Field Of Computer Science Research Articles

Treatment of cancer patients by generalizing a Fermatean normal vague set with aggregation operators

Multi-attribute decision-making problems can be solved using a Fermatean vague set. Fermatean vague sets are extension of vague sets. We initiated generalized Fermatean vague weighted averaging, generalized Fermatean vague weighted geometric, power generalized Fermatean vague weighted averaging and power generalized Fermatean vague weighted geometric. The algebraic structures such as associative, distributive, idempotent, bounded, commutativity and monotonicity properties are satisfied by generalized Fermatean vague numbers. We discussed some mathematical properties of these sets, as well as the Hamming distance and Euclidean distance. An algorithm that uses aggregation operators to solves multi-attribute decision-making problems. The fields of computer science and medicine are essential for medical diagnosis research. Choosing cancer treatment is a complex process. Patients and health care professionals must communicate effectively to make the right decision about their cancer treatment. There are a number of factors that influence patients treatment decisions. As far as cancer patients are concerned, there are five types of cancer patients. Many different treatments are currently available and they are often combined as part of an overall treatment plan involving various treatment options. Patients with which cancer can be treated in various ways, such as cystoscopy, biopsy, blood tests and CT scan of the urogram. We intended to choose the best treatment based on our comparisons and options. Thus, it is evident that natural number q has a significant influence on the models. Additionally, flowchart based multi-criteria decision-making is offered and applied to a numerical example to show the efficiency of the recommended approach. The results are evaluated for different values of the parameter. Moreover, a comparative analysis has been performed to illustrate the superior outcomes of the suggested approach in comparison with existing methodologies.

Towards Sustainable Decarbonization: Addressing Challenges in Electric Vehicle Adoption and Infrastructure Development

The transition to electric vehicles (EVs) plays a pivotal role in achieving decarbonization within the transportation sector. However, the widespread adoption of EVs faces multifaceted challenges, particularly concerning infrastructure development. This paper investigates the intersection of sustainability, decarbonization, and EV adoption, with a focus on identifying and analyzing the challenges associated with infrastructure deployment. Strictly adhering to the methodological principles and process of systematic literature reviews, this paper analyzes research spanning the fields of engineering, energy, computer science, environmental science, social sciences, and others to elucidate the barriers hindering EV adoption, ranging from technological limitations to regulatory complexities and market dynamics. Furthermore, it examines the critical role of infrastructure, encompassing charging networks, grid integration, and supportive policies, in facilitating EV uptake and maximizing environmental benefits. The findings are finally used to present the implications for theory, practice, and policies and to highlight the avenues for future research.

A Comprehensive Study of Datasets for Research Works in IoT Networks

Internet of Things is a trending subject in the field of Computer Science and Information Technology. When a research scholar tries to find a problem statement in the arena of IoT, he or she probably Google the phrase “Security Issues in Internet of Things”. On this day August 22, 2024, when we searched the Google with this phrase, we got about 48,40,00,000 results in 0.46 seconds. When we searched Google Scholar with the same phrase, we got about 40,70,000 results (0.52 sec). This shows that lakhs of researchers are toiling to find solutions for Security Issues in IoT. There is a new and trending way of research that is finding solutions for issues in IoT using Machine Learning and Deep Learning Models. ML and DL algorithms need large sets of Data to train and test the algorithms. This made us to take up a study on available Datasets for this research topic. When we made review of literature, in this arena of study, we found that researchers are working on only a few datasets, though numerous datasets are available freely on the internet for academic purposes. Hence in this paper we made a comprehensive study of Datasets available for research works in Internet of Things.

Paediatric systemic inflammatory response syndrome (SIRS) and the development of patient-specific therapy: ethical perspectives through experts' opinions.

