Papers

FlowMLLab is an open-source framework that connects validated fluid-mechanics solvers to scientific machine-learning experiments under a reproducible contract. Its computational fluid dynamics (CFD) pathway generates cavity data, whereas its direct simulation Monte Carlo (DSMC) pathway supplies rarefied-flow evidence with sampling diagnostics. Case-wise partitions prevent leakage, and matched non-neural baselines determine whether learning offers a measurable advantage. Examples include a coordinate network and a proper orthogonal decomposition (POD) deep operator network (DeepONet), together with learned kinetic closures. Python components regenerate every figure, while sixteen tutorials use the same interfaces as the command-line tools. By retaining numerical provenance, blind-case protocols and physical validation targets, FlowMLLab emphasizes traceable evidence rather than black-box curve fitting.

This book is a self-contained, tutorial introduction to topology, differential geometry, and general relativity for students and researchers in physics. Its distinguishing feature is a Mathematica-based approach that makes abstract constructions concrete through explicit, runnable notebooks and worked examples. A key contribution is a large collection of original Mathematica notebooks, written by the authors themselves, that let readers run, verify, and extend every demonstration. The first part covers point-set topology, topological spaces, continuity of maps, homotopy, and the fundamental group with applications that highlight the role of topology in modern physics. The second and largest part develops differential geometry from the ground up: manifolds, tangent and dual spaces, vector fields, pullbacks and pushforwards, Lie brackets and Lie algebras, local flows, and the Lie derivative. It then treats tensors, differential forms, the exterior derivative, volume forms, the metric tensor, and Hodge duality, emphasizing coordinate-free formulations and their computational realization. These tools are applied to Maxwell's equations in the language of forms and the generalized Stokes theorem, while Lie groups, fiber bundles, connections, and curvature bridge geometry and gauge-theoretic physics. The final part uses this framework to present general relativity, computing curvature tensors and field equations for standard spacetimes with dedicated Mathematica packages. Throughout, the book balances mathematical rigor with hands-on computation, enabling readers both to understand the theory and to reproduce every result themselves.

Generative artificial intelligence (genAI) systems are increasingly integral to epistemic processes such as hypothesis generation, explanation construction, and decision-making. Although they reliably enhance performance, emerging evidence reveals a metacognitive dilemma: as external generative capacity increases, internal monitoring, calibration, and cognitive engagement may decline. This reflects a redistribution of cognitive control within distributed human-AI systems that cannot be explained by automation bias or reliance on algorithms alone. We propose the AIRIS (AI-Augmented Inquiry and Regulation in Hybrid Systems) framework to analyze this dilemma and specify where regulatory intervention can counteract it. AIRIS is a multi-level control allocation architecture specifying the conditions under which epistemic agency can be preserved in hybrid generative systems. Drawing on distributed cognition, cognitive load theory, multimedia learning, and self-regulated learning, it identifies seven interacting mechanisms through which hybrid cognition may become destabilized, from delegation and calibration drift to motivational-affective drift. Five regulatory operators (Anticipate, Interrogate, Reflect, Integrate, and Synthesize) target internal generative engagement at points of emerging instability. The architecture does not itself improve learning; it specifies what must remain in place for genAI-supported work to sustain understanding, whether through instructional design, teacher guidance, or learners' own regulation. We derive testable propositions concerning the seven mechanisms and the five operators, reframing AI augmentation as a problem of control allocation in distributed generative systems. Beyond theory, AIRIS offers a research agenda, a design framework for genAI-integrated learning environments, and a conceptual toolkit for the governance of hybrid human-AI cognition.

We present an AI transcription workflow that uses freely available tools to transcribe instructional physics videos with substantial mathematical content into math-accessible PDFs. Guided by our carefully crafted system instructions, Google Gemini transcribes linked or uploaded videos into LaTeX output. Importantly, all board-written or screen-presented equations are transcribed into LaTeX math and incorporated into the verbal flow or presented in descriptive segments, which also describe graphs, diagrams, and pedagogically important actions and interactions. Gemini's LaTeX output is appended to a prewritten SE tagging-enabled preamble and compiled by a LuaLaTeX engine; the resulting PDF transcripts routinely compile without errors and pass PDF/UA-2 and ISO 32005 validation tests. We provide a balanced performance assessment using numerous examples from test transcriptions. The publicly available supplementary resources contain complete transcripts corresponding to all presented examples, and also contain our well-tested system instructions and preamble so that others can use, adapt, study, and further develop our transcription framework.

Multimodal AI can read handwritten physics solutions, but high-stakes grading requires agreement with official scores and outcomes. This study evaluated GPT-5.5-based grading on 10364 scanned pages from 520 handwritten submissions by 416 unique candidates or students across three assessments: a national Physics Olympiad theory examination, the final Olympiad selection camp with theory and experiment components, and a university quantum-mechanics examination. Each submission was graded twice by AI using the official rubrics. The second round used revised page-by-page and evidence-location instructions developed after first-round disagreement analysis. During grading, AI did not see official human marks or AI–human comparisons. Total-score correlations with official marks were high (0.91–0.97). For the final Olympiad selection, AI recovered the same five-student team as official grading. The second round improved aggregate question-part agreement, especially where first-round disagreements were larger. The main difficulty remained exact partial-credit grading, especially in experimental work. Reliable AI grading therefore depends on detailed rubrics and should be used as a second reader or audit tool under examiner control.

