The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review

Smart Learning Environments, Год журнала: 2024, Номер 11(1)

Опубликована: Июнь 18, 2024

Abstract The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance learning aids. Despite examination ethical concerns associated with these technologies, there is a noticeable gap in investigations on how issues AI contribute to students’ over-reliance systems, such affects cognitive abilities. Overreliance occurs when users accept AI-generated recommendations without question, leading errors task performance context decision-making. This typically arises individuals struggle assess reliability or much trust place its suggestions. systematic review investigates particularly those embedded generative models for academic learning, their critical capabilities including decision-making, thinking, analytical reasoning. By using Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, our evaluated body literature addressing contributing factors effects contexts. comprehensive spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, Web Science. Our findings indicate that stemming impacts abilities, as increasingly favor fast optimal solutions over slow ones constrained by practicality. tendency explains why prefer efficient shortcuts, heuristics, even amidst presented technologies.

Язык: Английский

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Large Language Models: A Guide for Radiologists

Korean Journal of Radiology, Год журнала: 2024, Номер 25(2), С. 126 - 126

Опубликована: Янв. 1, 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based optimize efficiency radiologists in terms professional work and research endeavors. Importantly, these are a trajectory rapid evolution, wherein challenges "hallucination," high training cost, issues addressed, along with inclusion multimodal inputs. In this review, we aim offer conceptual knowledge actionable guidance interested utilizing through succinct overview topic summary radiology-specific aspects, beginning potential future directions.

Язык: Английский

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The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI

Japanese Journal of Radiology, Год журнала: 2024, Номер 42(7), С. 685 - 696

Опубликована: Март 29, 2024

Abstract The advent of Deep Learning (DL) has significantly propelled the field diagnostic radiology forward by enhancing image analysis and interpretation. introduction Transformer architecture, followed development Large Language Models (LLMs), further revolutionized this domain. LLMs now possess potential to automate refine workflow, extending from report generation assistance in diagnostics patient care. integration multimodal technology with could potentially leapfrog these applications unprecedented levels. However, come unresolved challenges such as information hallucinations biases, which can affect clinical reliability. Despite issues, legislative guideline frameworks have yet catch up technological advancements. Radiologists must acquire a thorough understanding technologies leverage LLMs’ fullest while maintaining medical safety ethics. This review aims aid that endeavor.

Язык: Английский

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The Potential Applications and Challenges of ChatGPT in the Medical Field

International Journal of General Medicine, Год журнала: 2024, Номер Volume 17, С. 817 - 826

Опубликована: Март 1, 2024

ChatGPT, an AI-driven conversational large language model (LLM), has garnered significant scholarly attention since its inception, owing to manifold applications in the realm of medical science. This study primarily examines merits, limitations, anticipated developments, and practical ChatGPT clinical practice, healthcare, education, research. It underscores necessity for further research development enhance performance deployment. Moreover, future avenues encompass ongoing enhancements standardization mitigating exploring integration applicability translational personalized medicine. Reflecting narrative nature this review, a focused literature search was performed identify relevant publications on ChatGPT's use process aimed at gathering broad spectrum insights provide comprehensive overview current state prospects domain. The objective is aid healthcare professionals understanding groundbreaking advancements associated with latest artificial intelligence tools, while also acknowledging opportunities challenges presented by ChatGPT.

Язык: Английский

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Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Korean Journal of Radiology, Год журнала: 2024, Номер 25(3), С. 224 - 224

Опубликована: Янв. 1, 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application generative artificial intelligence (AI) models medical field. This review summarizes different AI and their potential applications field medicine explores evolving landscape Adversarial Networks diffusion since introduction models. These have made valuable contributions to radiology. Furthermore, this also significance synthetic data addressing privacy concerns augmenting diversity quality within domain, addition emphasizing role inversion investigation outlining an approach replicate process. We provide overview Large Language Models, such as GPTs bidirectional encoder representations (BERTs), that focus on prominent representatives discuss recent initiatives involving language-vision radiology, including innovative large language vision assistant for biomedicine (LLaVa-Med), illustrate practical application. comprehensive offers insights into wide-ranging clinical research emphasizes transformative potential.

