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This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

The aim of this study was to examine the application of artificial intelligence (AI) and generative AI (GAI) in dental education through a comprehensive bibliometric analysis, with the goal of identifying potential future research areas in this field. Data were obtained from the Web of Science and Scopus databases, with no date restrictions. The inclusion criteria encompassed research articles, reviews and proceedings papers focused on the use of AI in dental education. The Bibliometrix R package was used for data analysis. A total of 54 documents were analysed, revealing a significant increase in publications from 2021 to 2024. The Journal of Dental Education was identified as the most prolific source, with the United States leading in terms of contributions. The findings showed a growing interest in AI's potential to transform dental education, with significant contributions from a diverse set of global authors and institutions. Several key themes were identified, including 'large language models', 'chatbots' and 'clinical decision support systems'. The citation analysis highlighted influential papers, including those by Thurzo etal. (2023) and Yüzbaşıoğlu (2021). There is a growing interest in the use of AI and GAI in dental education. However, further research, particularly experimental studies, is essential to fully understand their impact on educational outcomes in dentistry. Key areas for exploration include the application of AI for personalised learning, the integration of chatbots as educational tools to support students, and the use of AI for training and assessing practical skills.

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

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.

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.

With the rapid evolution of artificial intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in physics education and offers actionable AI policy recommendations. Using openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (GCSE), A-Level, and introductory university curricula. We employed various AI prompting techniques: Zero Shot, in context learning, and confirmatory checking, which merges chain of thought reasoning with reflection. The proficiency of gpt-3.5-turbo varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM’s accuracy on 5000 mathematical operations was found to be 45.2%. When evaluated as a marking tool, the LLM’s concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there’s no substantial threat from LLMs for non-invigilated physics questions. However, given the LLMs’ considerable proficiency in writing physics essays and coding abilities, non-invigilated examinations of these skills in physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect.

Summary The automated standardization of legacy datasets can facilitate the generation of valuable new insights, when consumed within a business intelligence platform. Where the digital transformation in the energy services sector has successfully led to the automation of data gathering and interpretation processes, a geoscientist’s time can be freed up to undertake the more valuable interpretive tasks that are so vital in understanding subsurface geology. A recent advancement in managing data that has significantly helped with this transition has been the adoption of business intelligence and analytics platforms to produce advanced visualizations and automated analysis. This presentation and article focusses on a recent example of this process in action where play cross sections in an unstructured PDF format are processed for use within an analytical platform.

In present business competition, organizations are emphasizing on optimal decision support systems (DSS) to enhance its growth-oriented decision support system process. To meet these requirements, the enterprise solutions facilitate certain robust and productive business intelligence (BI) applications. On the other hand, to retain market, organizations are emphasizing on service innovation discovery and its redesign (SIDRD) paradigm using BI applications. To accomplish SIDRD, DSS-oriented BI applications require huge feedback datasets to process where the data could be retrieved from certain feedback channel or data warehouses (DWs). The data warehouses encompass datasets from varied resources, and to ensure optimal data extraction and retrieval, these DWs need robust data mining schemes compatible with multidimensional data model (MDDM). On the other hand, to provide most precise and accurate DSS applications, the data security and its privacy preservation are inevitable. Considering all these requirements, in this paper, a robust privacy-preserved mining model called Commutative RSA (CRSA) has been developed and implemented with C5.0 decision tree algorithm to achieve SIDRD objectives with BI utilities. The developed paradigm has exhibited optimal performance for optimized accuracy, overheads, and secure rule set generation for BI. The performance for the developed SIDRD has been analyzed in terms of coverage accuracy and F1 scores, and the overall system has performed optimal.

The creative industries in West Java have significantly boosted the region's economy, contributing to higher GDP, more jobs, and increased exports. However, the handicraft sector seeks to grow its presence in the international market, where it currently holds a minor share. To address the challenges of expanding, such as limited information, marketing obstacles, and regulatory hurdles, the handicraft industry is encouraged to adopt a business intelligence (BI) platform. This study aims to use a BI platform to present and analyze export data for West Java's craft industry, examining its distribution, trends, and future prospects to help increase exports from this Indonesian province. The analysis employs clustering with k-means, time series analysis, and forecasting methods, including exponential smoothing and the compound annual growth rate (CAGR), using export data from 2018 to 2022. The process involves collecting primary and secondary data, transforming it through ETL (Extract, Transform, Load) technology, and integrating it into the BI platform for analysis. This analysis aims to identify export patterns, trends, and make forecasts that can guide decision-making. The findings indicate that handicraft exports are categorized into three destination country clusters, each favoring different product types, revealing trends and growth opportunities for various handicraft items. Additionally, the study provides forecasts for handicraft exports, offering valuable insights for strategic planning.

