Usage Scenarios Research Articles

RPerf: Mining user reviews using topic modeling to assist performance testing: An industrial experience report

Software performance affects the user-perceived quality of software. Therefore, it is important to analyze the performance issues that users are concerned with. In this paper, we document our experience working with our industry partner on analyzing user reviews to identify and analyze performance issues users are concerned with. In particular, we designed an approach, RPerf, which automatically analyzes unstructured user reviews and generates a performance analysis report that can assist performance engineers with performance testing. In particular, RPerf uses BERTopic to uncover performance-related topics in user reviews. RPerf then maps the derived topics to performance KPIs (key performance indicators) such as response time. Such performance KPIs better help performance test design and allocate performance testing resources. Finally, RPerf extracts user usage scenarios from user reviews to help identify the causes. Through a manual evaluation, we find RPerf achieves a high accuracy (over 93%) in identifying the performance-related topics and performance KPIs from user reviews. RPerf can also accurately extract usage scenarios in over 80% of user reviews. We discuss the performance analysis report that is generated based on RPerf. We believe that our findings can assist practitioners with analyzing performance-related user reviews and inspire future research on user review analysis.

A DRL-based RAQ-GERT dynamic resource allocation algorithm considering utility for multibeam satellite system

With the evolution and popularity of smart devices, the demand and requirement (e.g., communication, file transfer) of satellite users have increased rapidly. Moreover, users have different preferences for services and the quality of service (QoS), like delay and throughput, which leads to user heterogeneity. Facing numerous, time-varying, and heterogeneous users, how to dynamically allocate limited spectrum and on-board power while satisfying user requirements is the major challenge for the multibeam satellite system (MSS). Aiming to seek a solution, firstly, the resource allocation queue graphical evaluation and review technique (RAQ-GERT) network is constructed to describe the service process of the MSS, as well as to compute the channel condition parameters during the whole process. Next, appropriate QoS indicators are selected based on user requirements. Then, QoS indicators are calculated from the results of the RAQ-GERT network, which are combined to form the optimization objective of the MSS by drawing on the Cobb–Douglas utility function. After that, guided by the utility of the MSS, the proximal policy optimization (PPO) algorithm is applied to explore the optimal resource allocation scheme in this heterogeneous user scenario. Finally, the simulation comparisons show that the proposed scheme has enhancements in several performances, up to 42.19 % in service rate, 53.58 % in system capacity, and 3.42 % in throughput with minimal increase in latency.

Application of Different Sensors in Obstacle Avoidance of Delivery Robots

People's living conditions are constantly rising as the global economy and society evolve at a rapid pace, and improving production efficiency has become a crucial concern. This paper proposes a combination of multiple obstacle avoidance schemes and algorithms to achieve dynamic obstacle avoidance for delivery robots. By analysing three different obstacle avoidance schemes including visual obstacle avoidance scheme, ultrasonic obstacle avoidance scheme and laser obstacle avoidance scheme, three robot usage scenarios including hotels, restaurants and hospitals, and three vital algorithms including Dijkstras algorithm, ORCA algorithm and PID algorithm. Then by analyzing the advantages and disadvantages of each scheme and algorithm and the characteristics of different usage scenarios, this paper obtains a robot obstacle avoidance and algorithm usage plan in a specific scenario.

Overview of Terrain Traversability Evaluation for Autonomous Robots

ABSTRACTTraversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open‐source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.

Lithium Iron Phosphate Battery Failure Under Vibration

The failure mechanism of square lithium iron phosphate battery cells under vibration conditions was investigated in this study, elucidating the impact of vibration on their internal structure and safety performance using high-resolution industrial CT scanning technology. Various vibration states, including sinusoidal, random, and classical impact modes, were tested to simulate real-world usage scenarios. The findings demonstrate that different vibration conditions exert varying degrees of influence on the battery cells. Despite experiencing slight deformation and displacement after exposure to vibrations, their overall performance remains stable, with no significant safety hazards detected. Moreover, it was observed that while the side gap increases due to the partial absorption of impact load by both the battery cells and connection components, the bottom gap remains unchanged. This study holds immense significance in enhancing electric vehicle safety and reliability, while providing a scientific foundation for future optimization designs of lithium iron phosphate batteries.

