This conference paper by Chutisant Kerdvibulvech and Xudong Jiang, presented at HCII 2025, explores how generative AI is reshaping Human-Computer Interaction (HCI) across four key dimensions: user interaction, AI-driven emotional recognition, ethical considerations, and future directions. The authors argue that while traditional HCI frameworks relied on rule-based systems, modern AI-driven approaches incorporate dynamic learning, natural language processing, and emotion recognition to create more adaptive, intuitive, and personalized user experiences.
The paper highlights both the promise and the challenges of integrating AI into HCI. While AI significantly enhances usability and accessibility, the authors emphasize that issues such as algorithmic bias, data privacy, and ethical transparency must be carefully addressed to ensure responsible implementation. The work is published as part of the Lecture Notes in Computer Science series (vol. 15821) by Springer, and was partially supported by the National Institute of Development Administration (NIDA) in Thailand. The paper draws on a broad range of references spanning healthcare AI, metaverse learning, multimodal emotion recognition, and AI chatbot effectiveness, positioning it as a survey-style contribution mapping the evolving landscape of AI-enhanced HCI.
This Scientific Reports paper provides a comprehensive review of large language models (LLMs) and their applications across major industries. The authors trace the evolution of LLMs from early statistical language models and RNNs through the introduction of transformer architectures, which enabled breakthroughs like BERT, GPT, and LLaMA. The review covers key technical aspects including attention mechanisms, training methods, and evaluation metrics, providing a foundation for understanding how these models function at scale.
The study examines LLM applications across five major industries: healthcare and medicine (disease diagnosis, treatment planning, patient data management), automotive (predictive maintenance, autonomous driving), e-commerce (recommendation systems, consumer behavior analysis), education (personalized learning, automated grading), and finance and banking (fraud detection, risk management, customer service automation). The authors distinguish their work from prior reviews by covering multiple industries simultaneously rather than focusing on just one or two domains.
Despite the transformative potential, the paper highlights significant challenges including ethical concerns, biases in training data, data security and privacy issues, and substantial computational resource requirements. The authors argue these must be addressed for responsible and sustainable deployment. The review synthesizes over 100 research articles from 2020–2024, offering researchers a consolidated view of the current state and future directions of industrial LLM adoption.
