Published: Jan. 1, 2024
Language: Английский
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 246, P. 123224 - 123224
Published: Jan. 19, 2024
Language: Английский
Citations
32
Education Sciences, Journal Year: 2024, Volume and Issue: 14(6), P. 636 - 636
Published: June 13, 2024
The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage GenAI systems higher education (HE) and, thus, it still unclear how to make effective technology for teaching learning practice. This paper provides an overview current state research on HE. To this end, study conducted systematic review relevant studies indexed by Scopus, using preferred reporting items reviews meta-analyses (PRISMA) guidelines. search criteria revealed total 625 papers, which 355 met final inclusion criteria. findings from showed future trends documents, citations, document sources/authors, keywords, co-authorship. gaps identified suggest that while some authors have looked at understanding detection AI-generated text, may be beneficial understand can incorporated into supporting educational curriculum assessments, teaching, delivery. Furthermore, need additional interdisciplinary, multidimensional HE through collaboration. will strengthen awareness students, tutors, other stakeholders, instrumental formulating guidelines, frameworks, policies usage.
Language: Английский
Citations
28
Journal of Global Health, Journal Year: 2024, Volume and Issue: 14
Published: March 28, 2024
OpenAI's Chat Generative Pre-trained Transformer 4.0 (ChatGPT-4), an emerging artificial intelligence (AI)-based large language model (LLM), has been receiving increasing attention from the medical research community for its innovative 'Data Analyst' feature. We aimed to compare capabilities of ChatGPT-4 against traditional biostatistical software (i.e. SAS, SPSS, R) in statistically analysing epidemiological data.
Language: Английский
Citations
23
Computation, Journal Year: 2025, Volume and Issue: 13(2), P. 30 - 30
Published: Jan. 29, 2025
The escalating complexity of cyber threats, coupled with the rapid evolution digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores transformative role LLMs in addressing critical cybersecurity. With landscapes and increasing sophistication security mechanisms often fall short detecting, mitigating, responding complex risks. LLMs, such as GPT, BERT, PaLM, demonstrate unparalleled capabilities natural language processing, enabling them parse vast datasets, identify vulnerabilities, automate threat detection. Their applications extend phishing detection, malware analysis, drafting policies, even incident response. By leveraging advanced features like context awareness real-time adaptability, enhance organizational resilience against cyberattacks while also facilitating more informed decision-making. However, deploying is not without challenges, including issues interpretability, scalability, ethical concerns, susceptibility adversarial attacks. critically examines foundational elements, real-world applications, limitations highlighting key advancements their integration into frameworks. Through detailed analysis case studies, this paper identifies emerging trends proposes future research directions, improving robustness, privacy automating management. study concludes by emphasizing potential redefine cybersecurity, driving innovation enhancing ecosystems.
Language: Английский
Citations
9
Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7
Published: June 18, 2024
The release of GPT-4 has garnered widespread attention across various fields, signaling the impending adoption and application Large Language Models (LLMs). However, previous research predominantly focused on technical principles ChatGPT its social impact, overlooking effects human–computer interaction user psychology. This paper explores multifaceted impacts interaction, psychology, society through a literature review. author investigates ChatGPT’s foundation, including Transformer architecture RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms studies significant improvements GPT models bring conversational interfaces. analysis extends psychological impacts, weighing potential mimic human empathy support learning against risks reduced interpersonal connections. commercial domains, discusses applications in customer service services, highlighting efficiency challenges such as privacy issues. Finally, offers predictions recommendations for future development directions impact relationships.
Language: Английский
Citations
15
Publications, Journal Year: 2024, Volume and Issue: 12(1), P. 9 - 9
Published: March 21, 2024
This study delves into a bibliometric analysis of ChatGPT, an AI tool adept at analysing and generating text, highlighting its influence in the realm social sciences. By harnessing data from Scopus database, total 814 relevant publications were selected scrutinised through VOSviewer, focusing on elements such as co-citations, keywords international collaborations. The objective is to unearth prevailing trends knowledge gaps scholarly discourse regarding ChatGPT’s application Concentrating articles year 2023, this underscores rapid evolution research domain, reflecting ongoing digital transformation society. presents broad thematic picture analysed works, indicating diversity perspectives—from ethical technological sociological—regarding implementation ChatGPT fields reveals interest various aspects using which may suggest certain openness educational sector adopting new technologies teaching process. These observations make contribution field sciences, suggesting potential directions for future research, policy or practice, especially less represented areas socio-legal implications AI, advocating multidisciplinary approach.