Analysis of Two-Phase Flow within Porous Structures of Water Electrolysis Cells and Structural Correlation Evaluation

In recent years, issues such as environmental pollution and global warming caused using fossil fuels have become increasingly serious, prompting attention to hydrogen derived from renewable energy sources as an energy source. Among the various hydrogen production technologies, attention has recently been drawn to Proton Exchange Membrane Water Electrolyzers (PEMWEs) utilizing solid polymer electrolyte membranes. The advantages of these PEMWEs include rapid response to irregular power supply from renewable energy sources, low environmental impact with no byproducts such as greenhouse gases, and the ability to select from various usage scenarios and operating environments due to their compact and lightweight design. In PEMWEs, a porous transport layer (PTL) is inserted on the catalyst layer to facilitate the removal of generated bubbles. With the improvement in the performance of solid polymer membranes, an increase in concentration overpotential attributed to gas-liquid transport in the PTL has been reported[1]. Accurately analyzing gas-liquid behavior within the porous structure poses challenges. In this study, we investigated the causal relationship between the three-dimensional structure of the porous material and gas-liquid transport properties using computational methods.To simulate gas-liquid transport within the porous media, a Pore Network Model (PNM) is employed. This model defines the three-dimensional spaces within the porous structure as 'pores' and the connections between pores as 'throats,' solving for the transient mass balance between pores. Flow velocity within the porous media follows principles like Darcy's law, where flux is driven by pressure differences, but also must consider the driving force due to capillary pressure owing to the small-scale nature. In the case of water electrolysis in PEM, the flow rates vary with the current density applied to the device. Generated bubbles progress towards liquid channels within the porous media. It's expected that structures impeding bubble progression would degrade electrolysis performance.Gas-liquid transport is strongly influenced by the pore size within the porous material. Within the porous structure, as the pore size increases, the capillary pressure required for progression decreases, making transport easier. However, larger pores also tend to trap bubbles within the porous structure, leading to an increase in concentration overpotential. In PEMWEs, various porous medias are being utilized; however, only quantitative evaluations have been conducted[2][3]. This study aims to elucidate the relationship between pore size and pore size distribution and their impact on gas-liquid transport performance, with a focus on non-woven fabric and particle types as the main research subjects.We confirmed that the three-dimensional structure inherent in each design affects gas-liquid transport properties. In the future, in order to validate the computational accuracy, it is necessary to conduct validation through experimental measurements in addition to calculations.AcknowledgmentThis study was supported by Center of Advanced Instrumental Analysis, Kyushu University.Reference[1] B. Han et al., Int. J. Hydrog. Energy, 42, 4478 (2017).[2] P. Lettenmeier et al., J. Power Sources, 311, 153 (2016).[3] H.Ito et al., Int. J. Hydrog. Energy, 37, 7418(2012).

DRL for handover in 6G-vehicular networks: A survey

3GPP is working on technology improvements related to sixth-generation (6G) wireless communication networks to keep pace. 6G networks are being developed as the next phase forward. Compared to their predecessor wireless technologies, 6G networks are predicted to offer better coverage and flexibility by supporting higher throughput, faster velocities, lower latency, and higher capacity. 6G targets the health, education, industry, and transport sectors. The transport field is undergoing rapid change. 6G and artificial intelligence (AI) will have an essential impact in this area, bringing users new services and functionalities. Within this field, the handover (HO) mechanism remains a concern that researchers must consider for achieving excellent communication quality since HO technology is crucial for ensuring seamless connectivity during user transfers between cells. Numerous proposed Machine Learning (ML) approaches, including Deep Reinforcement Learning (DRL), were discussed to solve HO issues. Recently, DRL methods have garnered significant interest in prospective wireless networks. They can surmount the escalating obstacles of the wireless environment and the constraints of conventional approaches. Moreover, DRL is crucial in wireless networks because of its capability to overcome the specific threats and dynamic nature of wireless settings. Elaborating on a comprehensive survey of these approaches related to HO and DRL can provide a unified analysis of the current advancements. This overview will help to improve understanding of this topic. This survey provides an overview of requirements and usage scenarios for 6G. It highlights the impact of this new wireless technology on the transportation field or the Vehicle-to-everything (V2X). In addition, we provide a deep study of HO management in 6G networks and elaborate on the various DRL literature solutions for HO in mobile and vehicular networks.

Which Diversity? Expanding the Gap Model of Disability for Digital Information Systems.