Research for personalised therapies concerning the Systemic Inflammatory Response Syndrome (SIRS) in children involves the utilisation of OMICS technologies and Artificial Intelligence (AI). To identify specific ethical challenges through the perspective of healthcare professionals, we conducted 10 semi-structured interviews. The development of interview questions for the interviews was preceded by a systematic review of the scientific literature. To address the complexities of paediatric emergency research, informed consent, and data processing, experts with expertise in paediatric intensive care, computer science, and medical law were sought. After the transcription and anonymisation, the analysis followed established guidelines for qualitative content and thematic analysis. Interviewees highlighted the intricacies of managing consent in personalised SIRS research due to the large amount and complexity of information necessary for autonomous decision-making. Thus, instruments aimed at enhancing the understanding of legal guardians and to empowering the child were appreciated and the need for specific guidelines and establishing standards was expressed. Medical risks were estimated to be low, but the challenges of securing anonymisation and data protection were expected. It was emphasised that risks and benefits cannot be anticipated at this stage. Social justice issues were identified because of possible biases within the research population. Our findings were analysed using current ethical and legal frameworks for research with a focus on the particularities of the patient group and the emergency background. In this particular context, experts advocated for an enabling approach pertaining to AI in combination with OMICS technologies. As with every new technological development, ethical and legal challenges cannot be foreseen for SIRS-personalised treatment. Given this circumstance, experts emphasised the importance of extending the ethics-legal discourse beyond mere restrictions. The organisation of supervision should be reconsidered and not limited only to the precautionary principle, which per se was seen as impeding both the medical progress and clinical flexibility. It was noted that the establishment and monitoring of guidelines were emergent and should evolve through an interdisciplinary discourse. Therefore, it was recommended to enhance the qualifications of physicians in the field of computer science, impart ethics training to AI developers, and involve experts with expertise in medical law and data protection.

String diagrams for Strictification and Coherence

Whereas string diagrams for strict monoidal categories are well understood, and have found application in several fields of Computer Science, graphical formalisms for non-strict monoidal categories are far less studied. In this paper, we provide a presentation by generators and relations of string diagrams for non-strict monoidal categories, and show how this construction can handle applications in domains such as digital circuits and programming languages. We prove the correctness of our construction, which yields a novel proof of Mac Lane's strictness theorem. This in turn leads to an elementary graphical proof of Mac Lane's coherence theorem, and in particular allows for the inductive construction of the canonical isomorphisms in a monoidal category.

Impact of Machine Learning Integration in Qur’anic Studies

The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur’anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur’anic studies. This work explores the convergence of machine learning methodologies and Qur’anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur’anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur’anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur’anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.

Deep Learning and Human-machine Integration in Smart Prosthetic Control: Process and Perspectives

With the rapid development of science and technology and the maturity of deep learning technology, intelligent prosthetic limb control has made significant progress in human-machine integration. While traditional prosthetic technology has seen improvements in form and function, it still faces challenges in user experience and physiological adaptability. Deep learning, with its powerful pattern recognition and decision-making capabilities, offers new possibilities to address these issues. This paper reviews the current status and future development of the application of deep learning in intelligent prosthetic limb control, emphasizing its close relationship with the concept of human-machine integration. Intelligent prosthetic control involves not only mechanical engineering and bioengineering, but also interdisciplinary integration of the fields of neural engineering, electrical engineering, motion control and computer science. This paper reviews the limitations in traditional prosthetic technology, such as accuracy and user adaptability, introduces the successful cases and latest research results of deep learning in the medical field, and explores the potential of deep reinforcement learning in optimizing complex movements and adapting to different environments. Finally, the future direction of deep learning technology in smart prosthetic limb control is envisioned, and research challenges and recommendations are presented in the hope of providing theoretical support for the innovative development of smart prosthetic limb technology and improving the quality of life of people with disabilities.

Enhancing Network Security in Distributed Systems Using Middle Roman Dominating Functions

