Strengthening assessment integrity in the era of generative AI: evidence from a large-scale study by Liz Hardie (22277602)

“…This large-scale empirical study at a UK university, based on 590 student and 354 AI-generated answers, provides evidence on markers’ ability to detect the GenAI scripts and whether some assessment types are more robust than others against GenAI misuse. …”

Identification and Description of Emotions by Current Large Language Models - Dataset by Suketu Patel (17748162)

“…<p dir="ltr">The assertion that artificial intelligence (AI) cannot grasp the complexities of human emotions has been a long-standing debate. …”

Brief summary highlighting the study methology. a) AI Tool Development and Pediatric Repurposing: The AI tool was originally trained and validated using a large dataset of adult ch... by Prerana Agarwal (21781515)

Subjects:

AI-Enhanced Methods in Autonomous Systems: Large Language Models, DL Techniques, and Optimization Algorithms by I. de Zarza (17378452)

“…<p dir="ltr">Presentation for PhD thesis:</p><p dir="ltr">AI-Enhanced Methods in Autonomous Systems: Large Language Models, DL Techniques, and Optimization Algorithms https://doi.org/10.4995/Thesis/10251/202201</p><p dir="ltr">Abstract:</p><p dir="ltr">The proliferation of autonomous systems, and their increasing integration with day-to-day human life, have opened new frontiers of research and development. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Table 2_ePWV as a scalable risk factor for large-scale glaucoma screening: evidence from a national Chinese cohort.docx by Yupeng Xu (6787604)

Table 1_ePWV as a scalable risk factor for large-scale glaucoma screening: evidence from a national Chinese cohort.docx by Yupeng Xu (6787604)

<b>Enhancing Human-AI Interactions: The Impact of Pseudo-code Engineering on Improving Predictability and Stability in Large Language Models</b> - Appendix A by Gian Michaelsen (18824614)

“…On the other hand, prompts that integrate both natural language and pseudo-code see a 20% increase in content richness compared to those that use only natural language. …”

Supplementary file 1_Swedish Medical LLM Benchmark: development and evaluation of a framework for assessing large language models in the Swedish medical domain.pdf by Birger Moëll (21699569)

“…Introduction<p>We present the Swedish Medical LLM Benchmark (SMLB), an evaluation framework for assessing large language models (LLMs) in the Swedish medical domain.…”

Events O, A and B for the three time delay quantities. by Thomas Ward (688068)

Table 1_Association between the atherogenic index of plasma and risk of large-artery atherosclerotic ischemic stroke.xlsx by Wen Zhong (425650)

“…Objective<p>Ischemic stroke caused by large artery atherosclerosis (LAA) is a major subtype of ischemic stroke and poses a heavy public health burden. …”

Table_1_The impact of large language models on higher education: exploring the connection between AI and Education 4.0.XLSX by Iris Cristina Peláez-Sánchez (18827128)

“…Artificial Intelligence (AI), particularly Generative AI (GAI), has emerged as a pivotal disruption in education, showcasing the capability to produce diverse and context-relevant content. …”

Data_Sheet_1_Advanced large language models and visualization tools for data analytics learning.csv by Jorge Valverde-Rebaza (19350325)

<b>The Large Language Model GPT-4 Compared to Endocrinologist Responses on Initial Choice of Antidiabetic Medication Under Conditions of Clinical Uncertainty</b> by James H. Flory (236115)

“…After modifying the prompt to encourage metformin use, the selection of metformin by GPT-4 increased to 25% (95% CI 22%–28%). GPT-4 rarely selected metformin in patients with impaired kidney function, or a history of gastrointestinal distress (2.9% of responses, 95% CI 1.4%–5.5%). …”

Human-AI Collaborative Journaling with POCKET-MIND: A Dual-Prompt Framework for Emotional Exploration and Goal Attainment by HaeJi Yang (22829125)

“…We present POCKET-MIND, a personalized digital journaling system powered by a Large Language Model (LLM) that facilitates both emotional exploration and goal pursuit through a novel Dual-Prompt Framework. …”

Data Sheet 1_Conversational AI agent for precision oncology: AI-HOPE-WNT integrates clinical and genomic data to investigate WNT pathway dysregulation in colorectal cancer.docx by Ei-Wen Yang (149486)

“…</p>Methods<p>AI-HOPE-WNT employs a modular architecture combining large language models (LLMs), a natural language-to-code engine, and a backend statistical workflow interfaced with harmonized data from cBioPortal. …”

Violin plots of the best fit modelled distributions of times to clinical outcome over the course of the pandemic for category A. by Thomas Ward (688068)

Opening the Black Box(es) by Charles Pence (99065)

“…<p dir="ltr">A talk which explores challenges for contemporary work in digital humanities stemming from the increasing use of generative large language models.…”

Expression of lipid biosynthetic genes is decreased with polyamine depletion. by Yazmin E. Cruz-Pulido (20285238)

Data_Sheet_1_Application of a nomogram model for the prediction of 90-day poor outcomes following mechanical thrombectomy in patients with acute anterior circulation large-vessel o... by Xia Li (14984)

“…Moreover, five variables, namely, age (odds ratio [OR]: 1.049, 95% CI [1.016–1.083]; p = 0.003), glucose level (OR: 1.163, 95% CI [1.038–1.303]; p = 0.009), baseline National Institute of Health Stroke Scale (NIHSS) score (OR: 1.066, 95% CI [0.995–1.142]; p = 0.069), unsuccessful recanalization (defined as a TICI grade of 0 to 2a) (OR: 3.730, 95% CI [1.688–8.245]; p = 0.001), and early neurological deterioration (END, defined as an increase of ≥4 points between the baseline NIHSS score and the NIHSS score at 24 h after MT) (OR: 3.383, 95% CI [1.411–8.106]; p = 0.006), were included in the nomogram to predict the potential risk of poor outcomes at 90 days following MT in LVO patients, with a C-index of 0.763 (0.693–0.832) in the training set and 0.804 (0.719–0.889) in the validation set.…”