Publications

@inproceedings{li_enhancing_2025,

title = {Enhancing Linear Algebra Education with AI-Generated Content in the CityU Metaverse: A Comparative Study},

author = {Y. Li and E. C. H. Pang and C. S. Y. Ng and M. Azim and H. Leung},

editor = {Hao T. and Wu J.G. and Luo X. and Sun Y. and Mu Y. and Ge S. and Xie W.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003632691&doi=10.1007%2f978-981-96-4407-0_1&partnerID=40&md5=c067ba5d4c15e9c0353bf315680531fc},

doi = {10.1007/978-981-96-4407-0_1},

isbn = {03029743 (ISSN); 978-981964406-3 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15589 LNCS},

pages = {3–16},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {In today’s digital age, the metaverse is emerging as the forthcoming evolution of the internet. It provides an immersive space that marks a new frontier in the way digital interactions are facilitated and experienced. In this paper, we present the CityU Metaverse, which aims to construct a digital twin of our university campus. It is designed as an educational virtual world where learning applications can be embedded in this virtual campus, supporting not only remote and collaborative learning but also professional technical training to enhance educational experiences through immersive and interactive learning. To evaluate the effectiveness of this educational metaverse, we conducted an experiment focused on 3D linear transformation in linear algebra, with teaching content generated by generative AI, comparing our metaverse system with traditional teaching methods. Knowledge tests and surveys assessing learning interest revealed that students engaged with the CityU Metaverse, facilitated by AI-generated content, outperformed those in traditional settings and reported greater enjoyment during the learning process. The work provides valuable perspectives on the behaviors and interactions within the metaverse by analyzing user preferences and learning outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},

keywords = {Comparatives studies, Digital age, Digital interactions, digital twin, Educational metaverse, Engineering education, Generative AI, Immersive, Matrix algebra, Metaverse, Metaverses, Personnel training, Students, Teaching, University campus, Virtual environments, virtual learning environment, Virtual learning environments, Virtual Reality, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{weerasinghe_cross-device_2025,

title = {A Cross-Device Interaction with the Smartphone and HMD for Vocabulary Learning},

author = {M. Weerasinghe and M. Kljun and K. Č. Pucihar},

editor = {Zaina L. and Campos J.C. and Spano D. and Luyten K. and Palanque P. and Veer G. and Ebert A. and Humayoun S.R. and Memmesheimer V.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007828696&doi=10.1007%2f978-3-031-91760-8_18&partnerID=40&md5=4ebf202715ba880dcfeb3232dba7e2c4},

doi = {10.1007/978-3-031-91760-8_18},

isbn = {03029743 (ISSN); 978-303191759-2 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15518 LNCS},

pages = {269–282},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Cross-reality (XR) systems facilitate interaction between devices with differing levels of virtual content. By engaging with a variety of such devices, XR systems offer the flexibility to choose the most suitable modality for specific task or context. This capability enables rich applications in training and education, including vocabulary learning. Vocabulary acquisition is a vital part of language learning, employing techniques such as words rehearsing, flashcards, labelling environments with post-it notes, and mnemonic strategies such as the keyword method. Traditional mnemonics typically rely on visual stimuli or mental visualisations. Recent research highlights that AR can enhance vocabulary learning by combining real objects with augmented stimuli such as in labelling environments. Additionally,advancements in generative AI now enable high-quality, synthetically generated images from text descriptions, facilitating externalisation of personalised visual stimuli of mental visualisations. However, creating interfaces for effective real-world augmentation remains challenging, particularly given the limited text input capabilities of Head-Mounted Displays (HMDs). This work presents an XR system that combines smartphones and HMDs by leveraging Augmented Reality (AR) for contextually relevant information and a smartphone for efficient text input. The system enables users to visually annotate objects with personalised images of keyword associations generated with DALL-E 2. To evaluate the system, we conducted a user study with 16 university graduate students, assessing both usability and overall user experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Augmented Reality, Context-based, Context-based vocabulary learning, Cross-reality interaction, Engineering education, Head-mounted displays, Head-mounted-displays, Images synthesis, Keyword method, Mixed reality, Smart phones, Smartphones, Students, Text-to-image synthesis, Visualization, Vocabulary learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{juarez_innovative_2025,

title = {Innovative Methodology for the Integration of Emerging Technologies in Global Education: Mixed Realities, AI, Metaverse, and SDGs},

author = {A. Juarez and J. Rábago and A. Pliego and G. Salazar and C. Hinrichsen and M. Castro and T. Pachajoa},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011951378&doi=10.1109%2FIFE63672.2025.11024834&partnerID=40&md5=4e101ad649487ce729c3a5fa9e875559},

doi = {10.1109/IFE63672.2025.11024834},

isbn = {9798350355239 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The academic collaboration among Tecnologico de Monterrey (Mexico), the University of San Sebastián (Chile), and the Catholic University of Colombia was an innovative effort to transform the teaching of the "Formal Representation of Space" through the use of emerging technologies. This project was based on the convergence of the theory of Community of Inquiry (CoI), International Collaborative Online Learning (COIL), and the integration of Mixed Realities, Metaverse, and generative artificial intelligence. The central objective of this collaboration was to improve the technical and creative skills of students of architecture, industrial design, digital art, communication, and music production through a pedagogical approach that utilizes 3D spatial visualization and intercultural interaction. The use of the Tec Virtual Campus's Metaverse and the Global Classroom program was instrumental in facilitating real-time collaboration among students from different countries, allowing for the creation of joint projects that reflect a deep understanding of the Sustainable Development Goals (SDGs). This effort resulted in an advanced methodology that improves students' technical skills and promotes a meaningful global commitment to sustainability and social responsibility, reflecting the transformative power of international collaborative education. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Artificial intelligence, Arts computing, Collaborative learning, E-Learning, Education computing, Educational Innovation, Educational innovations, Educational Technology, Emerging technologies, Engineering education, Global education, High educations, higher education, Innovative methodologies, Me-xico, Metaverse, Metaverses, Mixed Realities, Mixed reality, Product design, Sebastian, Social aspects, Students, Sustainable development, Sustainable Development Goals, Teaching, Technical skills},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{anvitha_edubot_2025,

title = {EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval},

author = {K. Anvitha and T. Durjay and K. Sathvika and G. Gnanendra and S. Annamalai and S. K. Natarajan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013615976&doi=10.1109%2FGINOTECH63460.2025.11077097&partnerID=40&md5=b08377283f2ea2ee406d38d1d23f1e42},

doi = {10.1109/GINOTECH63460.2025.11077097},

isbn = {9798331507756 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{boubakri_exploring_2025,

title = {Exploring 3D Cardiac Anatomy with Text-Based AI Guidance in Virtual Reality},

author = {F. -E. Boubakri and M. Kadri and F. Z. Kaghat and A. Azough and H. Tairi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015676741&doi=10.1109%2FSCME62582.2025.11104869&partnerID=40&md5=c961694f97c50adc23b6826dddb265cd},

doi = {10.1109/SCME62582.2025.11104869},

isbn = {9798331534899 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {43–48},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {This paper presents the design and development of a social virtual reality (VR) classroom focused on cardiac anatomy education for students in grades K-12. The application allows multiple learners to explore a detailed 3D heart model within an immersive and collaborative environment. A crucial part of the system is the integration of a text-based conversational AI interface powered by ChatGPT, which provides immediate, interactive explanations and addresses student inquiries about heart anatomy. The system supports both guided and exploratory learning modes, encourages peer collaboration, and offers personalized support through natural language dialogue. We evaluated the system's effectiveness through a comprehensive study measuring learning perception (LPQ), VR perception (VRPQ), AI perception (AIPQ), and VR-related symptoms (VRSQ). Potential applications include making high-quality cardiac anatomy education more affordable for K-12 schools with limited resources, offering an adaptable AI-based tutoring system for students to learn at their own pace, and equipping educators with an easy-to-use tool to integrate into their science curriculum with minimal additional training. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D cardiac anatomy, 3d heart models, Anatomy education, Anatomy educations, Cardiac anatomy, Collaborative environments, Collaborative learning, Computer aided instruction, Curricula, Design and Development, E-Learning, Education computing, Generative AI, Heart, Immersive environment, Learning systems, Natural language processing systems, Social virtual reality, Students, Teaching, Three dimensional computer graphics, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhao_role_2025,

title = {The Role of 3D Virtual Humans in Communication and Assisting Students' Learning in Transparent Display Environments: Perspectives of Pre-Service Teachers},

author = {P. Zhao and X. Wei},

editor = {K. T. Chui and C. Jaikaeo and J. Niramitranon and W. Kaewmanee and K. -K. Ng and P. Ongkunaruk},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015746241&doi=10.1109%2FISET65607.2025.00069&partnerID=40&md5=08c39b84fa6bd6ac13ddbed203d7b1d9},

doi = {10.1109/ISET65607.2025.00069},

isbn = {9798331595500 (ISBN)},

year  = {2025},

date = {2025-01-01},

pages = {319–323},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The integration of transparent display and 3D virtual human technologies into education is expanding rapidly; however, their systematic incorporation into the CDIO teaching model remains underexplored, particularly in supporting complex knowledge delivery and collaborative practice. This study developed an intelligent virtual teacher assistance system based on generative AI and conducted a teaching experiment combining transparent display and 3D virtual human technologies. Feedback was collected through focus group interviews with 24 pre-service teachers. Results show that the virtual human, through natural language and multimodal interaction, significantly enhanced classroom engagement and contextual understanding, while its real-time feedback and personalized guidance effectively supported CDIO-based collaborative learning. Nonetheless, challenges remain in contextual adaptability and emotional feedback accuracy. Accordingly, the study proposes a path for technical optimization through the integration of multimodal emotion recognition, adaptive instructional algorithms, and nonintrusive data collection, offering empirical and theoretical insights into educational technology integration within the CDIO framework and future intelligent learning tools. © 2025 Elsevier B.V., All rights reserved.},