A Middle Roman dominating function (MRDN) on a graph G = (V,E) is a function f:v→{0,1,2,3} satisfying the condition that every vertex u with f(u)=0 is adjacent to at most one vertex v with f(v)=2 or 3. Further if a vertex is assigned 2, then at most two of its vertices can be assigned 0 and if a vertex is assigned 3, then all its neighbours can be assigned 0. The weight of a MRDF is the value f(V(G))=∑_uϵV▒〖f(u〗). The Middle Roman domination number γ_MR (G) is the minimum weight of a MRDF on G. In this paper, we introduce Middle Roman Domination number denoted as γ_MR (G), study the properties of the function, present some characterization and determine γ_MR (G)-value for some graphs. Middle Roman Dominating Functions (MRDFs) present a unique approach within graph theory, with significant implications for various fields in computer science. By assigning values to vertices under specific constraints, MRDFs enable the optimization of resources, enhancing network security, load balancing, and energy efficiency in distributed systems. This paper explores the application of MRDFs in scenarios such as intrusion detection, resilient network design, task scheduling, and sensor activation. By minimizing the overall weight while maintaining functional requirements, MRDFs provide an effective strategy for addressing challenges in network topology, resource allocation, and fault tolerance. The versatility of MRDFs makes them a valuable tool in the development of robust and efficient computing systems.

Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists.

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field.

A Decade of the Database Systems Research with the Lens of PODS, SIGMOD, TODS, and TKDE: A Bibliometric Perspective

Database management systems are an essential research field in data engineering, data mining, and computer science. The development and research in this field have evolved from manual bookkeeping to automatic data storage, rapidly growing with time. In this bibliometric study, the articles from four premier venues of publication in the field of database systems have been collected for analysis, including ACM Special Interest Group on Management of Data SIGMOD, a well-reputed symposium PODS (Principles of Database Systems) and two journals of the domain namely: Transactions on Database Systems (TODS) by ACM, and Transactions on Knowledge and Data Engineering (TKDE). This study includes the articles published between 2010 and 2019 in the selected venues to perform bibliometric analysis. Both TKDE and TODS are highly reputed journals in the field of database systems but publish quite different research. The SIGMOD conference began in 1975 and specializes in the research area of databases and data management problems, whereas PODS is the research conference that specializes in research related to database theory. This study presents the analysis of the collected articles from three major aspects: 1) metadata analysis, 2) content-based analysis, and 3) citation-based analysis. Furthermore, it also looked at the leading institutes, countries, and authorities based on article citation and publication count. As a whole, this study proposes a methodology for performing the extensive bibliometric analysis for the database management system and conducts a bibliometric analysis for the aforementioned top publication venues in this area of research.

Robust and accurate numerical framework for multi-dimensional fractional-order telegraph equations using Jacobi/Jacobi-Romanovski spectral technique

This paper presents a novel spectral algorithm for the numerical solution of multi-dimensional fractional-order telegraph equations, a critical model used to capture the combined effects of diffusion and wave propagation. The core innovation of this work is the application of Jacobi-Romanovski polynomials as the basis functions for spectral discretization. These polynomials offer unique advantages, including the ability to handle nonstandard domains and boundary conditions, making them particularly suitable for partial differential equation (PDE) applications. A comprehensive error analysis is conducted, providing deep insights into the convergence rates and factors affecting the accuracy of the numerical solutions. Extensive numerical experiments further demonstrate the superior performance of the proposed spectral algorithm in solving a wide range of multi-dimensional fractional-order telegraph equation models. The results show a significant improvement in accuracy and computational efficiency compared to traditional numerical methods, such as finite difference or finite element techniques. This research advances the field of computational science by offering a robust, efficient, and versatile numerical framework for the precise solution of complex multi-dimensional PDEs.

Enhancing Supply Chain Management Through Artificial Intelligence: A Case Study of JD Logistics

With the intensification of global economic competition, enterprises face the challenge of improving the supply chain management efficiency, and AI technology, as an emerging field in computer science, can provide effective solutions. As a leading e-commerce logistics service provider in China, JD Logistics has accumulated a wealth of logistics technology capabilities and digital transformation experience within and outside the JD Group. Using JD Logistics as a case study, this paper discusses the application of AI technology in supply chain management and its impact on enterprise responsiveness and operational efficiency. AI technology has significantly improved the supply chain operational efficiency and responsiveness of JD Logistics by applying intelligent supply chain planning, predictive analysis and intelligent decision-making. The study systematically summarizes the key role of AI technology in optimizing supply chain management through a literature review and specific case studies, providing an important reference and practical experience for enterprises to achieve digital transformation.