Language: Английский
Citations
11
Informatics, Journal Year: 2024, Volume and Issue: 11(1), P. 10 - 10
Published: Feb. 25, 2024
The penetration of intelligent applications in education is rapidly increasing, posing a number questions different nature to the educational community. This paper coming analyze and outline influence artificial intelligence (AI) on teaching practice which an essential problem considering its growing utilization pervasion global scale. A bibliometric approach applied outdraw “big picture” gathered bibliographic data from scientific databases Scopus Web Science. Data relevant publications matching query “artificial teaching” over past 5 years have been researched processed through Biblioshiny R environment order establish descriptive structure production, determine impact publications, trace collaboration patterns identify key research areas emerging trends. results point out growth production lately that indicator increased interest investigated topic by researchers who mainly work collaborative teams as some them are countries institutions. identified include techniques used applications, such intelligence, machine learning, deep learning. Additionally, there focus applicable technologies like ChatGPT, learning analytics, virtual reality. also explores context application for these various settings, including teaching, higher education, active e-learning, online Based our findings, trending topics can be encapsulated terms chatbots, AI, generative emotion recognition, large language models, convolutional neural networks, decision theory. These findings offer valuable insights into current landscape interests field.
Language: Английский
Citations
10
Acta Psychologica, Journal Year: 2024, Volume and Issue: 246, P. 104264 - 104264
Published: April 15, 2024
Design/methodology/approach. This article employs qualitative thematic modeling to gather insights from 30 informants. The study explores various aspects related the impact of COVID-19 pandemic on AI ChatGPT technologies. purpose this research is examine how has influenced increased usage and adoption ChatGPT. It aims explore pandemic's its applications in specific domains, as well challenges opportunities it presents. findings highlight that led a surge online activities, resulting heightened demand for been widely used areas such healthcare, mental health support, remote collaboration, personalized customer experiences. showcases examples ChatGPT's application during pandemic. framework enables delve deeply into multifaceted dimensions role pandemic, capturing diverse experiences users, practitioners, experts. By embracing nature inquiry offers comprehensive understanding challenges, opportunities, ethical considerations associated with utilization crisis contexts. have practical implications policymakers, developers, researchers. reserach emphasize need responsible implementation fully harness potential addressing societal needs beyond reliance changes user behavior, expectations, interactions. However, also unveiled risks. Addressing concerns, autonomy, privacy security, bias fairness, transparency accountability, crucial deployment contributes novel times crisis, particularly era highlights necessity provides valuable development technology future.
Language: Английский
Citations
9
Scientometrics, Journal Year: 2024, Volume and Issue: 129(6), P. 3593 - 3598
Published: April 29, 2024
Abstract In evaluative bibliometrics and higher education studies, one is frequently confronted with the task of comparing institutions similar institutions. this Letter to Editor, a simple approach discussed which applies ChatGPT. Although seems produce promising results (tested an example at level research institute university), it necessary investigate systematically based on sample including many before should be applied in evaluation. The challenge systematic investigations that ChatGPT provides user different answers sane request (missing reliability).
Language: Английский
Citations
9
Journal of Agriculture and Food Research, Journal Year: 2024, Volume and Issue: 16, P. 101148 - 101148
Published: April 4, 2024
Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness generalizability. In this study, we investigate deep transfer fine-grained classification maize plants, which is a challenging task due to the subtle nuanced patterns. We use four tailored frameworks: VGGNET, Inception V3, ResNet50, InceptionResNetV2. ResNet50 achieves highest validation accuracy of 87.51%, precision 90.33%, recall 99.80%, demonstrating efficacy superiority our approach. Our study offers an innovative solution accurate plants.
Language: Английский
Citations
8