Viviana Fanti

Research on large language model (LLM)-based generative artificial intelligence (GenAI) in physics education has examined model performance, students’ evaluations of generated responses, and learning outcomes. Less is known about how generated explanations and calculations enter students’ observable physics problem-solving activity. This qualitative multiple-case study examines epistemic-frame sequences and their relation to solution quality, alongside students’ interactions with ChatGPT and their subsequent use of generated contributions. It analyses 30 submissions from 73 students across five university courses: 22 using ChatGPT and eight without it. ChatGPT was selected because its free version was students’ most frequently reported GenAI tool for independent study. Written artefacts and interaction records were analysed using Nguyen et al.’s problem framing framework, with solution quality assessed independently. Activity recurrently moved between conceptual and algorithmic physics, but frame-sequence length did not distinguish solution quality. Challenges and reformulations made students’ evaluation of ChatGPT responses visible, whereas incorporation without documented evaluation sometimes carried generated errors into final submissions. The heterogeneous dataset confines interpretation to qualitative patterns and precludes causal comparison. The findings identify students’ observable evaluation and use of generated responses as a key link between GenAI output and documented physics reasoning.

Teacher professional development in science education often struggles to translate theoretical knowledge into meaningful classroom practice, particularly at the lower secondary level. This study presents an original teaching experiment in classical mechanics, designed to foster both pedagogical content knowledge and conceptual change through a situated, inquiry-based approach. Grounded on situated learning theory, our methodology integrates the PEC model (prediction, experimentation, comparison) within modular teaching episodes enabling authentic engagement with physics concepts through low-cost, hands-on activities. Unlike many top-down professional development programs, this initiative builds from teachers’ intuitive knowledge, actively confronting and reconstructing misconceptions through guided inquiry and collaborative reflection. Relying on qualitative data—collected through classroom observation, discussion transcripts, and informal interviews—we document how teachers revised deeply held misunderstandings about motion, force, mass, and weight. Findings reveal a substantial shift in participants’ conceptual frameworks and teaching practices, including increased use of student-centred experimentation, more accurate use of scientific language, and improved ability to model inquiry-based instruction. This research contributes original evidence that sustained, situated learning environments can effectively promote long-term professional growth and epistemological transformation in science teachers, even in structurally constrained educational systems.

Einstein Telescope (ET) is the European project for a gravitational-wave (GW) observatory of third-generation. In this paper we present a comprehensive discussion of its science objectives, providing state-of-the-art predictions for the capabilities of ET in both geometries currently under consideration, a single-site triangular configuration or two L-shaped detectors. We discuss the impact that ET will have on domains as broad and diverse as fundamental physics, cosmology, early Universe, astrophysics of compact objects, physics of matter in extreme conditions, and dynamics of stellar collapse. We discuss how the study of extreme astrophysical events will be enhanced by multi-messenger observations. We highlight the ET synergies with ground-based and space-borne GW observatories, including multi-band investigations of the same sources, improved parameter estimation, and complementary information on astrophysical or cosmological mechanisms obtained combining observations from different frequency bands. We present advancements in waveform modeling dedicated to third-generation observatories, along with open tools developed within the ET Collaboration for assessing the scientific potentials of different detector configurations. We finally discuss the data analysis challenges posed by third-generation observatories, which will enable access to large populations of sources and provide unprecedented precision.

Problem solving in physics requires more than applying formulas: it involves describing and modeling phenomena, connecting mathematics with physics, and justifying reasoning choices. This process, known as problem framing, has been extensively studied in its cognitive and epistemic dimensions, but its semiotic aspects – how visuals, symbols, language, and metaphors shape understanding – remain underexplored. Physics relies on multiple representational modes that must be coordinated to construct meaning, and semiotics plays a central role in this integration. In this theoretical paper, we propose a new framework – the Semiotic Problem Framing – that explicitly incorporates semiotics into existing problem framing in physics. SPF highlights how students mobilize and shift across linguistic, visual, symbolic, and metaphorical resources in problem solving. For students, it offers a guide to structure reasoning and develop representational fluency; for teachers, it provides a diagnostic tool to scaffold and monitor learning processes. SPF enables analysis of reasoning patterns and error types not captured in previous frameworks, and suggests new directions for instructional design in physics education.

This study presents the design and the implementation of an inquiry-based reconstruction of Rutherford’s gold foil experiment. The activity engaged students in collaborative exploration using a ‘black box’ as an analogical model of the atom. Grounded in constructivist theory and the Focus–Action–Reflection model to introduce analogies in the in-class context, we explore how analogical reasoning can support high school students in understanding the atomic structure. Data from student worksheets, graphical representations, and group discussions were analyzed using thematic, phenomenographic, and conceptual metaphor analysis. Results indicate that students were able to develop and refine analogical models, reason from empirical evidence, and engage in critical reflection on the epistemological limits of the analogy. While students demonstrated conceptual gains and collaborative problem-solving, the analysis also revealed persistent risks of reifying metaphors or overextending macroscopic analogies to quantum domains. These findings highlight the value—and limitations—of structured analogical modeling in contemporary physics education, and point to the need for teacher training that fosters epistemological awareness and reflective use of models in the classroom.

The interdisciplinary teaching of contemporary physics has become increasingly common in physics education, especially for high school students and teachers. This approach, which integrates content and methodologies from various disciplines, fosters scientific reasoning, enhances creativity, and increases student motivation and interest in physics. The use of digital technologies, such as social media platforms, supports these educational goals by facilitating the inclusive and cost-effective dissemination of scientific knowledge and the development of soft skills. This paper introduces the “Gravitas” project, an initiative that employs an interdisciplinary approach to present contemporary physics topics to high school students through social media. Coordinated by the Cagliari Division of the National Institute of Nuclear Physics (INFN) in Italy, the “Gravitas” project offers a non-traditional learning environment where students explore modern physics and philosophy and the history of science. Through the creation of educational materials, such as social media posts, students actively engage in their learning. In 2022, around 250 students from 16 high schools across Sardinia, Italy, participated in this project. This paper discusses the learning outcomes, highlighting the potential of integrating formal high school curricula with innovative educational and digital tools.

Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling.