Search

Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom. [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems has published a special section on cognitive reapportionment in relation to artificial intelligence (AI) and advances in computing. The section explores the allocation of tasks between humans and machines as AI becomes more capable of cognitive tasks. The limitations of current AI systems are discussed, as well as the potential for collaboration between humans and AI. The journal also includes papers on topics such as knowledge-aware models, crowdsourcing, social media effects, and the impact of government contracting on high-tech firms. [Extracted from the article]

Cephalo is presented as a series of multimodal vision large language models (V‐LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and science‐focused Wikipedia data demonstrates it can interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The combination of a vision encoder with an autoregressive transformer supports multimodal natural language understanding, which can be coupled with other generative methods to create an image‐to‐text‐to‐3D pipeline. To develop more capable models from smaller ones, both mixture‐of‐expert methods and model merging are reported. The models are examined in diverse use cases that incorporate biological materials, fracture and engineering analysis, protein biophysics, and bio‐inspired design based on insect behavior. Generative applications include bio‐inspired designs, including pollen‐inspired architected materials, as well as the synthesis of bio‐inspired material microstructures from a photograph of a solar eclipse. Additional model fine‐tuning with a series of molecular dynamics results demonstrate Cephalo's enhanced capabilities to accurately predict statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials. [ABSTRACT FROM AUTHOR]

This research involved designing and building an interactive generative AI application to conduct a comparative analysis of two advanced Large Language Models (LLMs), GPT‐4, and Claude 2, using Langsmith evaluation tools. The project was developed to explore the potential of LLMs in facilitating postgraduate course recommendations within a simulated environment at Munster Technological University (MTU). Designed for comparative analysis, the application enables testing of GPT‐4 and Claude 2 and can be hosted flexibly on either Amazon Web Services (AWS) or Azure. It utilizes advanced natural language processing and retrieval‐augmented generation (RAG) techniques to process proprietary data tailored to postgraduate needs. A key component of this research was the rigorous assessment of the LLMs using the Langsmith evaluation tool against both customized and standard benchmarks. The evaluation focused on metrics such as bias, safety, accuracy, cost, robustness, and latency. Additionally, adaptability covering critical features like language translation and internet access, was independently researched since the Langsmith tool does not evaluate this metric. This ensures a holistic assessment of the LLM's capabilities. [ABSTRACT FROM AUTHOR]

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. Large language models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of large language models to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high- and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing. [ABSTRACT FROM AUTHOR]

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM‐based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities. Building on prior work that mapped the traditional machine learning life cycle, we provide a framework of the LLM life cycle from the initial development of LLMs to customizing pre‐trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM‐generated text (eg, tutoring conversations) because text encodings are high‐dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. The proposed framework clarifies the complex nature of bias in LLM applications and provides practical guidance for their evaluation to promote educational equity. Practitioner notesWhat is already known about this topic The life cycle of traditional machine learning (ML) applications which focus on predicting labels is well understood.Biases are known to enter in traditional ML applications at various points in the life cycle, and methods to measure and mitigate these biases have been developed and tested.Large language models (LLMs) and other forms of generative artificial intelligence (GenAI) are increasingly adopted in education technologies (EdTech), but current evaluation approaches are not specific to the domain of education.What this paper adds A holistic perspective of the LLM life cycle with domain‐specific examples in education to highlight opportunities and challenges for incorporating natural language understanding (NLU) and natural language generation (NLG) into EdTech.Potential sources of bias are identified in each step of the LLM life cycle and discussed in the context of education.A framework for understanding where to expect potential harms of LLMs for students, teachers, and other users of GenAI technology in education, which can guide approaches to bias measurement and mitigation.Implications for practice and/or policy Education practitioners and policymakers should be aware that biases can originate from a multitude of steps in the LLM life cycle, and the life cycle perspective offers them a heuristic for asking technology developers to explain each step to assess the risk of bias.Measuring the biases of systems that use LLMs in education is more complex than with traditional ML, in large part because the evaluation of natural language generation is highly context‐dependent (eg, what counts as good feedback on an assignment varies).EdTech developers can play an important role in collecting and curating datasets for the evaluation and benchmarking of LLM applications moving forward. [ABSTRACT FROM AUTHOR]

