An exploratory survey about using ChatGPT in education, healthcare, and research

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(10), P. e0292216 - e0292216

Published: Oct. 5, 2023

Objective ChatGPT is the first large language model (LLM) to reach a large, mainstream audience. Its rapid adoption and exploration by population at has sparked wide range of discussions regarding its acceptable optimal integration in different areas. In hybrid (virtual in-person) panel discussion event, we examined various perspectives use education, research, healthcare. Materials methods We surveyed in-person online attendees using an audience interaction platform (Slido). quantitatively analyzed received responses on questions about contexts. compared pairwise categorical groups with Fisher’s Exact. Furthermore, used qualitative analyze code discussions. Results 420 from estimated 844 participants (response rate 49.7%). Only 40% had tried ChatGPT. More trainees faculty. Those who were more interested it wider contexts going forwards. Of three discussed contexts, greatest uncertainty was shown education. Pros cons raised during for this technology Discussion There around uses healthcare, still much acceptability uses. respondents roles (trainee vs faculty staff). needed explore perceptions LLMs such as vital sectors healthcare research. Given involved risks unforeseen challenges, taking thoughtful measured approach would reduce likelihood harm.

Language: Английский

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Information, Journal Year: 2024, Volume and Issue: 15(2), P. 99 - 99

Published: Feb. 8, 2024

GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, design, thereby elevating efficiency quality scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions LLM specifically augmentation generation synthetic for research. Employing a meticulous examination 412 works, it distills selection 77 contributions addressing three questions: (1) on Generating Research data, (2) Data Analysis, (3) Design. The systematic literature adeptly highlights central focus encapsulating 48 pertinent contributions, extends proactive role in analysis shaping design. Pioneering comprehensive classification framework “GPT’s use Data”, study classifies existing into six categories 14 sub-categories, providing profound insights multifaceted applications data. This meticulously compares 54 pieces literature, evaluating domains, methodologies, advantages disadvantages, scholars with crucial seamless integration across diverse their pursuits.

Language: Английский

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The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool

AI and Ethics, Journal Year: 2024, Volume and Issue: unknown

Published: May 27, 2024

Using artificial intelligence (AI) in research offers many important benefits for science and society but also creates novel complex ethical issues. While these issues do not necessitate changing established norms of science, they require the scientific community to develop new guidance appropriate use AI. In this article, we briefly introduce AI explain how it can be used research, examine some raised when using it, offer nine recommendations responsible use, including: (1) Researchers are identifying, describing, reducing, controlling AI-related biases random errors; (2) should disclose, describe, their including its limitations, language that understood by non-experts; (3) engage with impacted communities, populations, other stakeholders concerning obtain advice assistance address interests concerns, such as related bias; (4) who synthetic data (a) indicate which parts synthetic; (b) clearly label data; (c) describe were generated; (d) why used; (5) systems named authors, inventors, or copyright holders contributions disclosed described; (6) Education mentoring conduct include discussion

Language: Английский

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How to optimize the systematic review process using AI tools

JCPP Advances, Journal Year: 2024, Volume and Issue: 4(2)

Published: April 23, 2024

Systematic reviews are a cornerstone for synthesizing the available evidence on given topic. They simultaneously allow gaps in literature to be identified and provide direction future research. However, due ever-increasing volume complexity of literature, traditional methods conducting systematic less efficient more time-consuming. Numerous artificial intelligence (AI) tools being released with potential optimize efficiency academic writing assist various stages review process including developing refining search strategies, screening titles abstracts inclusion or exclusion criteria, extracting essential data from studies summarizing findings. Therefore, this article we an overview currently how they can incorporated into improve quality research synthesis. We emphasize that authors must report all AI have been used at each stage ensure replicability as part reporting methods.

Language: Английский

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Mapping the Ethics of Generative AI: A Comprehensive Scoping Review

Minds and Machines, Journal Year: 2024, Volume and Issue: 34(4)

Published: Sept. 17, 2024

Language: Английский

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Leveraging Large Language Models for Enhancing Safety in Maritime Operations

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1666 - 1666

Published: Feb. 6, 2025

Maritime operations play a critical role in global trade but face persistent safety challenges due to human error, environmental factors, and operational complexities. This review explores the transformative potential of Large Language Models (LLMs) enhancing maritime through improved communication, decision-making, compliance. Specific applications include multilingual communication for international crews, automated reporting, interactive training, real-time risk assessment. While LLMs offer innovative solutions, such as data privacy, integration, ethical considerations must be addressed. concludes with actionable recommendations insights leveraging build safer more resilient systems.

Language: Английский

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Best Practices for Using AI Tools as an Author, Peer Reviewer, or Editor

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e51584 - e51584

Published: Aug. 31, 2023

The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter concern the publishing community. Generative AI computationally capable elaborating research questions; refining programming code; generating text language; and images, graphics, or figures. However, this technology should be used with caution. In editorial, we outline current state editorial policies on chatbot authorship, peer review, processing scholarly manuscripts. Additionally, provide JMIR Publications' these issues. We further detail approach to applications process for manuscripts review Publications journal.

Language: Английский

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Generative AI, Research Ethics, and Higher Education Research: Insights from a Scientometric Analysis

Information, Journal Year: 2024, Volume and Issue: 15(6), P. 325 - 325

Published: June 2, 2024

In the digital age, intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration this multifaceted relationship. This study aims to quantify characterize current research trends critically assess discourse on AI applications within HE. Employing mixed-methods design, we integrated quantitative data from Web Science, Scopus, Lens databases with qualitative insights selected studies perform scientometric content analyses, yielding nuanced landscape utilization in Our results identified vital areas through citation bursts, keyword co-occurrence, thematic clusters. We provided conceptual model for integration HE, encapsulating dichotomous perspectives AI’s role education. Three clusters were identified: frameworks policy development, academic integrity creation, student interaction AI. The concludes that, while offers substantial benefits educational advancement, it also brings challenges that necessitate vigilant governance uphold standards. implications extend policymakers, educators, developers, highlighting need guidelines, literacy, human-centered tools.

Language: Английский

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The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualization

Published: May 11, 2024

The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them agency ownership, about spending their time on text generated by soulless machines. the other AI-assistance can improve as long conform to publisher policies, be assured a been verified human. We argue system captures provenance interaction with an LLM help retain agency, communicate AI publishers transparently. Thus we propose HaLLMark, tool visualizing writer's LLM. evaluated HaLLMark 13 creative writers, found it helped sense control ownership text.

Language: Английский

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Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How

Published: May 2, 2024

Advances in Generative Artificial Intelligence (AI) are resulting AI-generated media output that is (nearly) indistinguishable from human-created content. This can drastically impact users and the sector, especially given global risks of misinformation. While currently discussed European AI Act aims at addressing these through Article 52's transparency obligations, its interpretation implications remain unclear. In this early work, we adopt a participatory approach to derive key questions based on disclosure obligations. We ran two workshops with researchers, designers, engineers across disciplines (N=16), where participants deconstructed relevant clauses using 5W1H framework. contribute set 149 clustered into five themes 18 sub-themes. believe not only help inform future legal developments interpretations 52, but also provide starting point for Human-Computer Interaction research (re-)examine human-centered lens.

Language: Английский

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