A SWOT analysis of ChatGPT: Implications for educational practice and research

Innovations in Education and Teaching International, Год журнала: 2023, Номер 61(3), С. 460 - 474

Опубликована: Март 27, 2023

ChatGPT is an AI tool that has sparked debates about its potential implications for education. We used the SWOT analysis framework to outline ChatGPT's strengths and weaknesses discuss opportunities threats The include using a sophisticated natural language model generate plausible answers, self-improving capability, providing personalised real-time responses. As such, can increase access information, facilitate complex learning, decrease teaching workload, thereby making key processes tasks more efficient. are lack of deep understanding, difficulty in evaluating quality responses, risk bias discrimination, higher-order thinking skills. Threats education understanding context, threatening academic integrity, perpetuating discrimination education, democratising plagiarism, declining high-order cognitive provide agenda educational practice research times ChatGPT.

Язык: Английский

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Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system

BenchCouncil Transactions on Benchmarks Standards and Evaluations, Год журнала: 2023, Номер 3(2), С. 100115 - 100115

Опубликована: Май 26, 2023

Artificial Intelligence (AI)-based ChatGPT developed by OpenAI is now widely accepted in several fields, including education. Students can learn about ideas and theories using this technology while generating content with it. built on State of the Art (SOA), like Deep Learning (DL), Natural Language Processing (NLP), Machine (ML), an extrapolation a class ML-NLP models known as Large Model (LLMs). It may be used to automate test assignment grading, giving instructors more time concentrate instruction. This utilised customise learning for kids, enabling them focus intently subject matter critical thinking excellent tool language lessons since it translate text from one another. provide lists vocabulary terms meanings, assisting students developing their proficiency resources. Personalised opportunities are ChatGPT's significant applications classroom. might include creating educational resources tailored student's unique interests, skills, goals. paper discusses need features education system. Further, identifies Using ChatGPT, educators design instructional materials specific each requirements skills based current trends. work at speed areas where they most support, resulting effective efficient environment. Both profit significantly Instructors save numerous duties technology. In future, will become powerful enhancing students' teachers' experience.

Язык: Английский

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ChatGPT for healthcare services: An emerging stage for an innovative perspective

BenchCouncil Transactions on Benchmarks Standards and Evaluations, Год журнала: 2023, Номер 3(1), С. 100105 - 100105

Опубликована: Фев. 1, 2023

Generative Pretrained Transformer, often known as GPT, is an innovative kind of Artificial Intelligence (AI) which can produce writing that seems to have been written by a person. OpenAI created this AI language model called ChatGPT. It built using the GPT architecture and trained on large corpus text data respond natural inquiries resemble person's requirements. This technology has lots applications in healthcare. The need for accurate current one major obstacles adopting ChatGPT must access precise up-to-date medical provide trustworthy suggestions treatment options. might be accomplished ensuring used received from reliable sources updated regularly. Since sensitive information would involved, it will also crucial consider privacy security issues while utilising healthcare industry. paper briefs about its healthcare, significant Work Flow Dimensions typical features Healthcare domain. Finally, identified discussed comprehend conversational context contextually appropriate replies. Its effectiveness tool makes useful chatbots, virtual assistants, other applications. However, we see many limitations ethics, interpretation, accountability related privacy. Regarding specialised tasks like creation, translation, categorisation, summarisation, creating conversation systems, pre-trained data, somewhat satisfactory results expected. Moreover, utilised various Natural Language Processing (NLP) activities, including sentiment analysis, part-of-speech tagging, named entity identification.

Язык: Английский

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ChatGPT in healthcare: A taxonomy and systematic review

Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 245, С. 108013 - 108013

Опубликована: Янв. 21, 2024

The recent release of ChatGPT, a chat bot research project/product natural language processing (NLP) by OpenAI, stirs up sensation among both the general public and medical professionals, amassing phenomenally large user base in short time. This is typical example 'productization' cutting-edge technologies, which allows without technical background to gain firsthand experience artificial intelligence (AI), similar AI hype created AlphaGo (DeepMind Technologies, UK) self-driving cars (Google, Tesla, etc.). However, it crucial, especially for healthcare researchers, remain prudent amidst hype. work provides systematic review existing publications on use ChatGPT healthcare, elucidating 'status quo' applications, readers, professionals as well NLP scientists. biomedical literature database PubMed used retrieve published works this topic using keyword 'ChatGPT'. An inclusion criterion taxonomy are further proposed filter search results categorize selected publications, respectively. It found through that current has achieved only moderate or 'passing' performance variety tests, unreliable actual clinical deployment, since not intended applications design. We conclude specialized models trained (bio)medical datasets still represent right direction pursue critical applications.