The adoption of large language models (LLMs) in education holds much promise. However, like many technological innovations before them, adoption and access can often be inequitable from the outset, creating more divides than they bridge. In this paper, we explore the magnitude of the country and language divide in the leading open‐source and proprietary LLMs with respect to knowledge of K‐12 taxonomies in a variety of countries and their performance on tagging problem content with the appropriate skill from a taxonomy, an important task for aligning open educational resources and tutoring content with state curricula. We also experiment with approaches to narrowing the performance divide by enhancing LLM skill tagging performance across four countries (the USA, Ireland, South Korea and India–Maharashtra) for more equitable outcomes. We observe considerable performance disparities not only with non‐English languages but with English and non‐US taxonomies. Our findings demonstrate that fine‐tuning GPT‐3.5 with a few labelled examples can improve its proficiency in tagging problems with relevant skills or standards, even for countries and languages that are underrepresented during training. Furthermore, the fine‐tuning results show the potential viability of GPT as a multilingual skill classifier. Using both an open‐source model, Llama2‐13B, and a closed‐source model, GPT‐3.5, we also observe large disparities in tagging performance between the two and find that fine‐tuning and skill information in the prompt improve both, but the closed‐source model improves to a much greater extent. Our study contributes to the first empirical results on mitigating disparities across countries and languages with LLMs in an educational context. Practitioner notesWhat is already known about this topic Recent advances in generative AI have led to increased applications of LLMs in education, offering diverse opportunities.LLMs excel predominantly in English and exhibit a bias towards the US context.Automated content tagging has been studied using English‐language content and taxonomies.What this paper adds Investigates the country and language disparities in LLMs concerning knowledge of educational taxonomies and their performance in tagging content.Presents the first empirical findings on addressing disparities in LLM performance across countries and languages within an educational context.Improves GPT‐3.5's tagging accuracy through fine‐tuning, even for non‐US countries, starting from zero accuracy.Extends automated content tagging to non‐English languages using both open‐source and closed‐source LLMs.Implications for practice and/or policy Underscores the importance of considering the performance generalizability of LLMs to languages other than English.Highlights the potential viability of ChatGPT as a skill tagging classifier across countries. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Pedagogy in Health Promotion" celebrating its 10-year anniversary. The editorial highlights promising methods and practices for educators in the field of health promotion and public health. The article identifies five key areas of pedagogical practices: active learning and critical reflection methods, holistic and student-centered pedagogical activities, mentoring and anti-oppression oriented teaching approaches, resources to address mis/disinformation, and the inclusion and evaluation of Artificial Intelligence (AI) in public health pedagogy. The editorial provides specific examples and recommendations from articles published in the journal to guide educators in implementing innovative and effective teaching techniques. [Extracted from the article]

This document is a guest editorial from the Journal of Documentation, discussing the use of artificial intelligence (AI) in the analysis and accessibility of digital cultural heritage materials. The special issue focuses on how AI can make cultural heritage collections more accessible and how it can be used for analysis. The articles in the issue cover a range of topics, including metadata creation, archival practice, archaeological fieldwork, and more. The editorial also provides context for the special issue, explaining that it is part of the AEOLIAN project, funded by the National Endowment for the Humanities and the Arts and Humanities Research Council. The project has produced workshops, case studies, blog posts, and interviews on the topic of AI in cultural heritage. The document concludes by highlighting the diverse range of articles in the special issue, which cover various fields, methodologies, and AI approaches. The article discusses the application of artificial intelligence (AI) in the field of cultural heritage preservation. It explores different approaches to preserving and making accessible various forms of cultural content, including audiovisual materials, born-digital materials, and ancient cultural practices such as martial arts. The authors highlight the challenges and opportunities of using AI in these contexts, emphasizing the importance of multidisciplinary methods and community involvement. They also caution against the overreliance on AI and emphasize the need for ongoing improvement and critical evaluation of AI technologies. Overall, the article presents a thoughtful and nuanced perspective on the use of AI in cultural heritage preservation. [Extracted from the article]

The pace of technological advancement over the last three decades has led to a slew of new companies adopting the latest technologies and marrying them to innovative new business models to threaten more traditional businesses. Start-ups such as Google, Meta and Amazon, to name a few, have revolutionised the way consumers engage with service providers, consume information and purchase goods. Fintech start-ups have also threatened to change the way financial services are provided, albeit with varying degrees of success due to barriers such as consumer trust in new brands, regulatory compliance and the financial strength of banks to build those same services internally. There is no denying, however, that custodian banks today face many challenges that are slowly eroding margins. Regulators are demanding shorter settlement times, clients are demanding greater control over their accounts, staff costs are rising and cyber security threats are increasing. This paper seeks to highlight some of the threats the industry is facing while exploring the role that artificial intelligence (AI) may be able to play in addressing some of these challenges. It offers a broad overview of not just areas of application but also weaknesses of the technology that the bank needs to be aware of and also possible issues with implementation. It also seeks to highlight the fact that AI is not a single technology, unlike distributed ledger systems. There are many nuances to AI, such as convolution neural networks, natural language processing and generative AI, and the judicious application of the right nuance of AI to the problem will be key to a successful implementation. [ABSTRACT FROM AUTHOR]

