Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

International Journal of Educational Technology in Higher Education, Год журнала: 2023, Номер 20(1)

Опубликована: Июль 16, 2023

Abstract This study explores university students’ perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, effective integration. A survey 399 undergraduate postgraduate students from various disciplines Hong Kong revealed a generally positive attitude towards GenAI teaching learning. Students recognized the for personalized learning support, writing brainstorming assistance, research analysis capabilities. However, concerns about accuracy, privacy, ethical issues, impact personal development, career prospects, societal values were also expressed. According John Biggs’ 3P model, student significantly influence approaches outcomes. By understanding perceptions, educators policymakers can tailor technologies address needs while promoting Insights this inform policy development around integration into education. addressing concerns, create well-informed guidelines strategies responsible implementation tools, ultimately enhancing experiences

Язык: Английский

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A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage

Опубликована: Июль 10, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Язык: Английский

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Generative AI in healthcare: an implementation science informed translational path on application, integration and governance

Implementation Science, Год журнала: 2024, Номер 19(1)

Опубликована: Март 15, 2024

Abstract Background Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative capable of generating new data such text images, holds promise enhancing patient care, revolutionizing disease diagnosis expanding treatment options. However, utility impact AI healthcare remain poorly understood, concerns around ethical medico-legal implications, integration into service delivery workforce utilisation. Also, there is not clear pathway implement integrate delivery. Methods This article aims provide comprehensive overview use focusing on technology its translational application highlighting need for careful planning, execution management expectations adopting medicine. Key considerations include factors privacy, security irreplaceable role clinicians’ expertise. Frameworks like acceptance model (TAM) Non-Adoption, Abandonment, Scale-up, Spread Sustainability (NASSS) are considered promote responsible integration. These frameworks allow anticipating proactively addressing barriers adoption, facilitating stakeholder participation responsibly transitioning care systems harness AI’s potential. Results transform through automated systems, enhanced democratization expertise diagnostic support tools providing timely, personalized suggestions. applications across billing, diagnosis, research can also make more efficient, equitable effective. necessitates meticulous change risk mitigation strategies. Technological capabilities alone cannot shift complex ecosystems overnight; rather, structured adoption programs grounded implementation science imperative. Conclusions It strongly argued this that usher tremendous progress, if introduced responsibly. Strategic based science, incremental deployment balanced messaging opportunities versus limitations helps safe, Extensive real-world piloting iteration aligned priorities should drive development. With conscientious governance centred human wellbeing over technological novelty, enhance accessibility, affordability quality care. As these models continue advancing rapidly, ongoing reassessment transparent communication their strengths weaknesses vital restoring trust, realizing positive and, most importantly, improving

Язык: Английский

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Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration

Healthcare, Год журнала: 2023, Номер 11(20), С. 2776 - 2776

Опубликована: Окт. 20, 2023

Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data information management in healthcare medicine. However, there is scant literature guiding their integration non-AI professionals. This study conducts a scoping review to address the critical need guidance on integrating generative AI LLMs into medical practices. It elucidates distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning chain-of-thought reasoning, which differentiates them traditional, rule-based systems. requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, clinicians consumers, achieve benefits. Although global research examining both opportunities challenges, ethical legal dimensions, offer advancements enhancing management, retrieval, decision-making processes. Continued innovation acquisition, model fine-tuning, prompt strategy development, evaluation, system implementation imperative realizing full potential of technologies. Organizations should proactively engage with technologies improve quality, safety, efficiency, adhering guidelines responsible application.

Язык: Английский

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Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers

Digital Health, Год журнала: 2023, Номер 9

Опубликована: Янв. 1, 2023

The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently contributing to improved diagnostic accuracy, optimized treatment planning, patient outcomes. rapid evolution AI, especially generative AI large language models (LLMs), have reignited the discussions about their potential impact on healthcare industry, particularly regarding role providers. Concerning questions, “can replace doctors?” “will doctors who are using those not it?” been echoed. To shed light this debate, article focuses emphasizing augmentative healthcare, underlining that aimed complement, rather than replace, fundamental solution emerges with human–AI collaboration, which combines cognitive strengths providers analytical capabilities AI. A human-in-the-loop (HITL) approach ensures systems guided, communicated, supervised by human expertise, thereby maintaining safety quality services. Finally, adoption can be forged further organizational process informed HITL improve multidisciplinary teams loop. create a paradigm shift complementing enhancing skills providers, ultimately leading service quality, outcomes, more efficient system.