An in-depth study of sorting algorithms

Sorting algorithms are very popular fundamental algorithms in the field of computer science. Its job is sorting statistics so that they are arranged according to certain rules. In this paper, the kinds of sorting algorithms and their research method of them will be discussed. It will consist of discussing bubble sort, selection sorting, insertion sorting, hill sorting, and merge sorting. Those of them are that arrange a group of elements according to a certain number of ones. They are widely used in computer science and data processing in all walks of life. The most common sorting methods are numerical order and lexicographic order. Efficient sorting algorithms are significant in some of the algorithms, such as search and merge algorithms, so that they can arrive at the correct solution. Dealing with textual data and producing readable output are also important applications of sorting. The outcome is a permutation or recombination of the original input. So learning about the principles of those sorting algorithms by recognizing the code or reading pictures is significant. It is also necessary to be familiar with the applications of those algorithms which are beneficial to the daily life.

Establishing the Foundations of Emotional Intelligence in Care Companion Robots to Mitigate Agitation Among High-Risk Patients With Dementia: Protocol for an Empathetic Patient-Robot Interaction Study.

An estimated 6.7 million persons are living with dementia in the United States, a number expected to double by 2060. Persons experiencing moderate to severe dementia are 4 to 5 times more likely to fall than those without dementia, due to agitation and unsteady gait. Socially assistive robots fail to address the changing emotional states associated with agitation, and it is unclear how emotional states change, how they impact agitation and gait over time, and how social robots can best respond by showing empathy. This study aims to design and validate a foundational model of emotional intelligence for empathetic patient-robot interaction that mitigates agitation among those at the highest risk: persons experiencing moderate to severe dementia. A design science approach will be adopted to (1) collect and store granular, personal, and chronological data using Personicle (an open-source software platform developed to automatically collect data from phones and other devices), incorporating real-time visual, audio, and physiological sensing technologies in a simulation laboratory and at board and care facilities; (2) develop statistical models to understand and forecast the emotional state, agitation level, and gait pattern of persons experiencing moderate to severe dementia in real time using machine learning and artificial intelligence and Personicle; (3) design and test an empathy-focused conversation model, focused on storytelling; and (4) test and evaluate this model for a care companion robot (CCR) in the community. The study was funded in October 2023. For aim 1, architecture development for Personicle data collection began with a search for existing open-source data in January 2024. A community advisory board was formed and met in December 2023 to provide feedback on the use of CCRs and provide personal stories. Full institutional review board approval was received in March 2024 to place cameras and CCRs at the sites. In March 2024, atomic marker development was begun. For aim 2, after a review of open-source data on patients with dementia, the development of an emotional classifier was begun. Data labeling was started in April 2024 and completed in June 2024 with ongoing validation. Moreover, the team established a baseline multimodal model trained and validated on healthy-person data sets, using transformer architecture in a semisupervised manner, and later retrained on the labeled data set of patients experiencing moderate to severe dementia. In April 2024, empathy alignment of large language models was initiated using prompt engineering and reinforcement learning. This innovative caregiving approach is designed to recognize the signs of agitation and, upon recognition, intervene with empathetic verbal communication. This proposal has the potential to have a significant impact on an emerging field of computational dementia science by reducing unnecessary agitation and falls of persons experiencing moderate to severe dementia, while reducing caregiver burden. PRR1-10.2196/55761.

