quantum readiness, and building a diverse, talented workforce through quantum education initiatives. Specialized university programs, public awareness campaigns and STEM outreach activities all serve a critical function in fostering a deeper understanding of quantum physics and quantum technologies among non-experts. At the level of high school, a major vision is for these initiatives to reshape the teaching of 20th century physics in STEM classrooms, and inspire the future generation of quantum scientists and professionals. In the current paper, we are presenting the materials we have created for an outreach workshop introducing quantum computing to high school students in an approachable and engaging way. Intro2QC is an exploration of quantum computing's relevance to solving real-world challenges by relating how the unintuitive behaviours of quantum systems can be useful in providing computational advantage. The workshop materials include an online interactive quantum programming tutorial hosted on the Intro2QC website, along with the full lesson plan for STEM instructors and the presentation slides available on the Github repository. This single-session 90-minute workshop has been delivered to approximately 160 students in multiple school groups from Grades 9-12 in British Columbia Canada in the highlight of the International Year of Quantum Science and Technology.
Cassandra Paul, David Webb
In this paper we use multilevel modeling of 10 introductory courses from four STEM disciplines to quantify associations of Learning Assistants (LAs) with the student-level outcomes, pass-rates, and retention. Overall we find that having an LA in a class is associated with improvement of these metrics for all students. Fur- thermore, along with all students showing improvement, we find noticeable demographic differences in the amount of improvement. For instance, LAs seem to be associated with larger improvements in pass-rates for historically marginalized groups, and larger increases in graduation rates for women identifying as belonging to historically marginalized races and ethnicities when compared to students from the same group but with class sections without LAs. This indicates that Learning Assistant programs may be an excellent investment for diverse institutions.
Roxana Zus, Călin Alexa, Paul Grăvilă +2
The establishment of a joint coordinated national programme for graduate education represents a significant step in strengthening higher education systems and their alignment with international research environments. This paper examines the creation of the Romanian National Masters Programme in High-Energy Physics, the first initiative of its kind in the country. We outline the motivation for its development, based on the need to prepare students for active participation in large-scale international collaborations such as CERN, while also consolidating domestic expertise. The article describes the programmes design, including its multi-institutional structure, curriculum, and integration of research training, as well as the policy and administrative processes that enabled its implementation. Initial outcomes highlight the programmes contribution to student mobility, research engagement, and capacity building within the Romanian higher education system. Finally, we discuss lessons learned and future perspectives, situating the initiative within broader European efforts to modernise graduate education in the sciences.