Язык: Английский

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Comparison of Ophthalmologist and Large Language Model Chatbot Responses to Online Patient Eye Care Questions

JAMA Network Open, Год журнала: 2023, Номер 6(8), С. e2330320 - e2330320

Опубликована: Авг. 22, 2023

Large language models (LLMs) like ChatGPT appear capable of performing a variety tasks, including answering patient eye care questions, but have not yet been evaluated in direct comparison with ophthalmologists. It remains unclear whether LLM-generated advice is accurate, appropriate, and safe for patients.To evaluate the quality ophthalmology generated by an LLM chatbot ophthalmologist-written advice.This cross-sectional study used deidentified data from online medical forum, which questions received responses written American Academy Ophthalmology (AAO)-affiliated A masked panel 8 board-certified ophthalmologists were asked to distinguish between answers human answers. Posts dated 2007 2016; accessed January 2023 analysis was performed March May 2023.Identification on 4-point scale (likely or definitely artificial intelligence [AI] vs likely human) evaluation presence incorrect information, alignment perceived consensus community, likelihood cause harm, extent harm.A total 200 pairs user AAO-affiliated evaluated. The mean (SD) accuracy distinguishing AI 61.3% (9.7%). Of 800 evaluations chatbot-written answers, 168 (21.0%) marked as human-written, while 517 human-written (64.6%) AI-written. Compared more frequently rated probably (prevalence ratio [PR], 1.72; 95% CI, 1.52-1.93). containing inappropriate material comparable (PR, 0.92; 0.77-1.10), did differ terms harm 0.84; 0.67-1.07) nor 0.99; 0.80-1.22).In this AI-generated appeared responding long user-written health posts largely appropriate that significantly deviation ophthalmologist community standards. Additional research needed assess attitudes toward LLM-augmented fully autonomous content generation, clarity acceptability perspective, test performance LLMs greater clinical contexts, determine optimal manner utilizing ethical minimizes harm.

Язык: Английский

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ChatGPT in Healthcare: A Taxonomy and Systematic Review

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Март 30, 2023

Abstract The recent release of ChatGPT, a chat bot research project / product natural language processing (NLP) by OpenAI, stirs up sensation among both the general public and medical professionals, amassing phenomenally large user base in short time. This is typical example ‘productization’ cutting-edge technologies, which allows without technical background to gain firsthand experience artificial intelligence (AI), similar AI hype created AlphaGo (DeepMind Technologies, UK) self-driving cars (Google, Tesla, etc.). However, it crucial, especially for healthcare researchers, remain prudent amidst hype. work provides systematic review existing publications on use ChatGPT healthcare, elucidating ‘status quo’ applications, readers, professionals as well NLP scientists. biomedical literature database PubMed used retrieve published works this topic using keyword ‘ChatGPT’. An inclusion criterion taxonomy are further proposed filter search results categorize selected publications, respectively. It found through that current has achieved only moderate or ‘passing’ performance variety tests, unreliable actual clinical deployment, since not intended applications design. We conclude specialized models trained (bio)medical datasets still represent right direction pursue critical applications.

Язык: Английский

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Comparison of ChatGPT–3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations

Journal of the American Academy of Orthopaedic Surgeons, Год журнала: 2023, Номер unknown

Опубликована: Сен. 4, 2023

Introduction: Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was compare performance orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on assessment examinations. A secondary objective perform a subgroup analysis comparing each group questions that included image interpretation versus text-only Methods: ResStudy question bank used as primary source One hundred eighty choices from nine different subspecialties were directly input into ChatGPT-3.5 then GPT-4. ChatGPT did not consistently available interpretation, so no images provided either AI format. Answers recorded correct incorrect by chatbot, resident based user data ResStudy. Results: Overall, ChatGPT-3.5, GPT-4, ortho scored 29.4%, 47.2%, 74.2%, respectively. There difference among three groups in testing success, with scoring higher than ( P < 0.001 0.001). = 0.002). performed dividing stems without images. more (37.8% vs. 22.4%, respectively, OR 2.1, 0.033) ChatGPT-4 also (61.0% 35.7%, 2.8, 0.001), when Residents 72.6% 75.5% images, significant 0.302). Conclusion: Orthopaedic able accurately is superior for answering Both better It unlikely or would pass American Board Surgery written examination.