This study explores the polarization of news content shared on Facebook compared with email using data from the New York Times' Most Emailed and Most Shared lists over 2.5 years. Employing latent Dirichlet allocation and large language models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (versus email), even after accounting for factors like topics, emotion, and article age. Additionally, distinct topic preferences emerge, with social issues dominating Facebook shares and lifestyle topics prevalent in emails. Contrary to expectations, political polarization of articles shared on Facebook did not escalate post-2020 election. We introduce a novel approach to measuring polarization of text content that leverages generative artificial intelligence models, like ChatGPT, and it is both scalable and cost effective. This research contributes to the evolving intersection of LLMs, social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04134. [ABSTRACT FROM AUTHOR]

A new era in Generative Artificial Intelligence has begun with the release of powerful Large Language Models (LLMs). These models have shown significant potential in marketing. Essentially, they have been redefining sales practices and changing sales funnel steps. Despite their promise and persuasive capabilities, there remains a dearth of comprehensive understanding regarding the capabilities of LLMs in generating sales arguments, which is a task traditionally characterized by high costs, time consumption, subjective biases, and the need for considerable expertise and skills. In this study, we take the first step towards exploring the ability of LLMs to generate sales arguments. To this end, we evaluated GPT-4 as one of the most capable LLMs to date in performing zero-shot sales argument creation from product features. We created a dataset containing textual descriptions of features extracted from brochures, catalogs, and technical data sheets of various products of a global company specializing in the manufacturing and retailing of furniture, appliances, and home accessories. We conducted a human evaluation with five experts, covering three main criteria of argument quality, namely coherence, persuasiveness, and relevance. The experimental results revealed the remarkable ability of GPT-4 to generate high-quality and well-structured sales arguments according to the Feature-Advantage-Benefit method. Over 98% of the evaluated arguments were coherent and persuasive. Regarding relevance, the model exhibited an accuracy of 91.53% to align the generated arguments to customers' purchase motives, namely security, vanity, novelty, comfort, money, and likability. These findings could lead to significant time and cost reductions, allowing the sales force to focus on higher-value tasks. We posit that this study heralds a novel avenue for exploring LLMs' capabilities in other steps of the sales funnel process. [ABSTRACT FROM AUTHOR]

This article explores the ethical concerns surrounding the use of generative artificial intelligence (GAI) for qualitative data analysis. The authors raise issues related to data ownership, privacy, biases, and the responsibilities of researchers. They argue that relying solely on automated systems for data analysis can lead to biased and incomplete results, and stress the importance of human insight and understanding in research, particularly in areas of marginalization and oppression. The article calls for the development of ethical guidelines to address these concerns and ensure the integrity of qualitative data analysis using GAI. [Extracted from the article]

This article discusses the application of Sociocultural Theory (SCT) in the field of second language (L2) education, specifically in conjunction with corpus linguistics. The authors outline the central principles of SCT and how they have been used to guide L2 development through pedagogical practices. They introduce concept-based language instruction (C-BLI), which organizes L2 curricula around linguistic concepts and uses corpora to support learner engagement. The article also explores Vygotskian SCT and its role in artificial development through education. It concludes by discussing the implementation of C-BLI programs and the potential use of corpora to identify learners' current understanding of language features. [Extracted from the article]

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a "quasi-other"; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions The aim of this study was to assess the ChatGPT‐4 (ChatGPT) large language model (LLM) on tasks relevant to community pharmacy.ChatGPT was assessed with community pharmacy‐relevant test cases involving drug information retrieval, identifying labelling errors, prescription interpretation, decision‐making under uncertainty and multidisciplinary consults. Drug information on rituximab, warfarin, and St. John's wort was queried. The decision‐support scenarios consisted of a subject with swollen eyelids and a maculopapular rash in a subject on lisinopril and ferrous sulfate. The multidisciplinary scenarios required the integration of medication management with recommendations for healthy eating and physical activity/exercise.The responses from ChatGPT for rituximab, warfarin, and St. John's wort were satisfactory and cited drug databases and drug‐specific monographs. ChatGPT identified labeling errors related to incorrect medication strength, form, route of administration, unit conversion, and directions. For the patient with inflamed eyelids, the course of action developed by ChatGPT was comparable to the pharmacist's approach. For the patient with the maculopapular rash, both the pharmacist and ChatGPT placed a drug reaction to either lisinopril or ferrous sulfate at the top of the differential. ChatGPT provided customized vaccination requirements for travel to Brazil, guidance on management of drug allergies and recovery from a knee injury. ChatGPT provided satisfactory medication management and wellness information for a diabetic on metformin and semaglutide.LLMs have the potential to become a powerful tool in community pharmacy. However, rigorous validation studies across diverse pharmacist queries, drug classes and populations, and engineering to secure patient privacy will be needed to enhance LLM utility. [ABSTRACT FROM AUTHOR]