In this paper, we extend the gap model of disability that supports digital equipment and cyber-physical situational variations. We explore an alternative approach to visualizing and understanding disability that takes into account the diversity of personal abilities, the diversity of digital equipment and equally important, the situation (environmental, psychological, and digital) in which the information system is used. The most important implications of this work are twofold. First, this work will contribute to the need for Universal Design to take into account not only the diversity of abilities and impairments of the users that are tangible and recognizable in the physical environment, but also the diversity of digital equipment and usage situations. Second, as part of the systems development cycle, in user testing of digital services, this work highlights the essential need to not only involve a diverse set of users, but also to conduct testing in diverse digital environments (smartphones, tablets, laptops and desktop PCs), and in diverse realistic usage scenarios, including strong sunlight or low light situation, as well as tired, alert, stressed or distracted, and slow network. The proposed model attempts to give a more holistic view and promote a more comprehensive understanding of the disabilities that can occur during use of digital services or mediated communication in daily life.

Potential room temperature Li-ion battery thermal runaway gases sensor based on heterometal-doped CdS monolayer: Insights from DFT study

Under the extreme usage scenario, the thermal runaway gases (H2 and CO2) will be produced and leaked from the electrolyte of lithium battery. The molecular structure, adsorption properties, charge density difference (CDD), density of state (DOS), partial density of state (PDOS), desorption time, sensitivity, work function (WF) and frontier orbital theory are investigated to analyze the sensing characteristics toward H2 and CO2 of CdS monolayer, Ag–CdS, Pt–CdS, and Pd–CdS. The optimal CdS monolayer structure consist of hexagons, and the bandgap is 2.043 eV. After heterometal doping, the Eg decreases to 0.339 eV for Ag–CdS, 1.202 eV for Pt–CdS, and 1.358 eV for Pd–CdS. The adsorbing energy of the CdS–H2, Ag–CdS–H2, Pt–CdS–H2, and Pd–CdS–H2 is - 2.27 eV, - 0.80 eV, - 0.81 eV, and - 0.81 eV respectively, which correspond to the desorption time of 1.33 × 1026, 27.2, 40.1, and 40.1 s at 300 k in sequence. At room temperature, the response value of Pt–CdS to hydrogen is 78.61%, while the response value of Pd–CdS to hydrogen reaches as high as 98.17%. The Pt-doped and the Pd-doped CdS monolayer shows potential for room temperature H2 sensing, while the CdS monolayer displays the potential for H2 and CO2 cleaning.

Optimization of Tourism Destination Recommendations in Batang Regency Using Content-Based Filtering

In an era where tourism plays a pivotal role in economic development, the need for effective navigation through diverse attractions has never been more critical. This research presents a cutting-edge tourism recommendation system tailored for Batang Regency, leveraging Content-Based Filtering (CBF) to deliver personalized suggestions that enhance the tourist experience. By categorizing tourist attractions into Culinary, Culture, Accommodation, Nature, and Leisure, and employing the Haversine formula for precise geographical calculations, our system prioritizes recommendations based on user preferences and proximity. Recommendation testing yielded an impressive average F1 Score of 0.965, underscoring the system's accuracy and relevance, particularly in straightforward user scenarios. However, the research also identifies challenges in more complex cases, suggesting the need for future enhancements through hybrid models and the integration of user feedback. This innovative approach not only streamlines the decision-making process for tourists but also aims to boost local tourism, making it an invaluable tool for both visitors and the Batang Regency community. Join us in exploring how technology can transform the way we experience travel, ensuring that every journey is tailored to individual desires and needs.

The Role of Multi-Agents in Digital Twin Implementation: Short Survey

In recent years, Digital Twin (DT) technology has emerged as a significant technological advancement. A digital twin is a digital representation of a physical asset that mirrors its data model, behaviour, and interactions with other physical assets. Digital Twin aims at achieving adaptability, seamless data integration, modelling, simulation, automation, and real-time data management. The primary goal of this article is to explore the role of agents in DT implementations, seeking to understand their predominant usage scenarios and purposes. From our perspective, agents serving as intelligent entities play a role in realising the features of DTs. This article also discusses the gaps in DT, highlights future directions, and analyses various technologies integrated with multi-agent systems technologies in DT implementations. Finally, the article briefly discusses an overview of an architecture to implement a DT for smart agriculture with multi-agents.

