Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI

settingsOrder Article Reprints Open AccessArticle Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI by Khashayar Ghamati 1,2,*ORCID,Maryam Banitalebi Dehkordi 1,2ORCID andAbolfazl Zaraki 1,2ORCID 1 School of Physics, Engineering and Computer Science (SPECS), University of Hertfordshire, Hatfield AL10 9AB, UK 2 Robotics Research Group, University of Hertfordshire, Hatfield AL10 9AB, UK * Author to whom correspondence should be addressed. Sensors 2025, 25(7), 2024; https://doi.org/10.3390/s25072024 Submission received: 29 December 2024 / Revised: 14 March 2025 / Accepted: 21 March 2025 / Published: 24 March 2025 (This article belongs to the Special Issue Big Data Analytics, the Internet of Things (IoTs), and Robotics) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language models within the field of human–robot interaction and human–computer interaction. While research in this field has primarily focused on the technical deployment of LLMs, critical academic challenges persist regarding their ability to adapt dynamically to user-specific contexts and evolving environments. To address this fundamental gap, we present a methodology for personalising LLMs using domain-specific data and tests using the NeuroSense EEG dataset. By enabling the personalised data interpretation, our approach promotes conventional implementation strategies, contributing to ongoing research on AI adaptability and user-centric application. Furthermore, this study engages with the broader ethical dimensions of PLLM, critically discussing issues of generalisability and data privacy concerns in AI research. Our findings demonstrate the usability of using the PLLM in a human–robot interaction scenario in real-world settings, highlighting its applicability across diverse domains, including healthcare, education, and assistive technologies. We believe the proposed system represents a significant step towards AI adaptability and personalisation, offering substantial benefits across a range of fields.