Does It Affect You? Social and Learning Implications of Using Cognitive-Affective State Recognition for Proactive Human-Robot Tutoring

• URL: http://arxiv.org/abs/2212.10346v1
• Date: Tue, 20 Dec 2022 15:31:58 GMT
• Title: Does It Affect You? Social and Learning Implications of Using Cognitive-Affective State Recognition for Proactive Human-Robot Tutoring
• Authors: Matthias Kraus, Diana Betancourt, Wolfgang Minker
• Abstract summary: This paper investigates how the student's cognitive-affective states can be used by a robotic tutor for triggering proactive tutoring dialogue. We studied whether the initiation of proactive behaviour after the detection of signs of confusion improves the student's concentration and trust in the agent. The results show that high proactive behaviour harms trust, especially when triggered during negative cognitive-affective states.
• Score: 4.384546153204966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using robots in educational contexts has already shown to be beneficial for a student's learning and social behaviour. For levitating them to the next level of providing more effective and human-like tutoring, the ability to adapt to the user and to express proactivity is fundamental. By acting proactively, intelligent robotic tutors anticipate possible situations where problems for the student may arise and act in advance for preventing negative outcomes. Still, the decisions of when and how to behave proactively are open questions. Therefore, this paper deals with the investigation of how the student's cognitive-affective states can be used by a robotic tutor for triggering proactive tutoring dialogue. In doing so, it is aimed to improve the learning experience. For this reason, a concept learning task scenario was observed where a robotic assistant proactively helped when negative user states were detected. In a learning task, the user's states of frustration and confusion were deemed to have negative effects on the outcome of the task and were used to trigger proactive behaviour. In an empirical user study with 40 undergraduate and doctoral students, we studied whether the initiation of proactive behaviour after the detection of signs of confusion and frustration improves the student's concentration and trust in the agent. Additionally, we investigated which level of proactive dialogue is useful for promoting the student's concentration and trust. The results show that high proactive behaviour harms trust, especially when triggered during negative cognitive-affective states but contributes to keeping the student focused on the task when triggered in these states. Based on our study results, we further discuss future steps for improving the proactive assistance of robotic tutoring systems.

Related papers

• Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data [0.0]
  Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system. This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals.
  arXiv  Detail & Related papers  (2024-10-18T04:54:46Z)
• Socially Assistive Robot in Sexual Health: Group and Individual Student-Robot Interaction Activities Promoting Disclosure, Learning and Positive Attitudes [0.0]
  Socially assistive robots (SARs) sometimes are perceived as more trustworthy than humans. Students were more open to asking SE-related questions to the robot than their human teacher.
  arXiv  Detail & Related papers  (2024-07-17T21:36:21Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning [3.9011896000134825]
  Well-defined proactive behavior may improve human-machine cooperation. We propose a novel approach including both social as well as task-relevant features in the dialog.
  arXiv  Detail & Related papers  (2022-11-25T14:29:26Z)
• I am Only Happy When There is Light: The Impact of Environmental Changes on Affective Facial Expressions Recognition [65.69256728493015]
  We study the impact of different image conditions on the recognition of arousal from human facial expressions. Our results show how the interpretation of human affective states can differ greatly in either the positive or negative direction.
  arXiv  Detail & Related papers  (2022-10-28T16:28:26Z)
• When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning [57.53138994155612]
  A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. We propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them.
  arXiv  Detail & Related papers  (2022-10-19T17:57:24Z)
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy [83.61404191470126]
  We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting. Specifically, we learn the trade-off via a learned goal weight, which consists of four factors. The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
  arXiv  Detail & Related papers  (2022-04-07T14:11:31Z)
• Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue [67.89143112645556]
  We provide a NICO robot with the ability to learn the associations between a perceived auditory stimulus and an emotional expression. NICO is able to do this for both individual subjects and specific stimuli, with the aid of an emotion-driven dialogue system. The robot is then able to use this information to determine a subject's enjoyment of perceived auditory stimuli in a real HRI scenario.
  arXiv  Detail & Related papers  (2021-03-05T20:55:48Z)
• A Robotic Positive Psychology Coach to Improve College Students' Wellbeing [16.70932067272569]
  We investigate the use of a social robot coach to deliver positive psychology interventions to college students living in on-campus dormitories. We found a statistically significant improvement in participants' psychological wellbeing, mood, and readiness to change behavior for improved wellbeing after they completed the study. Students' personality traits were found to have a significant association with intervention efficacy.
  arXiv  Detail & Related papers  (2020-09-08T15:51:11Z)
• SensAI+Expanse Emotional Valence Prediction Studies with Cognition and Memory Integration [0.0]
  This work contributes with an artificial intelligent agent able to assist on cognitive science studies. The developed artificial agent system (SensAI+Expanse) includes machine learning algorithms, empathetic algorithms, and memory. Results of the present study show evidence of significant emotional behaviour differences between some age ranges and gender combinations.
  arXiv  Detail & Related papers  (2020-01-03T18:17:57Z)

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.