Secrecy performance of D2D assisted cooperative uplink NOMA system with optimal power allocation strategy

This paper investigates the physical-layer security (PLS) aspects of the uplink cooperative non-orthogonal multiple access (NOMA) system in the context of two user scenario. More specifically, this work employs device-to-device (D2D) pair users (i.e., D1, and D2) to further improve the spectral efficiency (SE) of the proposed cooperative uplink NOMA (CU-NOMA) system. One D2D user acts as a decode-and-forward relay, improving the performance of both the cell-edge user (CU) and another D2D user i.e., (D2). We analyze the secrecy performance of CU and D2 under perfect and imperfect successive interference cancellation (SIC), and optimizes power allocation (PA) to boost the performance. The proposed CU-NOMA system is evaluated in terms of its performance metrics like ergodic secrecy capacity (ESC), ergodic secrecy sum capacity (ESSC), non-zero secrecy capacity (NSC), effective secrecy throughput (EST), and secrecy outage probability (SOP) under the presence of an external eavesdropper (Eav). In addition, this work derives the closed-form analytical expressions of ESC, NSC, EST, and SOP metrics for both CU and D2 under perfect SIC (pSIC) and imperfect (ipSIC) cases in order to characterize the secrecy performance of the proposed secure CU-NOMA network. Further, the outcomes of the simulations are shown as evidence for both the validation of the mathematical analysis and the performance of the method being suggested. The simulation result analysis of this work infers that the secrecy performance of CU-NOMA system with optimal PA exhibits superior performance than that of the fixed PA scheme.

Interleaved practice enhances grammar skill learning for similar and dissimilar tenses in Romance languages

BackgroundInterleaved practice (or interleaving), the strategy of alternating between categories or concepts during study or practice, can enhance second language grammar skills. It remains to be determined, however, whether that enhancement specifically involves identifying tenses, conjugating verbs, or both, and whether close similarity between tenses is essential. AimsThis study investigated the language skills that interleaving can enhance and the extent to which that enhancement is limited to highly similar tenses. SampleParticipants were college students (Experiment 1, 92 participants; Experiment 2, 109 participants) and adult learners (Experiment 3, 104 participants; Experiment 4, 88 participants). MethodsIn each experiment, participants completed two weekly learning sessions and a one-week delayed criterial test. In the blocked group, participants learned one tense per session. In the interleaved group, participants alternated between two tenses during each session. The criterial test assessed: verb conjugation skills (all experiments), tense identification ability for specific usage scenarios (Experiments 1–3), and the capacity to identify the language of a sentence written in a specific tense (Experiment 4). ResultsInterleaving improved verb conjugation skills in all experiments, tense identification ability in Experiments 1 and 3, and language identification skills in Experiment 4. Benefits of interleaving were observed across tenses varying in usage, meaning, and suffixes. ConclusionsInterleaving enhances multiple language skills, including verb conjugation, tense identification, and language identification. Those benefits are not limited to highly similar tenses. Accordingly, these results challenge assumptions about interleaving and underscore its potential as an effective approach for improving language learning.

Deep Configuration Performance Learning: A Systematic Survey and Taxonomy

Performance is arguably the most crucial attribute that reflects the quality of a configurable software system. However, given the increasing scale and complexity of modern software, modeling and predicting how various configurations can impact performance becomes one of the major challenges in software maintenance. As such, performance is often modeled without having a thorough knowledge of the software system, but relying mainly on data, which fits precisely with the purpose of deep learning. In this paper, we conduct a comprehensive review exclusively on the topic of deep learning for performance learning of configurable software, covering 1,206 searched papers spanning six indexing services, based on which 99 primary papers were extracted and analyzed. Our results outline key statistics, taxonomy, strengths, weaknesses, and optimal usage scenarios for techniques related to the preparation of configuration data, the construction of deep learning performance models, the evaluation of these models, and their utilization in various software configuration-related tasks. We also identify the good practices and potentially problematic phenomena from the studies surveyed, together with a comprehensive summary of actionable suggestions and insights into future opportunities within the field. To promote open science, all the raw results of this survey can be accessed at our repository: https://github.com/ideas-labo/DCPL-SLR .

Releasing characteristics and risk of micro/nanoplastics from Chinese herbal decoction packages under daily usage scenarios

The single-use plastic packaging for traditional Chinese herbal decoction is prevalent for its convenience. However, concerns have been raised regarding the biosafety implications of dissolved plastic particles from such food contact materials and their potential impact on drug efficacy. To initially investigate the release characteristics of micro/nanoplastics (M/NPs) from Chinese herbal decoction packages under different usage scenarios, 100 mL of water was filled at temperatures ranging from 40 ℃ to 100 ℃ and stored at 4 ℃ for 1 to 14 days before reheating at 60 ℃ prior to consumption. Fluorescence microscopy and nano flow cytometry revealed that one package could release 0.15–1.21 million MPs (over 60 % of MPs < 2 µm) and 0.48–4.32 billion NPs (over 40 % of NPs < 100 nm). The quantity of MPs decreased while NPs increased with higher filling temperatures, peaking after one day and seven days respectively. Exposure modeling estimated a daily intake of 2115–17,300 MPs/kg·day and 6.90–61.68 million NPs/kg·day from consuming one dose of Chinese herbal decoction. Furthermore, exposure of RAW264.7 cells to these plastic particles resulted in elevated levels of ROS and IL-6, aligning with dissolved NPs concentration trends. These findings cause apprehensions when utilizing plastic food packages due to M/NPs releases and potential proinflammatory risks.

