large language models

Developing and validating the attitudes toward large language models scale

Architectural and Governance Approaches to Controlling Large Language Model Costs in Healthcare Information Systems

Large language models are entering healthcare information systems through documentation, retrieval, summarization, multimodal interpretation, and administrative workflows. Their operating costs depend on more than unit pricing because repeated context transfers, unnecessary model calls, retries, long outputs, and reuse...

Multimodal LLMs for Leprosy Diagnostic Support: Evaluating Performance and Healthcare Professionals’ Trust and Acceptance

Overall, general-purpose LLMs show potential for leprosy diagnostic support, but their clinical evaluation should extend beyond aggregate classification performance to consider clinically informative inputs, error profiles, response consistency, and the presentation of outputs in ways that support appropriate human ove...

Reimagining Inquiry Through AI: Emerging Technologies for Scalable Complex Problem-Solving

How intentional instructional design can mediate the risks of AI while leveraging its affordances to support complex learning environments is illustrated by illustrating how intentional instructional design can mediate the risks of AI while leveraging its affordances to support complex learning environments.

Large Language Models Are More Sycophantic in Chinese Than in English

AI sycophancy has emerged as a prominent concern in human–AI interaction, as large language models (LLMs) may unduly affirm users’ beliefs or actions rather than provide independent assessments. Such behavior may vary across languages because language can cue culturally patterned norms of social interaction. Prior work...

Codevelopment, Implementation, and Evaluation of an AI-powered Conversational Interface for Tobacco Cessation: Protocol for a Single-Group Pre-Post Study

Background Despite several efforts to expand tobacco cessation services through tobacco cessation centers (TCCs) and quit lines, key operational challenges still persist. Recently, AI-based digital interventions have shown promise globally for smoking cessation; however, they remain underused in tobacco cessation strat...

QI Meets AI—Implications and Opportunities for Laboratory Medicine

Abstract The integration of artificial intelligence (AI) and large language models into healthcare is reshaping quality improvement (QI), creating unprecedented opportunities while introducing new risks that require dedicated governance frameworks and professional competencies. AI-enabled QI has significant implication...

Evaluating Gemma 3 27B for local undergraduate physics tutoring

Abstract Physics education research has developed a range of student-centered approaches that emphasize conceptual understanding and problem solving, creating interest in whether large language models (LLMs) can provide complementary, on-demand tutoring support. Locally deployable models are particularly relevant for i...

Boosting AI-based speech performance assessment through pairwise comparative scoring of crowd data

Open-ended performance assessments can capture intellectual status that is difficult to measure using highly constrained response formats. However, their practical use is limited by the difficulty of obtaining reliable, scalable, and interpretable scores. Recent advances in large language models provide new opportuniti...

The Model-Infrastructure Co-Evolution in Conversational NLU: From ELIZA to Large Language Models

Conversational Natural Language Understanding (NLU) has passed through five distinguishable technological eras: rule-based symbolic parsing, statistical and probabilistic modelling, neural sequence learning, Transformer-based pretraining, and the current large language model regime. Existing accounts of this history te...

Evaluating LLM-Generated Conceptual Models: A Theoretical Approach for Formalizing the Calculation of Metrics on the Meta Level

Multi-Crop Leaf Disease Detection with LLM-Assisted Diagnosis and Explainable AI

Early identification of plant diseases is crucial for maintaining agricultural productivity and ensuring food security. Manually inspecting crop leaves takes a lot of time and requires expertise, making automated disease detection systems very helpful. This paper presents a framework for detecting leaf diseases in mult...