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Exploring LLM-powered multi-session human-robot interactions with university students.

Mauliana Mauliana1, Ashita Ashok1, Daniela Czernochowski2

  • 1Robotics Research Lab, RPTU Kaiserslautern-Landau, Department of Computer Science, Kaiserslautern, Germany.

Frontiers in Robotics and AI
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

This study explored user interaction with a large language model (LLM)-powered social humanoid robot (SHR). Findings show sustained engagement but highlight technical challenges impacting long-term adoption in educational settings.

Keywords:
generative AIhuman-robot interactionlarge-language modelssocial robotsuser studies

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Social Robotics

Background:

  • Social humanoid robots (SHRs) are increasingly integrated into various environments.
  • Large language models (LLMs) offer potential for naturalistic human-robot interaction.
  • Understanding user perceptions and adoption factors is crucial for SHR deployment.

Purpose of the Study:

  • To investigate the impact of multi-session, open-domain interactions with an LLM-powered SHR on user perceptions.
  • To assess user willingness for adoption of such robots in a university setting.
  • To identify factors influencing long-term social engagement with SHRs.

Main Methods:

  • Exploratory study involving 13 university students interacting with an LLM-powered SHR (EMAH) over four weekly sessions.
  • Utilized a compact open-source LLM (Flan-T5-Large) for multi-turn conversations.
  • Employed mixed-method measures: subjective ratings, behavioral observations, and conversational analysis.

Main Results:

  • Perceptions of sociability, agency, and engagement remained stable; engagement was sustained despite repeated exposure.
  • Perceived animacy increased with familiarity, but disturbance ratings did not significantly decline.
  • Technical challenges (response time, speech recognition) correlated with a mid-study drop in interaction metrics; prior robot experience did not predict adoption willingness.

Conclusions:

  • LLM-powered robots can maintain open-domain interactions, but technical robustness is key.
  • Personalized conversation strategies and expectation management are needed for long-term social engagement.
  • Actionable insights are provided for advancing humanoid robot deployment in educational contexts.