Язык: Английский

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The Capability of ChatGPT in Predicting and Explaining Common Drug-Drug Interactions

Cureus, Год журнала: 2023, Номер unknown

Опубликована: Март 17, 2023

Background Drug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they not aware potential between their medications. Many times, patients self-prescribe without knowing DDI. Objective The objective is to investigate the effectiveness ChatGPT, a large language model, in predicting explaining common DDIs. Methods A total 40 DDIs lists were prepared from previously published literature. This list was used converse with ChatGPT two-stage question. first question asked as "can I take X Y together?" two names. After storing output, next asked. second "why should output stored further analysis. responses checked by pharmacologists consensus categorized "correct" "incorrect." ones classified "conclusive" "inconclusive." text reading ease scores grades education required understand text. Data tested descriptive inferential statistics. Results Among DDI pairs, one answer incorrect correct answers, 19 conclusive 20 inconclusive. For question, wrong. 17 22 mean Flesch score 27.64±10.85 answers 29.35±10.16 p = 0.47. Flesh-Kincaid grade level 15.06±2.79 14.85±1.97 0.69. When we compared levels hypothetical 6th grade, significantly higher than expected (t 20.57, < 0.0001 t 28.43, answers). Conclusion partially effective tool Patients, immediate access healthcare facility getting information about DDIs, help ChatGPT. However, on several occasions, it provide incomplete guidance. Further improvement usage ideas

Язык: Английский

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Assessing the Capability of ChatGPT in Answering First- and Second-Order Knowledge Questions on Microbiology as per Competency-Based Medical Education Curriculum

Cureus, Год журнала: 2023, Номер unknown

Опубликована: Март 12, 2023

Background and objective ChatGPT is an artificial intelligence (AI) language model that has been trained to process respond questions across a wide range of topics. It also capable solving problems in medical educational However, the capability accurately answer first- second-order knowledge field microbiology not explored so far. Hence, this study, we aimed analyze answering on subject microbiology. Materials methods Based competency-based education (CBME) curriculum microbiology, prepared set first-order questions. For total eight modules CBME for six according National Medical Commission-recommended curriculum, amounting (8 x 12) 96 The were checked content validity by three expert microbiologists. These used converse with single user responses recorded further analysis. answers scored microbiologists rating scale 0-5. average scores was taken as final score As data normally distributed, non-parametric statistical test. overall tested one-sample median test hypothetical values 4 5. compared Mann-Whitney U Module-wise Kruskall-Wallis followed post hoc pairwise comparisons. Results 4.04 ±0.37 (median: 4.17, Q1-Q3: 3.88-4.33) mean being 4.07 ±0.32 4-4.33) 3.99 ±0.43 4, 3.67-4.33) (Mann-Whitney p=0.4). significantly below 5 (one-sample p<0.0001) similar p=0.09). Overall, there variation obtained categories topics indicating inconsistent performance different Conclusion results study indicate both related achieved accuracy approximately 80% no difference between model's findings suggest potential be effective tool automated question-answering continued improvement training development models necessary enhance their make them suitable academic use.

Язык: Английский

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How to harness the potential of ChatGPT in education?

Knowledge Management & E-Learning An International Journal, Год журнала: 2023, Номер unknown, С. 133 - 152

Опубликована: Май 19, 2023

Technological advancements, particularly in the field of artificial intelligence (AI) have played an increasingly important role transforming education. More recently, ground-breaking AI applications like ChatGPT demonstrated potential to bring radical changes educational landscape due their capability understand complex questions, generate plausible responses and human-like writing, assist with completion tasks. However, has limitations quality its output, such as inclusion inaccurate, fabricated biased information lack critical thinking in-depth understanding. The combinations these capabilities along external factors (e.g., growing demand for personalized learning support, irresponsible unethical use AI) presents a range opportunities challenges This paper thorough SWOT (strength, weakness, opportunity, threat) analysis ChatGPT, based on which we propose how can be properly integrated into teaching practice harness

Язык: Английский

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