As quantum circuits grow beyond small toy examples, preparing them for execution on physical devices becomes increasingly complex. Design automation is therefore essential for scalable quantum computing: Compilation procedures optimize resource requirements and transform circuits to a format compatible with specific hardware; resource estimation evaluates execution cost; verification methods prove circuit correctness. However, these concepts present a steep learning curve for novices, particularly when quantum circuits are introduced through low-level textual representations. To address this, we present a block-based programming framework for quantum design automation, implemented as an extension to the Scratch programming platform. This system allows users to build quantum circuits as a sequence of blocks and embed them in classical control logic to perform evaluations, compare simulation results, and directly apply different design automation techniques. We evaluated the approach in a user study with computer science students, who completed guided exercises using the platform and provided structured feedback in the form of self-reports and short knowledge assessments. Results demonstrate strong understanding and confidence in quantum design automation concepts, suggesting that the block-based approach successfully lowers the entry barrier to quantum design automation. The implemented framework is open-source and available at https://github.com/munich-quantum-toolkit/scratch-quantum.

Research-based assessments (RBAs) have proven to be valuable tools in PER, supporting both instructional reform and foundational research. In the rapidly-growing field of Quantum Information Science (QIS), the lack of suitable RBAs limits the field's ability to make evidence-based decisions about curricula and program development. In this paper, we introduce the Quantum Computing Conceptual Survey (QCCS), an instrument developed to help address this gap by measuring student conceptual understanding in the foundations of quantum computing. Using pilot data from over 50 courses and 700 students, we present evidence supporting the validity of the QCCS for use in introductory QIS courses, drawing on analyses from both classical test theory and the Rasch model. We detail the potential uses for the QCCS, intending for it to serve as both a practical tool for instructors and a means of facilitating measurement-driven QIS education research.

Video analysis software is a staple of the modern undergraduate physics laboratory, enabling students to extract quantitative kinematic data from real-world phenomena. In this paper, we introduce PhysTrackX, a modern, open-source video analysis framework designed to minimize cognitive load and maximize pedagogical value. Built with a streamlined graphical interface, PhysTrackX features a comprehensive set of functionalities including video trimming, spatial calibration, and coordinate system definition with robust, machine learning based tracking. Furthermore, it introduces an integrated optical character recognition (OCR) module for synchronizing kinematic motion with external digital sensor displays, an interactive geometry plugin for in-situ spatial measurements, and a data management tool. In this article, we introduce the software and its functionality through some case studies.

This paper describes the development of the RIOT 2, an observational tool that can be used to study classrooms in which there are multiple members of an instructional team. The RIOT 2 is based on the Real-time Instructor Observational Tool (RIOT), which characterizes the behavior of an instructor over time. While the RIOT was previously developed for classrooms with only one instructor, our team has developed a version to observe classrooms supported by Learning Assistants (LAs). LA are undergraduate peer educators who facilitate discussions in active-learning classrooms. Unlike the RIOT, the RIOT 2 allows for the categorization of LA-instructor interactions. In this paper, we describe the development of RIOT 2 and present some initial data collected using the RIOT 2. While our data are preliminary, they illustrate how the RIOT 2 may be useful for future research and practice. This work is part of a broader research effort to understand how LA-faculty partnerships impact what LAs and instructors do in the classroom.

We describe two open-source, 3D-printable, flexure-based tools for the quantitative measurement of extensional properties of viscoelastic fluids. These low-cost, portable, and scalable devices (which we have termed “PocketCaBER” and “PocketDoS”) are particularly applicable for use in the field and in graduate-level teaching environments due to their low cost, printability on hobby 3D printers, compatibility with cell phone cameras, portability and user-friendly operation. We characterize and benchmark each device's performance against its lab-equivalent counterpart and provide downloadable STL files for rapid fabrication. We discuss experimental limitations of these devices compared with their bench-top counterparts. Finally, we illustrate the use of such tools in facilitating student engagement in polymer science and complex fluids classes—specifically, how progress in learning goals can be uniquely and effectively accelerated by providing the necessary rheological instruments directly to each individual student (especially for advanced modules such as nonlinear extensional rheology). By giving students personal, indefinite access to laboratory-level instrumentation through these open-source frugal science tools, we discuss our efforts to expand participation and engagement within the field of nonlinear rheology.

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.

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.

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.

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. Because the asperites are flexible, not rigid, the conservative friction force generates thermal energy and can decrease or increase the objects' translational kinetic energies. 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. The numerical model is used to illuminate the reasons why work is typically greater than pseudowork. 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.

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.

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

We describe the development, validation and implementation of a Quantum Interactive Learning Tutorial (QuILT) on quantum key distribution (QKD) using entanglement, a context which involves a practical application of quantum concepts relevant for the second quantum revolution. The QuILT helps students learn quantum concepts relevant for quantum cryptography using a simple two-particle system. The protocol uses two entangled particles and two Stern-Gerlach Apparati to generate a random shared key over a public channel for encrypting and decrypting information. It actively engages students in the learning process and helps them build links between the concepts learned in class and their real world applications. The QuILT was implemented as a homework in a traditional quantum mechanics course and a quantum computing and quantum information course. The evaluation suggests that the QuILT is helpful in improving students' understanding of the concepts related to QKD in both courses. Also, the evaluation of the QuILT in both types of courses with or without lecture-based instruction in relevant QKD concepts suggests that the QuILT can be given as a homework after lecture-based instruction on entanglement without in-class discussion on QKD. Therefore, entanglement can be covered without taking up much in-class time, while also providing students with an understanding of the QKD method and how it protects from eavesdroppers.

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

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.

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.