Towards neuroadaptive chatbots: a feasibility study.

ABSTRACT:

Introduction

Large-language models (LLMs) are transforming most industries today and are set to become a cornerstone of the human digital experience. While integrating explicit human feedback into the training and development of LLM-based chatbots has been integral to the progress we see nowadays, more work is needed to understand how to best align them with human values. Implicit human feedback enabled by passive brain-computer interfaces (pBCIs) could potentially help unlock the hidden nuance of users' cognitive and affective states during interaction with chatbots. This study proposes an investigation on the feasibility of using pBCIs to decode mental states in reaction to text stimuli, to lay the groundwork for neuroadaptive chatbots.

Methods

Two paradigms were created t

SUBMITTER: Gherman DE 

PROVIDER: S-EPMC12568581 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature