What Robots Do Matters More Than What They Look Like: Task Context Shapes Trust in Educational HRI

• URL: http://arxiv.org/abs/2606.14602v1
• Date: Fri, 12 Jun 2026 16:23:49 GMT
• Title: What Robots Do Matters More Than What They Look Like: Task Context Shapes Trust in Educational HRI
• Abstract summary: Socially assistive robots (SARs) are increasingly deployed in educational and information-sharing contexts.<n>We examine how robot appearance and task type jointly influence trust in robots.
• Score: 0.6299766708197883
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Socially assistive robots (SARs) are increasingly deployed in educational and information-sharing contexts, supported by advances in large language models that enable fluent real-time interaction. Despite the growing diversity of robot embodiments, it remains unclear whether a single robot appearance is appropriate across different interaction tasks or whether trust depends primarily on contextual factors. In this study, we examine how robot appearance and task type jointly influence trust in robots. Using a within-subjects video-based experiment (N = 81), participants evaluated three robots with distinct appearances while performing three educationally relevant tasks: teaching, procedural instruction, and personal-information discussion. Results from repeated-measures analyses show a strong main effect of task on trust, with participants reporting the highest trust during instructional guidance, moderate trust during teaching activities, and significantly lower trust when robots requested personal information. In contrast, robot appearance showed no significant main effect, and the interaction between appearance and task was marginal. These findings suggest that trust in human-robot interaction is shaped more strongly by task context than by physical embodiment alone. By focusing on future educators as end users, this work contributes empirical evidence toward task-aware robot deployment in educational environments and highlights the importance of aligning robot roles and behaviors with interaction goals rather than relying solely on anthropomorphic design.

Related papers

• Human-Robot Mutual Learning through Affective-Linguistic Interaction and Differential Outcomes Training [Pre-Print] [0.3811184252495269]
  We test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context. Taking inspiration from child- caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs.
  arXiv  Detail & Related papers  (2024-07-01T13:35:08Z)
• Human-Robot Interaction and Perceived Irrationality: A Study of Trust Dynamics and Error Acknowledgment [0.0]
  This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures.<n>We conducted a four-stage survey to explore how trust evolves throughout human-robot interactions.<n>Results indicate that trust in robotic systems significantly increased when robots acknowledged their errors or limitations.
  arXiv  Detail & Related papers  (2024-03-21T11:00:11Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• "Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration [0.8287206589886881]
  Personalization of assistive robots is positively correlated with robot adoption and user perceptions. Our findings indicate that increased levels of customization was associated with higher trust and comfort perceptions.
  arXiv  Detail & Related papers  (2023-10-28T19:31:40Z)
• HandMeThat: Human-Robot Communication in Physical and Social Environments [73.91355172754717]
  HandMeThat is a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. HandMeThat contains 10,000 episodes of human-robot interactions. We show that both offline and online reinforcement learning algorithms perform poorly on HandMeThat.
  arXiv  Detail & Related papers  (2023-10-05T16:14:46Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Evaluation of Performance-Trust vs Moral-Trust Violation in 3D Environment [1.4502611532302039]
  We aim to design an experiment to investigate the consequences of performance-trust violation and moral-trust violation in a search and rescue scenario. We want to see if two similar robot failures, one caused by a performance-trust violation and the other by a moral-trust violation have distinct effects on human trust.
  arXiv  Detail & Related papers  (2022-06-30T17:27:09Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• Impact of Explanation on Trust of a Novel Mobile Robot [1.603423221531441]
  The presence of an explanation of expected robot behavior affected a supervisor's trust in an autonomous robot. We measured trust both subjectively through surveys and objectively through a dual-task experiment design.
  arXiv  Detail & Related papers  (2021-01-26T14:36:26Z)
• Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
  We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
  arXiv  Detail & Related papers  (2020-07-24T23:35:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.