The adoption of Business Intelligence (BI), Big Data, and Advanced Data Analysis techniques (such as Artificial Intelligence) by organizations for decision support is seen as incontrovertible and imperative to be competitive in the global market. If first companies are concerned with the implementation on data platforms for Data Analysis/data science, as per example, Self-Service Business Intelligence, Cloud Data Lakes, Machine Learning frameworks, Mobile Intelligence, regulations (e.g., General Data Protection Regulation – GDPR) and information security (infosec) force organizations to put in place data governance programs and data maturity assessment. The foundations for business results on using data platforms are trusted data, improved risk and analytic decisions, cost reduction and operational efficiency, and regulatory compliance. Being the deployment of BI platforms necessary to store and extract knowledge from data, the data governance is mandatory to assure the central data management, data quality, regulation, security, and privacy. Another important topic, to establish the data roadmap and assess the maturity within an organization, is the adoption of mechanisms to measure the return of investments in BI and Big Data platforms. If currently organizations tend to be data centric, a transversal approach from tools to data governance and maturity control is required.

Every recognized hospital’s patient management unit (PMU) has focused its efforts on improving clinical patient care, with a process approach, analyzing it from adult emergency overcrowding to prolonged stays in clinical services. It is generating many patients waiting for beds in the emergency service. The PMU does not have a business intelligence (BI) platform that provides information in real‐time, generating a blind browsing problem. The purpose is to demonstrate the need for a BI platform using Artificial Intelligence (AI) to analyze in real‐time the relevant information for decision making. The methodology consists of analyzing qualitative and quantitatively the statistics of the last three years, both from the emergency service and from the clinical services. This study shows that the saturation of the emergency service responds to the number of patients waiting for beds, which interferes with outpatient care. The projections for 2020 underestimated the demand, and the efforts to open hospital beds and home hospitalization quotas allowed to shovel said excess demand. The average stay numbers continue to increase, as does the number of hospitalized patients for emergencies, generating a progressive growth in demand. It is necessary to have a BI system adapted with AI to perform real‐time analysis of the GRD, to be able to act during hospitalization and not afterward.

Many critical business and societal decisions in areas such as supply chain and healthcare involve numerous potential actions, complex constraints, and goals that can be modeled as objective functions. Mathematical optimization, a core area in Operations Research (OR), provides robust, mathematically grounded methodologies to address such decisions and has shown tremendous benefits in many applications. However, its application requires the creation of accurate and efficient optimization models, necessitating rare expertise and considerable time, creating a barrier to widespread adoption in decision-making. Thus, it is a long-standing goal to make these capabilities widely accessible. The advent of Large Language Models (LLMs) has made advanced Artificial Intelligence (AI) capabilities widely accessible through natural language. LLMs can accelerate expert work in creating formal models like computer programs, and emerging research indicates they can also speed up the development of optimization models by OR experts. We, therefore, propose integrating and advancing LLM and optimization modeling to empower organizational decision-makers to model and solve such complex problems without requiring deep expertise in optimization. In this work, we present our vision for democratizing optimization modeling for organizational decision-making by such a combination of LLMs and optimization modeling. We identify a set of fundamental requirements for the vision's implementation and describe the state of the art through a literature survey and some experimentation. We show that a) LLMs already provide substantial novel capabilities relevant to realizing this vision, but that b) major research challenges remain to be addressed. We also propose possible research directions to overcome these gaps. We would like this work to serve as a call to action to bring together the LLM and OR optimization modeling communities to pursue this vision, thereby enabling much more widespread improved decision-making and increasing by orders of magnitude the benefits AI and OR can bring to enterprises and society.

