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

: 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 usercentric 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.