The internship aims to develop a controller for a social mobile robot to have a conversation with people using large language models (LLMs) such as ChatGPT. The internship is part of the European SPRING project, which aims to develop mobile robots for healthcare environments. The intern will develop a controller (Python, ROS) for ARI, the social robot. The controller will navigate towards a human (or group), have a conversation with them, and leave the conversation. The intern will use existing components from the SPRING project such as mapping and localization of the robot and humans, human-aware navigation, speech recognition, and a simple dialogue system based on ChatGPT. The intern will also investigate how to optimally use LLMs such as ChatGPT for natural and comfortable conversation with the robot, for example, by using prompt engineering. The intern will have the chance to develop and implement their own ideas to improve the conversation with the robot, for example, by investigating gaze, gestures, or emotions.

Qualitative coding is the process of categorizing and labeling raw data to identify themes, patterns, and concepts within qualitative research. This process requires significant time, reflection, and discussion, often characterized by inherent subjectivity and uncertainty. Here, we explore the possibility to leverage large language models (LLM) to enhance the process and assist researchers with qualitative coding. LLMs, trained on extensive human-generated text, possess an architecture that renders them capable of understanding the broader context of a conversation or text. This allows them to extract patterns and meaning effectively, making them particularly useful for the accurate extraction and coding of relevant themes. In our current approach, we employed the chatGPT 3.5 Turbo API, integrating it into the qualitative coding process for data from the SWISS100 study, specifically focusing on data derived from centenarians' experiences during the Covid-19 pandemic, as well as a systematic centenarian literature review. We provide several instances illustrating how our approach can assist researchers with extracting and coding relevant themes. With data from human coders on hand, we highlight points of convergence and divergence between AI and human thematic coding in the context of these data. Moving forward, our goal is to enhance the prototype and integrate it within an LLM designed for local storage and operation (LLaMa). Our initial findings highlight the potential of AI-enhanced qualitative coding, yet they also pinpoint areas requiring attention. Based on these observations, we formulate tentative recommendations for the optimal integration of LLMs in qualitative coding research. Further evaluations using varied datasets and comparisons among different LLMs will shed more light on the question of whether and how to integrate these models into this domain.