AbstractPurposeMethodsResultsConclusionsTo compare student performance, examiner perceptions and cost of GPT-assisted (generative pretrained transformer-assisted) clinical and professional skills assessment (CPSAs) items against items created using standard methods.We conducted a prospective, controlled, double-blinded comparison of CPSA items developed using GPT-assistance with those created through standard methods. Two sets of six practical cases were developed for a formative assessment sat by final year medical students. One clinical case in each set was created with GPT-assistance. Students were assigned to one of the two sets.The results of 239 participants were analysed in the study. There was no statistically significant difference in item difficulty, or discriminative ability between GPT-assisted and standard items. One hundred percent (n = 15) of respondents to an examiner feedback questionnaire felt GPT-assisted cases were appropriately difficult and realistic. GPT-assistance resulted in significant labour cost savings, with a mean reduction of 57% (880 GBP) in labour cost per case when compared to standard case drafting methods.GPT-assistance can create CPSA items of comparable quality with significantly less cost when compared to standard methods. Future studies could evaluate GPT’s ability to create CPSA material in other areas of clinical practice, aiming to validate the generalisability of these findings. [ABSTRACT FROM AUTHOR]

The article provides an overview of the British television industry and its transition to artificial intelligence (AI). Topics discussed include the impact of AI on the stages of television content delivery, adoption of machine learning models and AI technologies in the distribution and monetisation phases, production and post-production phases, AI tools used as development engines, AI agents used for creative originality and training data, copyright, and intellectual property ownership issues.

AbstractWe provide experimental evidence of implicit racial bias in a large language model (specifically ChatGPT 3.5) in the context of an educational task and discuss implications for the use of these tools in educational contexts. Specifically, we presented ChatGPT with identical student writing passages alongside various descriptions of student demographics, including race, socioeconomic status, and school type. Results indicate that when directly prompted to consider race, the model produced higher overall scores than responses to a control prompt, but scores given to student descriptors of Black and White were not significantly different. However, this result belied a subtler form of prejudice that was statistically significant when racial indicators were implied rather than explicitly stated. Additionally, our investigation uncovered subtle sequence effects that suggest the model is more likely to illustrate bias when variables change within a single chat. The evidence indicates that despite the implementation of guardrails by developers, biases are profoundly embedded in ChatGPT, reflective of both the training data and societal biases at large. While overt biases can be addressed to some extent, the more ingrained implicit biases present a greater challenge for the application of these technologies in education. It is critical to develop an understanding of the bias embedded in these models and how this bias presents itself in educational contexts before using LLMs to develop personalized learning tools. [ABSTRACT FROM AUTHOR]

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, numerous AI tools have been employed to equip learners with diverse technical skills such as coding, data analysis, and other competencies related to computational sciences. However, the desired outcomes have not been consistently achieved. This study aims to analyze the perspectives of students and professionals from non-computational fields on the use of generative AI tools, augmented with visualization support, to tackle data analytics projects. The focus is on promoting the development of coding skills and fostering a deep understanding of the solutions generated. Consequently, our research seeks to introduce innovative approaches for incorporating visualization and generative AI tools into educational practices. Methods: This article examines how learners perform and their perspectives when using traditional tools vs. LLM-based tools to acquire data analytics skills. To explore this, we conducted a case study with a cohort of 59 participants among students and professionals without computational thinking skills. These participants developed a data analytics project in the context of a Data Analytics short session. Our case study focused on examining the participants' performance using traditional programming tools, ChatGPT, and LIDA with GPT as an advanced generative AI tool. Results: The results shown the transformative potential of approaches based on integrating advanced generative AI tools like GPT with specialized frameworks such as LIDA. The higher levels of participant preference indicate the superiority of these approaches over traditional development methods. Additionally, our findings suggest that the learning curves for the different approaches vary significantly. Since learners encountered technical difficulties in developing the project and interpreting the results. Our findings suggest that the integration of LIDA with GPT can significantly enhance the learning of advanced skills, especially those related to data analytics. We aim to establish this study as a foundation for the methodical adoption of generative AI tools in educational settings, paving the way for more effective and comprehensive training in these critical areas. Discussion: It is important to highlight that when using general-purpose generative AI tools such as ChatGPT, users must be aware of the data analytics process and take responsibility for filtering out potential errors or incompleteness in the requirements of a data analytics project. These deficiencies can be mitigated by using more advanced tools specialized in supporting data analytics tasks, such as LIDA with GPT. However, users still need advanced programming knowledge to properly configure this connection via API. There is a significant opportunity for generative AI tools to improve their performance, providing accurate, complete, and convincing results for data analytics projects, thereby increasing user confidence in adopting these technologies. We hope this work underscores the opportunities and needs for integrating advanced LLMs into educational practices, particularly in developing computational thinking skills. [ABSTRACT FROM AUTHOR]

Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well‐formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved. However, the availability of high‐quality paraphrase datasets has been limited, particularly for machine‐generated sentences. In this paper, we present ParaGPT, a new paraphrase dataset of 81,000 machine‐generated sentence pairs, including 27,000 reference sentences (ChatGPT‐generated sentences), and 81,000 paraphrases obtained by using three different large language models (LLMs): ChatGPT, GPT‐3, and T5. We used ChatGPT to generate 27,000 sentences that cover a diverse array of topics and sentence structures, thus providing diverse inputs for the models. In addition, we evaluated the quality of the generated paraphrases using various automatic evaluation metrics. Furthermore, we provide insights into the strengths and drawbacks of each LLM in generating paraphrases by conducting a comparative analysis of the paraphrasing performance of the three LLMs. According to our findings, ChatGPT's performance, as per the evaluation metrics provided, was deemed impressive and commendable, owing to its higher‐than‐average scores for semantic similarity, which implies a higher degree of similarity between the generated paraphrase and the reference sentence, and its relatively lower scores for syntactic diversity, indicating a greater diversity of syntactic structures in the generated paraphrase. ParaGPT is a valuable resource for researchers working on NLP tasks like paraphrasing, text simplification, and text generation. We make the ParaGPT dataset publicly accessible to researchers, and as far as we are aware, this is the first paraphrase dataset produced based on ChatGPT. [ABSTRACT FROM AUTHOR]

ABSTRACT Generative AI including large language models (LLMs) has recently gained significant interest in the geoscience community through its versatile task‐solving capabilities including programming, arithmetic reasoning, generation of sample data, time‐series forecasting, toponym recognition, or image classification. Existing performance assessments of LLMs for spatial tasks have primarily focused on ChatGPT, whereas other chatbots received less attention. To narrow this research gap, this study conducts a zero‐shot correctness evaluation for a set of 76 spatial tasks across seven task categories assigned to four prominent chatbots, that is, ChatGPT‐4, Gemini, Claude‐3, and Copilot. The chatbots generally performed well on tasks related to spatial literacy, GIS theory, and interpretation of programming code and functions, but revealed weaknesses in mapping, code writing, and spatial reasoning. Furthermore, there was a significant difference in the correctness of results between the four chatbots. Responses from repeated tasks assigned to each chatbot showed a high level of consistency in responses with matching rates of over 80% for most task categories in the four chatbots. [ABSTRACT FROM AUTHOR]

With the advent of Transformer-based generative AI, there has been a surge in research focused on large-scale generative language models, especially in natural language processing applications. Moreover, these models have demonstrated immense potential across various vertical fields, ranging from education and history to mathematics, medicine, information processing, and cybersecurity. In research on AI applications in Chinese, it has been found that the quality of text generated by generative AI has become a central focus of attention. However, research on the quality of input text still remains an overlooked priority. Consequently, based on the vectorization comparison of vertical field lexicons and text structure analysis, proposes three input indicators D1, D2, and D3 that affect the quality of generation. Based on this, we studied a text quality evaluation algorithm called VFS (Vertical Field Score) and designed an output evaluation metric named V-L (Vertical-Length). Our experiments indicate that higher-scoring input texts enable generative AI to produce more effective outputs. This enhancement aids users, particularly in leveraging generative AI for question-answering in specific vertical fields, thereby improving response effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

The unveiling of ChatGPT 4o by OpenAI, a multimodal large language model powered by Generative Artificial Intelligence (GenAI), has injected interest and incited debate throughout the echelon of education institutions regarding its prospective benefits and drawbacks. Nonetheless, investigations into the learners' perceptions of GenAI use in learning English as a Foreign Language (EFL) remain markedly insufficient. The study adopts an explorative stance and aims to explore the attitudes and perceptions of Chinese EFL learners toward GenAI use in language learning through the application of metaphor analysis. Data were collected from 281 EFL students of varying majors in four key universities across China by completing a sentence using metaphors to elicit their attitudes and perceptions toward GenAI use in language learning. Through qualitative analysis of metaphorical constructs, including HUMANS, TOOL/MACHINE, BRAIN, RESOURCES, FOOD/DRINK, and MEDICINE metaphors, the study unveils a spectrum of attitudes toward GenAI. While some language learners perceived GenAI as supportive, helpful, and intelligent, others expressed concerns about over-reliance and potential loss of critical thinking skills. The findings underscore the importance of considering learners' diverse attitudes and beliefs toward GenAI use and application in language learning pedagogy. The implications of these findings for the future integration of GenAI in language education are discussed, complemented by recommendations for further research and pedagogical practice. [ABSTRACT FROM AUTHOR]

