Balancing promise and peril: AI’s role in transforming science

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access).

Getting AI Right: Introductory Notes on AI &amp; Society

Accessible generative artificial intelligence (AI) tools like large-language models (LLMs) (e.g., Chat-GPT, 1 Minerva 2 ) are raising a flurry of questions about the potential and implications of generative algorithms and the ethical use of AI-generated text in a variety of contexts, including open science (Bugbee & Ramachandran, 2023), student assessment (Heidt, 2023), and medicine (Harrer, 2023) . Similarly, among the graphic and visual arts communities, the use of generative image synthesis algorithms (e.g., DALL-E, 3 Midjourney, 4 Stable Diffusion 5 ) that take text prompts as input and produce works in the style of a particular human artist, or no artist who ever lived, are causing consternation and posing challenging questions (Murphy, 2022; Plunkett, 2022) . The use of generative AI to create deep fakes has also been in the spotlight (Ruiter, 2021), as has its role in answering scientific research questions directly (Castelvecchi, 2023) . To our minds, the questions these technologies are raising do not seem to be of a fundamentally different character to questions asked about AI for many years. They largely concern (a) what is possible, (b) what is right, and (c) the implications of the technology's use. For instance,

Parkinson’s disease and Alzheimer’s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.

The emergence of generative artificial intelligence (GenAI) offers the potential to enhance health economics and outcomes research (HEOR) by streamlining traditionally time-consuming and labour-intensive tasks, such as literature reviews, data extraction, and economic modelling. To effectively navigate this evolving landscape, health economists need a foundational understanding of how GenAI can complement their work. This primer aims to introduce health economists to the essentials of using GenAI tools, particularly large language models (LLMs), in HEOR projects. For health economists new to GenAI technologies, chatbot interfaces like ChatGPT offer an accessible way to explore the potential of LLMs. For more complex projects, knowledge of application programming interfaces (APIs), which provide scalability and integration capabilities, and prompt engineering strategies, such as few-shot and chain-of-thought prompting, is necessary to ensure accurate and efficient data analysis, enhance model performance, and tailor outputs to specific HEOR needs. Retrieval-augmented generation (RAG) can further improve LLM performance by incorporating current external information. LLMs have significant potential in many common HEOR tasks, such as summarising medical literature, extracting structured data, drafting report sections, generating statistical code, answering specific questions, and reviewing materials to enhance quality. However, health economists must also be aware of ongoing limitations and challenges, such as the propensity of LLMs to produce inaccurate information ('hallucinate'), security concerns, issues with reproducibility, and the risk of bias. Implementing LLMs in HEOR requires robust security protocols to handle sensitive data in compliance with the European Union's General Data Protection Regulation (GDPR) and the United States' Health Insurance Portability and Accountability Act (HIPAA). Deployment options such as local hosting, secure API use, or cloud-hosted open-source models offer varying levels of control and cost, each with unique trade-offs in security, accessibility, and technical demands. Reproducibility and transparency also pose unique challenges. To ensure the credibility of LLM-generated content, explicit declarations of the model version, prompting techniques, and benchmarks against established standards are recommended. Given the 'black box' nature of LLMs, a clear reporting structure is essential to maintain transparency and validate outputs, enabling stakeholders to assess the reliability and accuracy of LLM-generated HEOR analyses. The ethical implications of using artificial intelligence (AI) in HEOR, including LLMs, are complex and multifaceted, requiring careful assessment of each use case to determine the necessary level of ethical scrutiny and transparency. Health economists must balance the potential benefits of AI adoption against the risks of maintaining current practices, while also considering issues such as accountability, bias, intellectual property, and the broader impact on the healthcare system. As LLMs and AI technologies advance, their potential role in HEOR will become increasingly evident. Key areas of promise include creating dynamic, continuously updated HEOR materials, providing patients with more accessible information, and enhancing analytics for faster access to medicines. To maximise these benefits, health economists must understand and address challenges such as data ownership and bias. The coming years will be critical for establishing best practices for GenAI in HEOR. This primer encourages health economists to adopt GenAI responsibly, balancing innovation with scientific rigor and ethical integrity to improve healthcare insights and decision-making.

Generative artificial intelligence and large language models in smart healthcare applications: Current status and future perspectives.

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) like ChatGPT, Claude, and Gemini, have prompted questions about their proximity to artificial general intelligence (AGI). This quantitative study compares LLMs’ performance on educational benchmarks. A quantitative research methodology and secondary exploratory analysis were used to test the proposed hypothesis, stating that current LLMs, including ChatGPT, Claude, and Gemini, possess AGI by comparing their educational metric scores to public education standards. This study used an ex-post research design, whereby secondary data from authoritative sources were collected to compare educational achievements and human literacy levels with the AI model’s performance on similar tasks. The results show that LLMs significantly outperform human benchmarks in tasks such as undergraduate knowledge and advanced reading comprehension (ARC), indicating substantial progress toward AGI.

