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In the rapidly evolving interdisciplinary field of quantum information science and technology, a big obstacle is the necessity of understanding high-level mathematics to solve complex problems. Visualizations like the (dimensional) circle notation enable us to visualize not only single-qubit but also complex multi-qubit states, entanglement, and quantum algorithms. Current findings in educational research suggest that incorporating visualizations in settings of problem solving can have beneficial effects on students' performance and cognitive load compared to solely relying on symbolic problem solving content. In this pilot study, we aim to take a first step to identify in which contexts students benefit from the presentation of visualizations of multi-qubit systems in addition to mathematical formalism. We compare students' performance, time taken, and cognitive load when solving problems using the mathematical-symbolic Dirac notation alone with using it accompanied by the circle notation or the dimensional circle notation in single- and multi-qubit systems. Although little overall differences in students' performance can be detected depending on the presented representations, we observe that problem-solving performance is student- and context-dependent. In addition, the results indicate reduced cognitive load when participants are presented with visualization. The results are discussed with respect to relevant design aspects for future studies., Comment: 32(+20) pages, 16(+6) figures

As distance learning becomes increasingly important and artificial intelligence tools continue to advance, automated systems for individual learning have attracted significant attention. However, the scarcity of open-source online tools that are capable of providing personalized feedback has restricted the widespread implementation of research-based feedback systems. In this work, we present RATsApp, an open-source automated feedback system (AFS) that incorporates research-based features such as formative feedback. The system focuses on core STEM competencies such as mathematical competence, representational competence, and data literacy. It also allows lecturers to monitor students' progress. We conducted a survey based on the technology acceptance model (TAM2) among a set of students (N=64). Our findings confirm the applicability of the TAM2 framework, revealing that factors such as the relevance of the studies, output quality, and ease of use significantly influence the perceived usefulness. We also found a linear relation between the perceived usefulness and the intention to use, which in turn is a significant predictor of the frequency of use. Moreover, the formative feedback feature of RATsApp received positive feedback, indicating its potential as an educational tool. Furthermore, as an open-source platform, RATsApp encourages public contributions to its ongoing development, fostering a collaborative approach to improve educational tools., Comment: 40 pages, 6 figures

The increased presence of large language models (LLMs) in educational settings has ignited debates concerning negative repercussions, including overreliance and inadequate task reflection. Our work advocates moderated usage of such models, designed in a way that supports students and encourages critical thinking. We developed two moderated interaction methods with ChatGPT: hint-based assistance and presenting multiple answer choices. In a study with students (N=40) answering physics questions, we compared the effects of our moderated models against two baseline settings: unmoderated ChatGPT access and internet searches. We analyzed the interaction strategies and found that the moderated versions exhibited less unreflected usage (e.g., copy \& paste) compared to the unmoderated condition. However, neither ChatGPT-supported condition could match the ratio of reflected usage present in internet searches. Our research highlights the potential benefits of moderating language models, showing a research direction toward designing effective AI-supported educational strategies., Comment: paper consists of 13 pages 6 figures; supplementary material 15 pages

Effectively supporting students in mastering all facets of self-regulated learning is a central aim of teachers and educational researchers. Prior research could demonstrate that formative feedback is an effective way to support students during self-regulated learning (SRL). However, for formative feedback to be effective, it needs to be tailored to the learners, requiring information about their learning progress. In this work, we introduce LEAP, a novel platform that utilizes advanced large language models (LLMs), such as ChatGPT, to provide formative feedback to students. LEAP empowers teachers with the ability to effectively pre-prompt and assign tasks to the LLM, thereby stimulating students' cognitive and metacognitive processes and promoting self-regulated learning. We demonstrate that a systematic prompt design based on theoretical principles can provide a wide range of types of scaffolds to students, including sense-making, elaboration, self-explanation, partial task-solution scaffolds, as well as metacognitive and motivational scaffolds. In this way, we emphasize the critical importance of synchronizing educational technological advances with empirical research and theoretical frameworks., Comment: 9 pages, 3 Figures, 1 Table