Questions and concerns about artificial intelligence (AI) technologies in education reached a fever pitch with the arrival of publicly accessible, user‐facing generative AI systems, especially ChatGPT. Many of these issues will require regulation and collective action to address. But when it comes to generative AI and literacy, we argue that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry. Agential realism in particular offers a useful perspective for exploring how generative AI matters in literacy practices, not as a unilaterally destructive force, but as a set of phenomena that intra‐actively reconfigures literacy practices. As a sociocultural (and as we argue, sociotechnical) practice, literacy arises out of the entanglement of bodies, spaces, contexts, positions, histories, and technologies. Generative AI is another in a long line of technologies that reconfigures literacy practices. In this article, we briefly explain how generative AI systems work, focusing on text‐based systems called Large Language Models (LLMs), and suggest ways that generative AI may reconfigure the sociocultural practice of literacy. We then offer three provocations to shift discussions about generative AI and literacy (1) from concerns about intentionality to questions of responsibility, (2) from concerns about authenticity to questions of mattering, and (3) from concerns about imitation to questions of multifarious communication. We conclude by encouraging literacy scholars and practitioners to draw inspiration from critical literacy efforts to discover what matters when it comes to generative AI and literacy. [ABSTRACT FROM AUTHOR]

The article reviews the relevant definitions, mechanisms, applications, and limitations of large language models (LLMs), with a focus on an orthopaedic audience. Topics discussed include an overview of LLMs and how it works, key terms and definitions related to LLMs, and the general purpose of LLMs compared with medical LLMs.

This article addresses the ethical challenges posed by generative artificial intelligence (AI) tools in higher education and explores the first responses of universities to these challenges globally. Drawing on five key international documents from the UN, EU, and OECD, the study used content analysis to identify key ethical dimensions related to the use of generative AI in academia, such as accountability, human oversight, transparency, or inclusiveness. Empirical evidence was compiled from 30 leading universities ranked among the top 500 in the Shanghai Ranking list from May to July 2023, covering those institutions that already had publicly available responses to these dimensions in the form of policy documents or guidelines. The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings. This top-down requirement aligns with a bottom-up approach, allowing instructors flexibility in determining how they utilize generative AI especially large language models in their own courses. Regarding human oversight, the typical response identified by the study involves a blend of preventive measures (e.g., course assessment modifications) and soft, dialogue-based sanctioning procedures. The challenge of transparency induced the good practice of clear communication of AI use in course syllabi in the first university responses examined by this study. [ABSTRACT FROM AUTHOR]

The article explores the integration of AI-generated imagery into digital mental health services. The authors introduce a concept called "AI-empowered imagery writing" (AIW), where users write sentences and an AI system generates corresponding art imagery that reflects the emotional context. They suggest that AIW could be a valuable intervention for mental health improvement, but more research is needed to verify its effectiveness and address potential risks. The authors emphasize the importance of collaboration between diverse fields to manage the risks and benefits of AI-generated imagery. Overall, AI-generated imagery has the potential to enhance mental health and well-being, but further investigation is required. [Extracted from the article]

The article focuses on the implications of artificial intelligence, specifically ChatGPT, in graduate medical education applications, including its potential impact on personal statements, letters of recommendation, and program evaluations.

The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals' assessments. We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient. Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement. ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists. [ABSTRACT FROM AUTHOR]

The article discusses the use of ChatGPT, a large language model created by OpenAI, to generate fake medical reports. The authors highlight the potential ethical concerns and challenges posed by AI-generated content in the field of healthcare. They present a case report of a fictitious disease called "Ikusaka disease" to demonstrate how easily ChatGPT can produce plausible yet medically inaccurate texts. The authors emphasize the need for improved validity and responsibility in the outputs of language models and call for high ethical standards among scientists using generative AI. They also discuss the importance of developing methods for detecting fake reports in peer review and publishing processes. [Extracted from the article]