Язык: Английский

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Artificial Intelligence and Public Health: Evaluating ChatGPT Responses to Vaccination Myths and Misconceptions

Vaccines, Год журнала: 2023, Номер 11(7), С. 1217 - 1217

Опубликована: Июль 7, 2023

Artificial intelligence (AI) tools, such as ChatGPT, are the subject of intense debate regarding their possible applications in contexts health care. This study evaluates Correctness, Clarity, and Exhaustiveness answers provided by ChatGPT on topic vaccination. The World Health Organization's 11 "myths misconceptions" about vaccinations were administered to both free (GPT-3.5) paid version (GPT-4.0) ChatGPT. AI tool's responses evaluated qualitatively quantitatively, reference those myth misconceptions WHO, independently two expert Raters. agreement between Raters was significant for versions (p K < 0.05). Overall, easy understand 85.4% accurate although one questions misinterpreted. Qualitatively, GPT-4.0 superior GPT-3.5 terms (Δ = 5.6%, 17.9%, 9.3%, respectively). shows that, if appropriately questioned, tools can represent a useful aid care field. However, when consulted non-expert users, without support medical advice, these not from risk eliciting misleading responses. Moreover, given existing social divide information access, improved accuracy raises further ethical issues.

Язык: Английский

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Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e53008 - e53008

Опубликована: Март 8, 2024

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding potential uses generative AI health care becomes increasingly important. Generative AI, including models such as adversarial networks large language models, shows promise transforming medical diagnostics, research, treatment planning, patient care. However, these data-intensive systems pose new threats protected information. This Viewpoint paper aims explore various categories care, drug discovery, virtual assistants, clinical decision support, while identifying security privacy within each phase life cycle (ie, data collection, model development, implementation phases). The objectives this study were analyze current state identify opportunities challenges posed by integrating technologies into existing infrastructure, propose strategies for mitigating risks. highlights importance addressing associated with ensure safe effective use systems. findings can inform development future help organizations better understand benefits risks By examining cases across diverse domains contributes theoretical discussions surrounding ethics, vulnerabilities, regulations. In addition, provides practical insights stakeholders looking adopt solutions their organizations.

Язык: Английский

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ChatGPT: promise and challenges for deployment in low- and middle-income countries

The Lancet Regional Health - Western Pacific, Год журнала: 2023, Номер 41, С. 100905 - 100905

Опубликована: Сен. 15, 2023

In low- and middle-income countries (LMICs), the fields of medicine public health grapple with numerous challenges that continue to hinder patients' access healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as potential tool in aiding efforts LMICs. This viewpoint details benefits employing ChatGPT LMICs improve encompassing broad spectrum domains ranging from literacy, screening, triaging, remote support, mental multilingual capabilities, communication documentation, medical training education, support for professionals. Additionally, we also share concerns limitations associated use provide balanced discussion on opportunities using

Язык: Английский

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Effects of Generative Chatbots in Higher Education

Information, Год журнала: 2023, Номер 14(9), С. 492 - 492

Опубликована: Сен. 7, 2023

Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to challenge of providing personalized learning experiences students, these can increase workload instructors due maintenance updates required keep courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology help overcome disadvantages by transforming pedagogical activities guiding both students interactively. this study, we explore compare main characteristics existing educational chatbots. Then, propose a new theoretical framework blended with intelligent integration enabling interact online create manage their using AI tools. The advantages proposed are as follows: (1) it provides comprehensive understanding transformative potential in education facilitates effective implementation; (2) offers holistic methodology enhance overall experience; (3) unifies applications teaching–learning within universities.

Язык: Английский

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Exploring the potential of ChatGPT as a supplementary tool for providing orthopaedic information

Knee Surgery Sports Traumatology Arthroscopy, Год журнала: 2023, Номер 31(11), С. 5190 - 5198

Опубликована: Авг. 8, 2023

To investigate the potential use of large language models (LLMs) in orthopaedics by presenting queries pertinent to anterior cruciate ligament (ACL) surgery generative pre-trained transformer (ChatGPT, specifically using its GPT-4 model March 14th 2023). Additionally, this study aimed evaluate depth LLM's knowledge and adaptability different user groups. It was hypothesized that ChatGPT would be able adapt target groups due strong understanding processing capabilities.

Язык: Английский

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