The educational applications of extended reality (XR) modalities, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), have increased significantly over the last ten years. Many educators within the Architecture, Engineering, and Construction (AEC) related degree programs see student benefits that could be derived from bringing these modalities into classrooms, which include but are not limited to: a better understanding of each of the subdisciplines and the coordination necessary between them, visualizing oneself as a professional in AEC, and visualization of difficult concepts to increase engagement, self-efficacy, and learning. These benefits, in turn, help recruitment and retention efforts for these degree programs. However, given the number of technologies available and the fact that they quickly become outdated, there is confusion about the definitions of the different XR modalities and their unique capabilities. This lack of knowledge, combined with limited faculty time and lack of financial resources, can make it overwhelming for educators to choose the right XR modality to accomplish particular educational objectives. There is a lack of guidance in the literature for AEC educators to consider various factors that affect the success of an XR intervention. Grounded in a comprehensive literature review and the educational framework of the Model of Domain Learning, this paper proposes a decision-making framework to help AEC educators select the appropriate technologies, platforms, and devices to use for various educational outcomes (e.g., learning, interest generation, engagement) considering factors such as budget, scalability, space/equipment needs, and the potential benefits and limitations of each XR modality. To this end, a comprehensive review of the literature was performed to decipher various definitions of XR modalities and how they have been previously utilized in AEC Education. The framework was then successfully validated at a summer camp in the School of Building Construction at Georgia Institute of Technology, highlighting the importance of using appropriate XR technologies depending on the educational context.

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Noise invades all aspects of our lives. The word noise is actually derived from the Latin word “nausea,” with one possible connection being that unpleasant sounds were made by seasick passengers or sailors in ancient times. In modern times, the demand for noise research and consulting has intensified in concert with rising population densities, growing industrialized societies, escalating demands from consumers, and increasingly common standards and legislation related to noise. The Acoustical Society of America Technical Committee on Noise (TC Noise) is concerned with all aspects of noise, ranging from noise generation and propagation, to active and passive methods of controlling noise, to the effects of noise on humans and animals. This talk will explore the broad topic of noise and its impact on our world.

The development of intelligent medical service robots for patient care presents significant chal-lenges, particularly in integrating diverse knowledge sources and enabling robots to autono-mously perform tasks in dynamic and unpredictable healthcare environments. This study in-troduces a novel framework that combines large language models (LLMs) with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant (RHA). Utilizing OpenAI’s ChatGPT, the RHA processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the RHA successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the LLM and predefined task specifications. The results demonstrate the potential of LLMs to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural lan-guage.