Preface

With the rapid development of science and technology, computer, remote sensing technology and aerospace science have increasingly become the focus of attention of academia and industry. Since the first International Conference on Computer, Remote Sensing and Aerospace (CRSA) was held, the conference has become an important platform for scholars in related fields to exchange ideas and share results. The 2024 4th International Conference on Computer, Remote Sensing and Aerospace (CRSA 2024) continues the high-level academic tradition of the previous three conferences, and was held virtually in Osaka, Japan from July 5th to 7th, 2024. The conference aimed to promote the cross-fertilization between computer science, remote sensing technology and aerospace science, to promote technological innovation and industrial upgrading, and to contribute wisdom and strength to the global scientific and technological development. It called for papers on such topics as High-Performance Computing, Computer Aided Design, Robotics and Automation, Geographic Information Science, Satellite Navigation and Positioning, Optical Remote Sensing, Aeroelasticity and Structural Dynamics, Manned / Unmanned Aviation, etc. The conference attracted the active participation of experts, scholars, engineers and industrial elites from all over the world, and the latest research results and technological advances in relevant field were demonstrated through thematic reports, oral and poster presentations. The thematic speeches covered the problems, challenges, solutions, and future research directions of computer science, remote sensing technology and aerospace science, and provided participants with comprehensive and in-depth understanding and insight, stimulating thinking and innovation. Scholars who participated in oral and poster presentations had in-depth exchanges with other participants, sharing experiences and insights, and jointly exploring cutting-edge issues in relevant disciplines and domains. In addition, we have carefully selected and prepared a collection of Proceedings based on this conference, which represents a bunch of excellent papers, covering the latest research achievements and innovative ideas in related fields. We hope that this Proceedings will be an important reference for the academic community, providing researchers with opportunities for in-depth research, and will also inject new vitality and impetus into the development and innovation in the fields of and related to computer science, remote sensing technology and aerospace science. At the same time, we also hope that scholar and researchers will continue to uphold the spirit of science and the courage to explore unknown areas, contributing to the prosperity and progress of mankind. Finally, we would like to express our sincere thanks to all for their support and participation in the conference. We believe that through the exchange and cooperation of this conference, we will jointly create a new future in the fields of computer science, remote sensing technology and aerospace science. We also believe that the publication of this Proceedings will make a positive contribution to the development of the academic community and the progress of aerospace industry! The Committee of CRSA 2024 List of Committee Member is available in this Pdf.

Authentic interdisciplinary online courses for alternative pathways into computer science

The field of computer science (CS) is facing a crucial challenge in broadening participation and embracing diversity, especially among underrepresented gender groups. The presented interdisciplinary educational program is an efficient response to this challenge, designed to catalyze diversity in CS through engagement with complex, interest-driven problems. This paper outlines the program’s structure, elucidates the pedagogical underpinnings, and reflects on the emergent challenges and opportunities. We delve into how the fusion of CS with other academic disciplines can allure a more varied demographic, emphasizing the engagement of female high school students — a demographic pivotally positioned yet significantly untapped in CS. Through a systematic survey analysis, we measure the program’s efficacy in increasing interest in CS and in cultivating an appreciation for its application in addressing real-world, cross-disciplinary challenges. Our findings affirm the program’s success in bridging the engagement gap by leveraging students’ intrinsic interests, thus charting alternative pathways into the CS field. These insights underscore the critical role of interdisciplinary approaches, establishing a new standard for transformative CS educational methods.

Demystifying The Object-Oriented Features Of Popular Object-Oriented Programming Languages.

Background: Indeed, Object-Oriented Programming (OOP) has emerged as one of the major fundamental paradigms in today’s world of software development as it contains principles that enhance modularity, reusability, and maintainability of software. This article explores deeper into some of the facets of OOP aspects as exhibited in some of the most frequently used programming languages ranging from Java, Python, C++, and C# with an intent of offering substantive information to aspects like encapsulation, inheritance, polymorphism, and abstraction. The article synthesizes the ones focusing on the relevance and conceptualization of OOP and problems related to it in the context of learners and practitioners. It also describes strengths and weaknesses of using C++, Python, and Java in relation to OOP while stressing peculiarity of these languages. Some of the core principles of OOP are highlighted such as encapsulation, inheritance, polymorphism, and abstraction and how they are complicated and significant in today’s software engineering. Materials and Methods: There was a presentation of a survey on which the research is based to determine the problems that programmers encounter when applying OOP features using a sample of 80 programmers in the computer science fields in some Nigerian Universities. Results: Findings reveal the significance of understanding OOP concepts in popular languages and suggest ways of increasing developers’ efficiency, minimizing errors, and improving the quality of developed software. Conclusion: Suggestions include a necessity for training and education to enhance software developers' understanding of OOP features. The findings can guide the creation of training materials and courses centered on OOP concepts, aiming to improve software development practices and elevate the quality of software products.