In today's rapidly evolving business landscape, Artificial Intelligence (AI) stands as the proverbial 'elephant in the room,' profoundly shaping diverse sectors and contexts. While debates rage among policymakers, practitioners, and politicians about regulating AI's widespread use, it's undeniable that AI represents a long-awaited digital technology poised to revolutionize organizational performance. Amidst the post-COVID era, the allure of AI has intensified, yet the critical question lingers: Can firms effectively harness AI and other technologies, such as Generative AI and Large Language Models (LLMs), to enhance their existing systems? Through a systematic literature review, a clear correlation between firms’ implementation of AI, including the cutting-edge Generative AI, and their ability to adapt to changing market dynamics, drive operational excellence, and unlock new avenues for growth are explored. Moreover, key drivers of AI and digital adoption, such as the imperative for data-driven decision-making, the quest for customer-centricity, and the drive for sustainable business practices are explored. Our research not only highlights the transformative potential of AI and digital technologies but also provides actionable insights for business leaders navigating the complexities of technology adoption. By understanding the motivations, challenges, and strategic imperatives driving firms' technology choices, including the integration of Generative AI and LLMs, organizations can chart a path towards sustainable growth and competitive advantage in the digital age. This study underscores the revolutionary impact of Generative AI in digital transformation, offering a comprehensive understanding of their role in shaping the future of business.

The current debate on artificial intelligence (AI) tends to associate AI imaginaries with the vision of a future technology capable of emulating or surpassing human intelligence. This article advocates for a more nuanced analysis of AI imaginaries, distinguishing “strong AI narratives,” i.e., narratives that envision futurable AI technologies that are virtually indistinguishable from humans, from "weak" AI narratives, i.e., narratives that discuss and make sense of the functioning and implications of existing AI technologies. Drawing on the academic literature on AI narratives and imaginaries and examining examples drawn from the debate on Large Language Models and public policy, we underscore the critical role and interplay of weak and strong AI across public/private and fictional/non-fictional discourses. The resulting analytical framework aims to empower approaches that are more sensitive to the heterogeneity of AI narratives while also advocating normalising AI narratives, i.e., positioning weak AI narratives more firmly at the center stage of public debates about emerging technologies.

BackgroundThe integration of artificial intelligence (AI) in medical education is evolving, offering new tools to enhance teaching and assessment. Among these, script concordance tests (SCTs) are well-suited to evaluate clinical reasoning in contexts of uncertainty. Traditionally, SCTs require expert panels for scoring and feedback, which can be resource-intensive. Recent advances in generative AI, particularly large language models (LLMs), suggest the possibility of replacing human experts with simulated ones, though this potential remains underexplored.ObjectiveThis study aimed to evaluate whether LLMs can effectively simulate expert judgment in SCTs by using generative AI to author, score, and provide feedback for SCTs in cardiology and pneumology. A secondary objective was to assess students’ perceptions of the test’s difficulty and the pedagogical value of AI-generated feedback.MethodsA cross-sectional, mixed methods study was conducted with 25 second-year medical students who completed a 32-item SCT authored by ChatGPT-4o (OpenAI). Six LLMs (3 trained on the course material and 3 untrained) served as simulated experts to generate scoring keys and feedback. Students answered SCT questions, rated perceived difficulty, and selected the most helpful feedback explanation for each item. Quantitative analysis included scoring, difficulty ratings, and correlations between student and AI responses. Qualitative comments were thematically analyzed.ResultsThe average student score was 22.8 out of 32 (SD 1.6), with scores ranging from 19.75 to 26.75. Trained AI systems showed significantly higher concordance with student responses (ρ=0.64) than untrained models (ρ=0.41). AI-generated feedback was rated as most helpful in 62.5% of cases, especially when provided by trained models. The SCT demonstrated good internal consistency (Cronbach α=0.76), and students reported moderate perceived difficulty (mean 3.7, SD 1.1). Qualitative feedback highlighted appreciation for SCTs as reflective tools, while recommending clearer guidance on Likert-scale use and more contextual detail in vignettes.ConclusionsThis is among the first studies to demonstrate that trained generative AI models can reliably simulate expert clinical reasoning within a script-concordance framework. The findings suggest that AI can both streamline SCT design and offer educationally valuable feedback without compromising authenticity. Future studies should explore longitudinal effects on learning and assess how hybrid models (human and AI) can optimize reasoning instruction in medical education.