Язык: Английский

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Techniques for supercharging academic writing with generative AI

Nature Biomedical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Март 18, 2024

Язык: Английский

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Generative artificial intelligence in graduate medical education

Frontiers in Medicine, Год журнала: 2025, Номер 11

Опубликована: Янв. 10, 2025

Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potential opportunities risks of GenAI in graduate medical education (GME). We review existing literature provide commentary on how could impact GME, five key areas opportunity: electronic health record (EHR) workload reduction, clinical simulation, individualized education, research analytics support, decision support. then discuss significant risks, inaccuracy overreliance AI-generated content, challenges to authenticity academic integrity, biases AI outputs, privacy concerns. As technology matures, it will likely come have an important role future but its integration should be guided by a thorough understanding both benefits limitations.

Язык: Английский

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A manifesto for a globally diverse, equitable, and inclusive open science

Communications Psychology, Год журнала: 2025, Номер 3(1)

Опубликована: Янв. 29, 2025

The field of psychology has rapidly transformed its open science practices in recent years. Yet there been limited progress integrating principles diversity, equity and inclusion. In this Perspective, we raise the spectre Questionable Generalisability Practices issue MASKing (Making Assumptions based on Skewed Knowledge), calling for more responsible generalising study findings co-authorship to promote global knowledge production. To drive change, researchers must target all four key components research process: design, reporting, generalisation, evaluation. Additionally, macro-level geopolitical factors be considered move towards a robust behavioural that is truly inclusive, representing voices experiences majority world (i.e., low-and-middle-income countries). Psychology embrace evaluation counteract Knowledge).

Язык: Английский

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How well do large language model-based chatbots perform in oral and maxillofacial radiology?

Dentomaxillofacial Radiology, Год журнала: 2024, Номер 53(6), С. 390 - 395

Опубликована: Июнь 7, 2024

Abstract Objectives This study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those dental students on an oral and maxillofacial radiology examination. Methods ChatGPT, ChatGPT Plus, Bard, Bing Chat were tested 52 questions from regular college examinations. These categorized into three educational content areas: basic knowledge, imaging equipment, image interpretation. They also classified as multiple-choice (MCQs) short-answer (SAQs). The accuracy rates compared students, further analysis was conducted based question type. Results students’ overall rate 81.2%, while that varied: 50.0% for 65.4% 63.5% Chat. Plus achieved a higher knowledge than (93.8% vs. 78.7%). However, all performed poorly in interpretation, below 35.0%. All scored less 60.0% MCQs, but better SAQs. Conclusions unsatisfactory. Further training using specific, relevant data derived solely reliable sources is required. Additionally, validity these chatbots’ responses must be meticulously verified.

Язык: Английский

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ChatGPT’s scorecard after the performance in a series of tests conducted at the multi-country level: A pattern of responses of generative artificial intelligence or large language models

Current Research in Biotechnology, Год журнала: 2024, Номер 7, С. 100194 - 100194

Опубликована: Янв. 1, 2024

Recently, researchers have shown concern about the ChatGPT-derived answers. Here, we conducted a series of tests using ChatGPT by individual researcher at multi-country level to understand pattern its answer accuracy, reproducibility, length, plagiarism, and in-depth two questionnaires (the first set with 15 MCQs second KBQ). Among MCQ-generated answers, 13 ± 70 were correct (Median : 82.5; Coefficient variance 4.85), 3 0.77 incorrect (Median: 3, variance: 25.81), 1 10 reproducible, 11 not. KBQ, length each question (in words) is 294.5 97.60 (mean range varies from 138.7 438.09), mean similarity index 29.53 11.40 (Coefficient 38.62) for question. The statistical models also developed analyzed parameters study shows ChatGPT-derive answers correctness incorrectness urges an error-free, next-generation LLM avoid users' misguidance.

Язык: Английский

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