Preface

It is our pleasure to welcome the participants and dignitaries in the International Conference on Frontiers of Electronic, Electrical and Computer Science. This conference aims to bring together leading researchers, practitioners, and industry experts to share their latest findings, discuss emerging trends, and foster collaboration in the fields of electronic, electrical, and computer science. Our conference serves as a premier platform for showcasing a diverse range of topics and methodologies, reflecting the dynamic and evolving nature of these disciplines. I would like to extend my deepest gratitude to our proceedings editor, Dr. Hu Zhu from Nanjing University of Posts and Telecommunications. His meticulous efforts in overseeing the compilation and editing of these papers have been invaluable. Dr. Zhu’s dedication and expertise have ensured that the highest standards of academic quality are maintained. We are also honored to have four distinguished keynote speakers who have graciously shared their insights and knowledge, significantly enriching our conference: Prof. Yonghui Li from the University of Sydney, for his speech: Beyond 5G towards a Super-connected World Prof. Juin J. Liou from North Minzu University, for his speech: Broadband Impedance Measurements of EMI Filtering Chokes Prof. Yang Yue from Xi’an Jiaotong University, for his speech: Machine-Learning-based Opening Object Recognition Prof. Lei Lei from Nanjing University of Aeronautics and Astronautics, for his speech: Research on Intelligent Cooperation Theory of Unmanned Aerial Vehicular Swarms Based on Digital Twins Their presentations have not only provided deep insights into their respective areas of expertise but also inspired new directions for future research. I would also like to acknowledge the hard work and dedication of the organizing committee, reviewers, and all the volunteers who have contributed to making this conference a success. Their collective efforts have ensured a seamless and enriching experience for all participants. Finally, I extend my appreciation to all the authors who have submitted their work to ICFEECS 2024. Your contributions are the cornerstone of this conference, and your research advances the frontiers of electronic, electrical, and computer science. We hope that the knowledge and ideas exchanged during this conference will foster collaboration and innovation, driving forward the frontiers of these exciting fields. Thank you all for your participation and support. The Committee of ICFEECS 2024 List of Committee is available in this pdf.

Диагностика тесноты междисциплинарных связей по семестровым экзаменационным оценкам студентов в период смешанных форм обучения в вузе

The analysis of the closeness of interdisciplinary connections, built with the help of exam grades received by students in previous and subsequent disciplines, according to the sequence of their study according to the curriculum. The influence of the knowledge of mathematical disciplines on the knowledge of general engineering disciplines and the dependence of knowledge between the disciplines of a special block, achieved both in full-time and distance learning, is considered. The closeness of interdisciplinary ties was established on the basis of the results of the bachelor’s examinations in the field of Computer Science and Computer Engineering, conducted both before the outbreak and during the development of the pandemic.

Influence of critical thinking on LLM usage among Universitat d’Andorra students

As large language models continue to reshape educational practices, a comprehensive evaluation of critical thinking’s influence on large language models’ usage becomes essential. This study examines how students in the fields of education and computer science at the Universitat d’Andorra interact with large language models, with a particular focus on understanding their learning experiences, decision-making strategies, and problem-solving approaches. Using qualitative and quantitative methods, the research analyzes the frequency and purposes of using these technologies, as well as the critical thinking processes students employ to assess the reliability and relevance of content generated by artificial intelligence. Findings reveal a spectrum of attitudes towards large language models, ranging from enthusiastic adoption to skepticism. While many students appreciate the immediate and personalized academic support, content generation assistance, and writing skill improvement offered by these tools, concerns about the accuracy and potential biases of the outputs are prevalent. Notably, students demonstrate varying levels of the activation of their critical thinking skills when engaging with large language models, with some actively investigate the reliability of artificial intelligence generated information, while others exhibit a more passive reliance on these technologies. The study also highlights distinct usage patterns between computer science and education students. The results contribute to a deeper understanding of student behavior in the context of artificial intelligence enhanced education, providing valuable insights for educational institutions aiming to integrate these tools into their curricula effectively. Furthermore, this research emphasizes the need to enhance critical thinking skills within educational programs to empower students to navigate the complexities of large language models capabilities and limitations.