Objective: To assess the feasibility of using large language models (LLMs), specifically ChatGPT-4, to generate concise and accurate layperson summaries of musculoskeletal radiology reports. Methods: Sixty radiology reports, comprising 20 MR shoulder, 20 MR knee, and 20 MR lumbar spine reports, were obtained via PACS. The reports were deidentified and then submitted to ChatGPT-4, with the prompt "Produce an organized and concise layperson summary of the findings of the following radiology report. Target a reading level of 8-9th grade and word count <300 words." Three (two primary and one later added for validation) independent readers evaluated the summaries for completeness and accuracy compared to the original reports. Summaries were rated on a scale of 1 to 3: 1) summaries that were incorrect or incomplete, potentially providing harmful or confusing information; 2) summaries that were mostly correct and complete, unlikely to cause confusion or harm; and 3) summaries that were entirely correct and complete. Results: All 60 responses met the criteria for word count and readability. Mean ratings for accuracy were 2.58 for reader 1, 2.71 for reader 2, and 2.77 for reader 3. Mean ratings for completeness were 2.87 for reader 1 and 2.73 for reader 2 and 2.87 for reader 3. For accuracy, reader 1 identified three summaries as a 1, reader 2 identified one, and reader 3 identified none. For the two primary readers, inter-reader agreement was low for accuracy (kappa 0.33) and completeness (kappa 0.29). There were no statistically significant changes in inter-reader agreement when the third reader's ratings were included in analysis. Conclusion: Overall ratings for accuracy and completeness of the AI-generated layperson report summaries were high with only a small minority likely to be confusing or inaccurate. This study illustrates the potential for leveraging generative AI, such as ChatGPT-4, to automate the production of patient-friendly summaries for musculoskeletal MR imaging. [ABSTRACT FROM AUTHOR]

Advancements in artificial intelligence, machine learning, and natural language processing have culminated in sophisticated technologies such as transformer models, generative AI models, and chatbots. Chatbots are sophisticated software applications created to simulate conversation with human users. Chatbots have surged in popularity owing to their versatility and user-friendly nature, which have made them indispensable across a wide range of tasks. This article explores the dual nature of chatbots in the realm of cybersecurity and highlights their roles as both defensive tools and offensive tools. On the one hand, chatbots enhance organizational cyber defenses by providing real-time threat responses and fortifying existing security measures. On the other hand, adversaries exploit chatbots to perform advanced cyberattacks, since chatbots have lowered the technical barrier to generate phishing, malware, and other cyberthreats. Despite the implementation of censorship systems, malicious actors find ways to bypass these safeguards. Thus, this paper first provides an overview of the historical development of chatbots and large language models (LLMs), including their functionality, applications, and societal effects. Next, we explore the dualistic applications of chatbots in cybersecurity by surveying the most representative works on both attacks involving chatbots and chatbots' defensive uses. We also present experimental analyses to illustrate and evaluate different offensive applications of chatbots. Finally, open issues and challenges regarding the duality of chatbots are highlighted and potential future research directions are discussed to promote responsible usage and enhance both offensive and defensive cybersecurity strategies. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions. This paper presents results and reflections of an experiment done with the LLM GPT 3.5-Turbo to perform an inductive Thematic Analysis (TA). Previous research has worked on conducting deductive analysis. Thematic Analysis is a qualitative method for analysis commonly used in social sciences and it is based on interpretations by the human analyst(s) and the identification of explicit and latent meanings in qualitative data. The paper presents the motivations for attempting this analysis; it reflects on how the six phases to a TA proposed by Braun and Clarke can partially be reproduced with the LLM and it reflects on what are the model's outputs. The paper uses two datasets of open access semi-structured interviews, previously analysed by other researchers. The first dataset contains interviews with videogame players, and the second is a dataset of interviews with lecturers teaching data science in a University. This paper used the analyses previously conducted on these datasets to compare with the results produced by the LLM. The results show that the model can infer most of the main themes from previous research. This shows that using LLMs to perform an inductive TA is viable and offers a good degree of validity. The discussion offers some recommendations for working with LLMs in qualitative analysis. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Generative artificial intelligence (AI) models such as OpenAI's ChatGPT and Google's Bard have forced educators to consider how these tools will be efficiently utilized to improve medical education. This article investigates current literature on how generative AI is and could be used and implemented in undergraduate medical education (UME). METHODS: A rapid review of the literature was performed utilizing a librarian-generated search strategy to identify articles published before June 30, 2023, in 6 databases (Pubmed, EMBASE.com, Scopus, ERIC via EBSCO, Computer Science Database via EBSCO, and CINAHL via EBSCO). Inclusion criteria were (1) a focus on osteopathic and/or allopathic UME and (2) a defined use or implementation strategy for generative AI. Two reviewers screened all articles, and data extraction was performed by 1 reviewer and confirmed by the other reviewer. RESULTS: A total of 521 relevant articles were screened during this review. Forty-one articles underwent full-text review and data extraction. The majority of the articles were opinion pieces (9), case reports (8), letters to the editor (5), editorials (5), and commentaries (3) about the use of generative AI while 7 articles used qualitative and/or quantitative methods. The literature is best divided into 5 categories of uses for generative AI in UME: nonclinical learning assistant, content developer, virtual patient interaction, clinical decision-making tutor, and medical writing. The literature indicates generative AI tools' greatest potential is for use as a virtual patient and clinical decision-making tutor. CONCLUSIONS: While the possibilities proliferate for generative AI in UME, there remains a dearth of quantitative evidence of its use for improving learner outcomes. The majority of the literature opines the potential for utilization, but only 7 studies formally evaluated the results of using generative AI. Future research should focus on the effectiveness of incorporating generative AI into preclinical and clinical curricula in UME. [ABSTRACT FROM AUTHOR]