Large language models (LLMs) have recently gained popularity. However, the impact of their general availability through ChatGPT on sensitive areas of everyday life, such as education, remains unclear. Nevertheless, the societal impact on established educational methods is already being experienced by both students and educators. Our work focuses on higher physics education and examines problem solving strategies. In a study, students with a background in physics were assigned to solve physics exercises, with one group having access to an internet search engine (N=12) and the other group being allowed to use ChatGPT (N=27). We evaluated their performance, strategies, and interaction with the provided tools. Our results showed that nearly half of the solutions provided with the support of ChatGPT were mistakenly assumed to be correct by the students, indicating that they overly trusted ChatGPT even in their field of expertise. Likewise, in 42% of cases, students used copy & paste to query ChatGPT -- an approach only used in 4% of search engine queries -- highlighting the stark differences in interaction behavior between the groups and indicating limited reflection when using ChatGPT. In our work, we demonstrated a need to (1) guide students on how to interact with LLMs and (2) create awareness of potential shortcomings for users., Comment: Pre-print currently under review

The recent advancement of large language models presents numerous opportunities for teaching and learning. Despite widespread public debate regarding the use of large language models, empirical research on their opportunities and risks in education remains limited. In this work, we demonstrate the qualities and shortcomings of using ChatGPT 3.5 for physics task development by prospective teachers. In a randomized controlled trial, 26 prospective physics teacher students were divided into two groups: the first group used ChatGPT 3.5 to develop text-based physics tasks for four different concepts in the field of kinematics for 10th grade high school students, while the second group used a classical textbook to create tasks for the same concepts and target group. The results indicate no difference in task correctness, but students using the textbook achieved a higher clarity and more frequently embedded their questions in a meaningful context. Both groups adapted the level of task difficulty easily to the target group but struggled strongly with sufficient task specificity, i.e., relevant information to solve the tasks were missing. Students using ChatGPT for problem posing rated high system usability but experienced difficulties with output quality. These results provide insights into the opportunities and pitfalls of using large language models in education., Comment: 10 pages, 3 figures, 3 tables

Modern-days CMOS-based computation technology is reaching its fundamental limitations. The emerging field of magnonics, which utilizes spin waves for data transport and processing, proposes a promising path to overcome these limitations. Different devices have been demonstrated recently on the macro- and microscale, but the feasibility of the magnonics approach essentially relies on the scalability of the structure feature size down to an extent of a few 10 nm, which are typical sizes for the established CMOS technology. Here, we present a study of propagating spin-wave packets in individual yttrium iron garnet (YIG) conduits with lateral dimensions down to 50 nm. Space and time resolved micro-focused Brillouin-Light-Scattering (BLS) spectroscopy is used to characterize the YIG nanostructures and measure the spin-wave decay length and group velocity directly. The revealed magnon transport at the scale comparable to the scale of CMOS proves the general feasibility of a magnon-based data processing.

Magnetic field fluctuations arising from fundamental spins are ubiquitous in nanoscale biology, and are a rich source of information about the processes that generate them. However, the ability to detect the few spins involved without averaging over large ensembles has remained elusive. Here we demonstrate the detection of gadolinium spin labels in an artificial cell membrane under ambient conditions using a single-spin nanodiamond sensor. Changes in the spin relaxation time of the sensor located in the lipid bilayer were optically detected and found to be sensitive to near-individual proximal gadolinium atomic labels. The detection of such small numbers of spins in a model biological setting, with projected detection times of one second, opens a new pathway for in-situ nanoscale detection of dynamical processes in biology., Comment: 16 pages, 4 figures

The controlled scaling of diamond defect center based quantum registers relies on the ability to position NVs with high spatial resolution. Using ion implantation, shallow (< 10 nm) NVs can be placed with accuracy below 20nm, but generally show reduced spin properties compared to bulk NVs. We demonstrate the augmentation of spin properties for shallow implanted NV centers using an overgrowth technique. An increase of the coherence times up to an order of magnitude (T_2 = 250 \mu s) was achieved. Dynamic decoupling of defects spins achieves ms decoherence times. The study marks a further step towards achieving strong coupling among defects positioned with nm precision., Comment: 10 pages, 3 Figures

We present a solid state magnetic field imaging technique using a two dimensional array of spins in diamond. The magnetic sensing spin array is made of nitrogen-vacancy (NV) centers created at shallow depths. Their optical response is used for measuring external magnetic fields in close proximity. Optically detected magnetic resonance (ODMR) is readout from a 60x60 $\mu$m field of view in a multiplexed manner using a CCD camera. We experimentally demonstrate full two-dimensional vector imaging of the magnetic field produced by a pair of current carrying micro-wires. The presented widefield NV magnetometer offers in addition to its high magnetic sensitivity of 20 nT/$\sqrt{Hz}$ and vector reconstruction, an unprecedented spatio-temporal resolution and functionality at room temperature.