Constructing a multifunctional reactive MXene-based additive for micro-crosslinking polyurea elastomer to achieve superior mechanical properties and enhanced fire safety

The widespread application of polyurea (PUA) in protective coatings typically requires excellent flame retardancy due to real-world usage scenarios. However, the addition of flame retardant additives often compromises the material’s mechanical properties. In this study, a multifunctional reactive additive, MX-SP-NH2, was creatively prepared by covalently grafting spirocyclic pentaerythritol bisphosphorate disphosphoryl chloride (SPDPC) onto MXene (Ti3C2Tx) surfaces, followed by the introduction of 1,3-propanediamine (PDA) by the reaction with SPDPC. MX-SP-NH2 was then incorporated into PUA, participating in its crosslinking process and significantly enhancing both its mechanical strength and flame retardancy. Compared to the control PUA sample, the micro-crosslinked PUA sample with just 1.0 wt% MX-SP-NH2 demonstrated a 184.3% increase in tensile strength, a 126.1% increase in elongation at break, and a 436.3% increase in toughness. The flammability characterization revealed that the peak heat release rate, peak smoke production rate, and peak CO production rate were reduced by 31.7%, 28.9%, and 61.5%, respectively, indicating a substantial improvement in fire safety. Additionally, the flame retardant PUA-based flexible strain sensors demonstrated stable electrical signal responsiveness. This work has opened up new potential applications of PUA in flexible electronics.

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

&lt;span&gt;To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.&lt;/span&gt;

Energy efficient heating and ventilation of a factory hall by monitoring the indoor air climate

Background Different usage scenarios and design guidelines were considered when planning a factory building and its ventilation system. Accordingly, it often makes sense to analyze the actual operating conditions again during subsequent operations in order to optimally adapt the air supply to the respective conditions in terms of demand-responsive ventilation. The aim is to ensure good indoor air quality and thermal comfort while significantly reducing energy consumption. Methods For this purpose, in addition to the sensors in the building management system, approximately 120 wireless sensors were installed in the occupied areas to measure the air temperature, operating temperature, humidity, and CO2. In this way the spatial and temporal distributions of temperature, humidity, and CO2 in the hall were visualized and evaluated to reduce the volume flows of the ventilation systems. The recorded data and findings were used to optimize ventilation systems by gaining a deeper understanding of indoor air flow. Results As part of the investigation, a considerable reduction in the air volume flows and thus the required energy consumption of the air handling units was achieved while maintaining the same thermal comfort. It was shown that the temperature field and indoor air quality were not negatively affected by the change in the air volume flow over a wide range. Conclusions This was made possible by the well-designed ventilation system, which achieves a low-pulse, slightly stratified indoor airflow, and a structural envelope with very good thermal insulation.

On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications

AbstractBlockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage. Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.

A qualitative survey on perception of medical students on the use of large language models for educational purposes.

Large language models (LLMs)-based chatbots use natural language processing and are a type of generative artificial intelligence (AI) that are capable of comprehending user input and generating output in various formats. They offer potential benefits in medical education. This study explored the student's feedback on the utilization of LLMs in medical education. We conducted an in-depth interview with open-ended questions with Indian medical students via telephone conversation. The recording (average time 55.28±18.04 min) was transcribed and thematically analyzed to find major themes and sub-themes. We used QDA Miner Lite v.2.0.8 (Provalis Research: Montreal, Canada) for the thematic analysis of the text. A total of 25 students from eight Indian states studying from the first to final year of studies participated in this study. Three major themes were identified about usage scenario, augmented learning, and limitation of LLMs. Students use LLMs for clarifying complex topics, searching for customized answers, solving MCQs, making simplified notes, and streamlining assignments. While they appreciated the ease of access, ready reference for getting clarity on doubts, lucid explanation of questions, and time-saving aspects of LLMs, concerns were raised regarding erroneous results, limited usage due to reliability and privacy issues, and the overreliance on chatbots for educational needs. Hence, they emphasized the need for training for the integration of LLM in medical education. In conclusion, according to students' perception, LLMs have the potential to enhance medical education. However, addressing challenges and leveraging the strengths of LLMs are crucial for optimizing their integration into medical education.