Generally, the Black-Litterman portfolio model relies heavily on the expert investment opinions, where high-quality investment opinions would improve model performance whereas low-quality investment opinions would result in poor model performance. Essentially, expert investment opinions are highly subjective, sourcing from these financial experts' information and knowledge. ChatGPT, as an advanced generative AI model, could extract and analyze information from huge multi-modal data, which is beneficial to build AI investment opinions for Black-Litterman portfolio model. In this study, we construct and analyze the large language model-based Black-Litterman portfolios, ChatGPT-BL and BARD-BL, where the goals of minimum variance and mean-variance trade-off are taken into account. Computational results show that the ChatGPT-based portfolios tend to be conservative, which is suitable for risk-averse investors, while the BARD-based portfolios are aggressive, which is appropriate for risk-seeking investors. Also, the superiority of Black-Litterman model using investor views generating from gradient boosting regression and GJR-GARCH algorithm is illustrated by the efficient frontiers. [ABSTRACT FROM AUTHOR]

The article focuses on how Artificial Intelligence (AI) is transforming Human Resources (HR) practices, emphasizing key areas for effective implementation. Topics discussed include building a business case for AI in HR, ensuring accountability within organizations, and developing responsible policies to manage risks associated with AI.

This text explores the comparison between artificial intelligence (AI) and human intelligence (HI) in the context of project management. It discusses the strengths and weaknesses of both AI and HI, highlighting that while AI may excel in certain tasks, it lacks empathy and relationship-building skills. The text suggests that the ideal project manager is a human augmented with AI, as they can leverage the strengths of both to deliver more value in a shorter timeframe. It also emphasizes the importance of individuals adapting and harnessing the capabilities of AI to succeed in the evolving professional landscape. [Extracted from the article]

The study analyzes the impact of artificial intelligence (AI) on the creative industry, focusing on the generative image creation logic of Midjourney. It highlights that the advertising and communication industry elite misjudged the effects of social media. The document details how AI reshapes creative workflows and has already become integral to visual content creation. The study provides a comprehensive overview of Midjourney’s functioning and development principles, including large language models and diffusion techniques, which allow for the creation of unique, generative images from textual descriptions. It emphasizes the creative potential of generative AIs and the challenges users face in producing high-quality and consistent images. The study illustrates various creative modes and parameterization options within Midjourney that enable users to achieve desired visual effects. It argues that international protests over the AI learning from thousands of artists’ styles are somewhat moot, as the new Style Reference parameter successfully mimics an artist’s style even without prior model training on that specific style. According to the document, generative AIs like Midjourney are not just tools but also sources of new visual styles that define future creative trends. Finally, the author examines the rapid development of generative AI technologies and their influence on the daily practices of the creative profession. He posits that while the future impacts of AI are difficult to predict, these technologies are already bringing significant changes to the creative ecosystem, and it is crucial for professionals to continually evolve and adapt to the opportunities presented by new tools. [ABSTRACT FROM AUTHOR]

Recently, large language models (LLMs) have shown remarkable advancements in natural language processing tasks, including question-answering. However, despite their excellent performance, these models often generate inaccurate or incomplete answers, revealing certain limitations. To overcome these limitations, we propose a novel approach that leverages ensemble diversity to improve the response quality of LLMs. We aim to mitigate the shortcomings of a single LLM approach by utilizing the response diversity of various LLM methods, thereby generating more accurate and reliable answers. This approach is mainly based on parallel bagging, a technique of ensemble diversity. Furthermore, we allow the proposed method to predict relative performance metrics by comparing its results with other LLM methods. As a result of the experiment, the EDG-A showed an average performance improvement of 11.5% compared to the RAG method and an average of 16.5% compared to the DAG method. [ABSTRACT FROM AUTHOR]