Enhancing Decision Making Through the Integration of Large Language Models and Operations Research Optimization

Large language models (LLMs) are advanced artificial intelligence (AI) tools capable of generating human-like text and are increasingly used in education, clinical care, and research. Little is known about their use within oral and maxillofacial surgery (OMFS) training. This study investigates LLM usage trends, perceived value, and educational integration among OMFS residents in the United States. A national, anonymous cross-sectional survey was distributed to OMFS residents via program directors. It gathered demographic data, LLM usage patterns, applications, perceived limitations, and attitudes toward incorporating LLMs into formal education. Eighty-one residents responded, 79.0% (64/81) reported having used an LLM, and of that group, 96.9% (62/64) use ChatGPT. 51.9% (42/81) of respondents used LLMs at least monthly in residency; however, 97.5% (79/81) reported having received no formal LLM education during residency. Residents used LLMs for clinical decision support, board preparation, research, and career planning. Free-text responses revealed a wide spectrum of views. Some advocated for curricular integration and patient education applications, while others questioned the need for formal instruction. Some respondents supported integrating LLMs into curriculums and patient education while others questioned the need for formal instruction. LLMs are used frequently by OMFS residents for a variety of purposes. As AI and LLMs become embedded in healthcare, understanding how OMFS residents interact with LLMs is vital. These findings may guide curriculum development, fostering responsible and effective use of LLMs in surgical training and practice.

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 notes What 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.

Evaluating gpt-4 for zero-shot classification of bleeding and clotting events: Can large language models serve as second reviewers?

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.

BackgroundIn recent years, generative artificial intelligence and large language models (LLMs) have rapidly advanced, offering significant potential to transform medical education. Several studies have evaluated the performance of chatbots on multiple-choice medical examinations.ObjectiveThe study aims to assess the performance of two LLMs—GPT-4o and OpenAI o1—on the Médico Interno Residente (MIR) 2024 examination, the Spanish national medical test that determines eligibility for competitive medical specialist training positions.MethodsA total of 176 questions from the MIR 2024 examination were analyzed. Each question was presented individually to the chatbots to ensure independence and prevent memory retention bias. No additional prompts were introduced to minimize potential bias. For each LLM, response consistency under verification prompting was assessed by systematically asking, “Are you sure?” after each response. Accuracy was defined as the percentage of correct responses compared to the official answers provided by the Spanish Ministry of Health. It was assessed for GPT-4o, OpenAI o1, and, as a benchmark, for a consensus of medical specialists and for the average MIR candidate. Subanalyses included performance across different medical subjects, question difficulty (quintiles based on the percentage of examinees correctly answering each question), and question types (clinical cases vs theoretical questions; positive vs negative questions).ResultsOverall accuracy was 89.8% (158/176) for GPT-4o and 90% (160/176) after verification prompting, 92.6% (163/176) for OpenAI o1 and 93.2% (164/176) after verification prompting, 94.3% (166/176) for the consensus of medical specialists, and 56.6% (100/176) for the average MIR candidate. Both LLMs and the consensus of medical specialists outperformed the average MIR candidate across all 20 medical subjects analyzed, with ≥80% LLMs’ accuracy in most domains. A performance gradient was observed: LLMs’ accuracy gradually declined as question difficulty increased. Slightly higher accuracy was observed for clinical cases compared to theoretical questions, as well as for positive questions compared to negative ones. Both models demonstrated high response consistency, with near-perfect agreement between initial responses and those after the verification prompting.ConclusionsThese findings highlight the excellent performance of GPT-4o and OpenAI o1 on the MIR 2024 examination, demonstrating consistent accuracy across medical subjects and question types. The integration of LLMs into medical education presents promising opportunities and is likely to reshape how students prepare for licensing examinations and change our understanding of medical education. Further research should explore how the wording, language, prompting techniques, and image-based questions can influence LLMs’ accuracy, as well as evaluate the performance of emerging artificial intelligence models in similar assessments.

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.

Readability & quality of large language model responses in CAR-T patient education

The Ministry of Food and Drug Safety (MFDS) of the Republic of Korea, similar to the United States Food and Drug Administration and the United Kingdom's Medicines and Healthcare products Regulatory Agency, issued specific regulatory guidelines on January 24, 2025, for approving generative artificial intelligence (AI) technologies as medical devices [1].Although these guidelines use the term 'generative AI,' they predominantly focus on the approval of AI software tools based on large language models (LLMs) and large multimodal models (LMMs) [2], the latter of which can process various types of input data, such as texts, images, videos, audio, and bio-signals.Unlike the broader guidelines for approving AI models as medical devices, specific regulatory guidelines for LLMs/LMMs have arguably not yet been proposed in other countries.

Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

As generative artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly used on both sides of high-stakes decision processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias---the tendency of LLMs to favor their own generated content---but its real-world implications have not been empirically evaluated. We focus on the hiring context, where job applicants increasingly use LLMs to refine resumes, while employers integrate LLMs into their recruitment pipelines to screen those same resumes. Using a controlled resume correspondence experiment, we find that LLMs consistently prefer resumes generated by themselves over those written by humans or by alternative models, even when content quality is controlled. The bias against human-written resumes is particularly substantial, with self-preference rates ranging from 68 percent to 92 percent across a diverse set of commercial and open-source models. This behavior introduces a novel form of algorithmic unfairness, one that advantages users of specific AI tools and disadvantages others based on their tool choices or access. We further show that this bias can be significantly reduced through simple interventions based on LLMs' self-recognition capabilities, yielding reductions of over 60 percent. These findings highlight an emerging but previously overlooked risk in AI-assisted decision making and call for expanded frameworks of AI accountability that address not only demographic disparities but also biases in model-to-model interaction.

Interacting with Large Language Models (LLMs) via declarative queries is increasingly popular for tasks like question answering and data extraction, thanks to their ability to process vast unstructured data. However, LLMs often struggle with answering complex factual questions, exhibiting low precision and recall in the returned data. This challenge highlights that executing queries on LLMs remains a largely unexplored domain, where traditional data processing assumptions often fall short. Conventional query optimization, typically cost-driven, overlooks LLM-specific quality challenges such as contextual understanding. Just as new physical operators are designed to address the unique characteristics of LLMs, optimization must consider these quality challenges. Our results highlight that adhering strictly to conventional query optimization principles fails to generate the best plans in terms of result quality. To tackle this challenge, we present a novel approach to enhance SQL results by applying query optimization techniques specifically adapted for LLMs. We introduce a database system, GALOIS, that sits between the query and the LLM, effectively using the latter as a storage layer. We design alternative physical operators tailored for LLM-based query execution and adapt traditional optimization strategies to this novel context. For example, while pushing down operators in the query plan reduces execution cost (fewer calls to the model), it might complicate the call to the LLM and deteriorate result quality. Additionally, these models lack a traditional catalog for optimization, leading us to develop methods to dynamically gather such metadata during query execution. Our solution is compatible with any LLM and balances the trade-off between query result quality and execution cost. Experiments show up to 144% quality improvement over questions in Natural Language and 29% over direct SQL execution, highlighting the advantages of integrating database solutions with LLMs.

Assessment and feedback activities in higher education are undergoing significant changes. Many universities and institutes still rely on traditional testing and grading methods, which often fall short in supporting meaningful student learning, especially in large classes. Although educational policies, such as those promoted by the Bologna process, encourage more feedback-oriented and student-centered approaches, these practices can be difficult to implement due to time constraints and limited resources. Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), has shown strong potential in addressing these challenges. This review examines 21 research studies published between 2023 and 2025 that explore the use of GenAI in providing feedback and assessing student work in higher education, with some studies also comparing GenAI’s performance with human instructors. Findings show that LLMs can generate personalized and constructive feedback and/or assist with fair and consistent assessment. However, in most studies, teachers still play a key role, as expert oversight is essential to ensure that grading assessments and assignment feedback are accurate, relevant, and aligned with learning objectives. For GenAI to be used effectively, educators need to understand how to work with these tools, such as learning GenAI prompt design and the basic principles behind LLMs. We recommend that academic institutions provide training for educators in AI literacy, prompt engineering, and the development of teaching strategies that combine the strengths of human judgment with AI support. By effectively integrating LLM tools, major assessment challenges, such as limited time and inconsistent feedback quality, can be addressed while also enhancing student learning and engagement.

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.

In the systems engineering (SE) community, generative artificial intelligence (GenAI), such as large language models (LLMs), continues to grow in popularity with applications varying from help with writing requirements, to creating traditional text‐based documents, and to generating models. Furthermore, the text‐based format of the Systems Modeling Language version 2 (SysMLv2) gives rise to wide ranging possibilities when combined with LLMs. It is common to specialize LLMs on specific tasks, such as SE; which may be achieved by a number of means. Fine‐tuning and retrieval augmented generation (RAG) are example methods to teach a model how to act and what the model needs to know, respectively. Typical practice evaluates LLMs relative to one‐another through the use of benchmarks. Benchmarks are tasks that use a common scoring‐based method where the LLM either answers a set of multiple‐choice questions or textual queries to verified, correct answers. A domain specific benchmark for SE, SysEngBench, was developed over the last year. With many in the SE community developing custom language models, the usage of a standard benchmark is essential for relative performance comparison. We have conducted a limited experiment using SysEngBench to compare performance, on SE tasks, of a small set of LLMs (control plus two modified models). The intent of this study was to set a baseline for ranking models according to performance on SE tasks, the results suggest what many in the SE community may find intuitively unexpected.