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

As generative artificial intelligence (AI) tools and large language models (LLMs)-powered applications develop rapidly in the era of algorithms, it should be integrated thoughtfully to enhance English as a Foreign Language (EFL) teaching and learning without replacing learners’ critical thinking (CT). This study systematically analyzes the impact of generative AI tools and LLMs on language learners’ CT in EFL education using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies from 2022 to 2025. A thorough review of 15 selected studies focuses on generative AI tools and LLMs’ dual nature, research methods, main focuses, theory and models, limitations and challenges, and future directions in the field based on Web of Science (WoS), SCOPUS, ERIC, ProQuest, and Google Scholar. The findings identified generative AI tools and LLMs possessed both the potential to nurture and the risk of hindering CT in EFL education. 66.67% of studies reported generative AI tools and LLMs’ positive role in CT, while 33.33% of studies reported its negative role in CT. Furthermore, 3 types of research methods, 3 key themes of research focus, and 4 groups of theoretical perspectives were examined. However, 4 kinds of limitations in this field remain, including research scope, user dependency, generative AI reliability, and pedagogical integration. Future research can focus on assessing long-term effects, broadening research scope, promoting responsible AI use, and refining pedagogical strategies. Finally, Limitations, implications and future direction of this study were discussed.

This paper introduces a new field of AI research called machine unlearning and examines the challenges and approaches to extend machine unlearning to generative AI (GenAI). Machine unlearning is a model-driven approach to make an existing artificial intelligence (AI) model unlearn a set of data from its learning. Machine unlearning is becoming important for businesses to comply with privacy laws such as General Data Protection Regulation (GDPR) customer’s right to be forgotten, to manage security and to remove bias that AI models learn from their training data, as it is expensive to retrain and deploy the models without the bias or security or privacy compromising data. This paper presents the state of the art in machine unlearning approaches such as exact unlearning, approximate unlearning, zero-shot learning (ZSL) and fast and efficient unlearning. The paper highlights the challenges in applying machine learning to GenAI which is built on a transformer architecture of neural networks and adds more opaqueness to how large language models (LLM) learn in pre-training, fine-turning, transfer learning to more languages and in inference. The paper elaborates on how models retain the learning in a neural network to guide the various machine unlearning approaches for GenAI that the authors hope can be built upon their work. The paper suggests possible futuristic directions of research to create transparency in LLM and particularly looks at hallucinations in LLMs when they are extended to do machine translation for new languages beyond their training with ZSL to shed light on how the model stores its learning of newer languages in its memory and how it draws upon it during inference in GenAI applications. Finally, the paper calls for collaborations for future research in machine unlearning for GenAI, particularly LLMs, to add transparency and inclusivity to language AI.

The advent of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to evaluate the accuracy and reliability of these LLMs in evaluating the achievement of learning outcomes across different cognitive domains in a scientific inquiry course on sports physiology. Human graders and three LLMs, GPT-3.5, GPT-4o, and Gemini, were tasked with scoring submitted student assignments according to a set of rubrics aligned with various cognitive domains, namely "Understand," "Analyze," and "Evaluate" from the revised Bloom's taxonomy and "Scientific Inquiry Competency." Our findings revealed that while LLMs demonstrated some level of competency, they do not yet meet the assessment standards of human graders. Specifically, interrater reliability (percentage agreement and correlation analysis) between human graders was superior as compared to between two grading rounds for each LLM, respectively. Furthermore, concordance and correlation between human and LLM graders were mostly moderate to poor in terms of overall scores and across the pre-specified cognitive domains. The results suggest a future where AI could complement human expertise in educational assessment but underscore the importance of adaptive learning by educators and continuous improvement in current AI technologies to fully realize this potential.NEW & NOTEWORTHY The advent of large language models (LLMs) such as ChatGPT and Gemini has offered new learning and assessment opportunities to integrate artificial intelligence (AI) with education. This study evaluated the accuracy of LLMs in assessing an assignment from a course on sports physiology. Concordance and correlation between human graders and LLMs were mostly moderate to poor. The findings suggest AI's potential to complement human expertise in educational assessment alongside the need for adaptive learning by educators.

Ecoliteracy in informal science education settings

Artificial intelligence (AI), particularly generative AI (GenAI) and large language models (LLMs), is transforming scientific research and higher education, offering new opportunities while raising significant ethical, legal, and regulatory challenges. This opinion piece explores the intersection of AI and science, focusing on the implications for copyright, peer review, and open science. AI systems, such as LLMs, are increasingly used in research applications, including text generation, data analysis, and peer review, with recent studies suggesting that AI-assisted reviews may improve efficiency and address reviewer shortages. However, concerns about bias, confidentiality, and the lack of guidelines for AI use in peer review persist. The rise of AI also poses challenges to copyright, as LLMs often rely on vast datasets of scientific works, raising questions about fair use, attribution, and licensing. Current regulatory frameworks in the United States, China, the European Union, and the United Kingdom focus on promoting innovation and responsible AI development, but gaps remain, particularly in addressing the use of copyrighted works for AI training. Creative Commons licenses, widely used for open-access outputs, do not fully address the complexities of AI training, and the absence of proper attribution in AI systems challenges the concept of originality. This paper calls for action to ensure that AI training is not considered a fair use exception to copyright law, advocating for authors' rights to refuse the use of their works for AI training and for universities to take a leading role in regulating AI. Governments and international organizations must develop harmonized legislative measures to protect authors' rights and ensure transparency in AI training datasets. The paper concludes that while AI offers transformative potential for science, a careful and responsible approach is needed to balance innovation with ethical and legal considerations, preventing the emergence of an oligopolistic market that prioritizes profit over scientific integrity.