Robot Assisted 3D Block Building to Augment Spatial Visualization Skills in Children - An exploratory study

The unique social presence of robots can be leveraged in learning situations to increase comfortability and engagement of kids, while still providing instructional guidance. When and how to interfere to provide feedback on their mistakes is still not fully clear. One effective feedback strategy used by human tutors is to implicitly inform the students of their errors rather than explicitly providing corrective feedback. This essay explores if and how a social robot can be utilized to provide implicit feedback to a user who is performing spatial visualization tasks. We explore impact of implicit and explicit feedback strategies on user's learning gains, self-regulation and perception of robot during 3D block building tasks in one-on-one child-robot tutoring. We demonstrate a realtime system that tracks the assembly of a 3D block structure using a RealSense RGB-D camera. The system allows three control actions: Add, Remove and Adjust on blocks of four basic colors to manipulate the structure in the play area. 3D structures can be authored in the Learning mode for system to record, and tracking enables the robot to provide selected feedback in the Teaching mode depending on the type of mistake made by the user. Proposed system can detect five types of mistakes i.e., mistake in: shape, color, orientation, level from base and position of the block. The feedback provided by the robot is based on mistake made by the user. Either implicit or explicit feedback, chosen randomly, is narrated by the robot. Various feedback statements are designed to implicitly inform the user of the mistake made. Two robot behaviours have been designed to support the effective delivery of feedback statements i.e., nodding and referential gaze. We conducted an exploratory study to evaluate our robot assisted 3D block building system to augment spatial visualization skills with one participant. We found that the system was easy to use. The robot was perceived as trustworthy, fun and interesting. Intentions of the robot are communicated through feedback statements and its behaviour. Our goal is to explore that the suggestion of mistakes in implicit ways can help the users self-regulate and scaffold their learning processes

LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines

Conventional Voice Assistants (VAs) rely on traditional language models to discern user intent and respond to their queries, leading to interactions that often lack a broader contextual understanding, an area in which Large Language Models (LLMs) excel. However, current LLMs are largely designed for text-based interactions, thus making it unclear how user interactions will evolve if their modality is changed to voice. In this work, we investigate whether LLMs can enrich VA interactions via an exploratory study with participants (N=20) using a ChatGPT-powered VA for three scenarios (medical self-diagnosis, creative planning, and debate) with varied constraints, stakes, and objectivity. We observe that LLM-powered VA elicits richer interaction patterns that vary across tasks, showing its versatility. Notably, LLMs absorb the majority of VA intent recognition failures. We additionally discuss the potential of harnessing LLMs for more resilient and fluid user-VA interactions and provide design guidelines for tailoring LLMs for voice assistance

"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.Comment: Under submission to CHI202

SmartSIM - a virtual reality simulator for laparoscopy training using a generic physics engine

International audienceVirtual reality (VR) training simulators have started playing a vital role in enhancing surgical skills, such as hand–eye coordination in laparoscopy, and practicing surgical scenarios that cannot be easily created using physical models. We describe a new VR simulator for basic training in lapa-roscopy, i.e. SmartSIM, which has been developed using a generic open‐source physics engine called the simulation open framework architecture (SOFA). This paper describes the systems perspective of SmartSIM including design details of both hardware and software components, while highlighting the critical design decisions. Some of the distinguishing features of SmartSIM include: (i) an easy‐to‐fabricate custom‐built hardware interface; (ii) use of a generic physics engine to facilitate wider accessibility of our work and flexibility in terms of using various graph-ical modelling algorithms and their implementations; and (iii) an intelligent and smart evaluation mechanism that facilitates unsupervised and independent learning

Correlation between serum ferritin level and liver function tests in thalassemic patients receiving multiple blood transfusions

Background:Multiple blood transfusions are the mainstay of thalassemic patients in order to combat the severe anemia. These frequent blood transfusions result in the excessive iron deposition, leading to multiple injuries to a variety of organs in the body. In response to these injuries, the levels of various enzymes are disturbed. The whole phenomena usually involve the interrelation of one parameter with some other. The present study aimed to estimate the levels of serum ferritin and hepatic enzymes and to find out any possible correlation between them in thalassemic patients receiving multiple blood transfusions.  Methods:A total number of 90 thalassemic patients of both sexes ranging from 10-15 years, receiving multiple blood transfusions were included in the present study. Blood samples from all the patients were withdrawn and analyzed for the values of serum ferritin, hemoglobin and hepatic enzymes (serum alanine transaminase, serum aspartate transaminase, serum alkaline phosphatase). Pearson correlation coefficient was applied to observe correlation between serum ferritin level and hepatic enzymes. A P value of ≤0.05 was considered statistically significant.Results:The overall values of serum ferritin, and hepatic enzymes (serum Alanine Transaminase, serum Aspartate Transaminase, serum Alkaline Phosphatase) were remarkably increased than their normal values. However, hemoglobin level was considerably decreased in thalassemic patients. A weak positive insignificant correlation was observed between serum ferritin with hepatic enzymes and hemoglobin in thalassemic patients.Conclusion:Multiple blood transfusions cause iron overload in the body, which in turn, lead to increased serum ferritin levels in thalassemic patients. High levels of hepatic enzymes are somewhat correlated to serum ferritin concentration. However, the exact reason of elevated levels is still unclear. Further detailed studies should be conducted in order to identify the exact mechanism behind this and to search for the promising correlations of various parameters in thalassemic patients receiving multiple blood transfusions.