keywords = {3D virtual human, Assistive technology, CDIO teaching model, Collaborative learning, Collaborative practices, Display environments, E-Learning, Educational Technology, Engineering education, feedback, Integration, Knowledge delivery, Knowledge transfer, Learning algorithms, Natural language processing systems, Preservice teachers, Psychology computing, Student learning, Students, Teaching, Teaching model, Transparent display environment, Transparent displays, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koizumi_development_2025,

title = {Development and Assessment of a System to Help Students Improve Self-compassion},

author = {M. Koizumi and M. Ohsuga and J. M. Corchado},

editor = {Chinthaginjala R. and Sitek P. and Min-Allah N. and Matsui K. and Ossowski S. and Rodríguez S.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218979175&doi=10.1007%2f978-3-031-82073-1_5&partnerID=40&md5=b136d4a114ce5acfa89f907ccecc145f},

doi = {10.1007/978-3-031-82073-1_5},

isbn = {23673370 (ISSN); 978-303182072-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Networks Syst.},

volume = {1259},

pages = {43–52},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ly_museum_2025,

title = {Museum in the Classroom: Engaging Students with Augmented Reality Museum Artifacts and Generative AI},

author = {C. Ly and E. Peng and K. Liu and A. Qin and G. Howe and A. Y. Cheng and A. Cuadra},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005741934&doi=10.1145%2F3706599.3719787&partnerID=40&md5=0a405b14f099e1132e32dd10c121eb37},

doi = {10.1145/3706599.3719787},

isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Museum field trips provide a rich learning experience for children. However, they are complex and expensive for teachers to organize. Fortunately, digitization of museum artifacts makes it possible to use museum resources within the classroom. Museum in the Classroom (MITC) explores how augmented reality (AR) and generative artificial intelligence (AI) can create an interactive learning experience around museum artifacts. This iPad app allows educators to select historical topics from a curated artifact library, generating AR-based exhibits that students can explore. MITC engages students through interactive AR artifacts, AI-driven chatbots, and AI-generated quiz questions, based on a real exhibition at the Cantor Arts Center at Stanford University. A formative study with middle schoolers (N = 20) demonstrated that the app increased engagement compared to traditional learning methods. MITC also fostered a playful and comfortable environment to interact with museum artifacts. Our findings suggest that combining AR and AI has the potential to enrich classroom learning and offer a scalable alternative to traditional museum visits. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Artifact or System, Child/parent, Children/Parents, Digitisation, Education/Learning, Engaging students, Engineering education, Field trips, Interactive learning, Learning experiences, Rich learning experiences, Students, Teachers', Teaching},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhu_designing_2025,

title = {Designing VR Simulation System for Clinical Communication Training with LLMs-Based Embodied Conversational Agents},

author = {X. T. Zhu and H. Cheerman and M. Cheng and S. R. Kiami and L. Chukoskie and E. McGivney},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005754066&doi=10.1145%2F3706599.3719693&partnerID=40&md5=6ad72d5adf98c2ca2437b5a3f6508a88},

doi = {10.1145/3706599.3719693},

isbn = {9798400713958 (ISBN); 9798400713941 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {VR simulation in Health Professions (HP) education demonstrates huge potential, but fixed learning content with little customization limits its application beyond lab environments. To address these limitations in the context of VR for patient communication training, we conducted a user-centered study involving semi-structured interviews with advanced HP students to understand their challenges in clinical communication training and perceptions of VR-based solutions. From this, we derived design insights emphasizing the importance of realistic scenarios, simple interactions, and unpredictable dialogues. Building on these insights, we developed the Virtual AI Patient Simulator (VAPS), a novel VR system powered by Large Language Models (LLMs) and Embodied Conversational Agents (ECAs), supporting dynamic and customizable patient interactions for immersive learning. We also provided an example of how clinical professors could use user-friendly design forms to create personalized scenarios that align with course objectives in VAPS and discuss future implications of integrating AI-driven technologies into VR education. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Clinical communications, Clinical Simulation, Communications training, Curricula, Embodied conversational agent, Embodied Conversational Agents, Health professions, Intelligent virtual agents, Language Model, Medical education, Model-based OPC, Patient simulators, Personnel training, Students, Teaching, User centered design, Virtual environments, Virtual Reality, VR simulation, VR simulation systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{harinee_elevating_2024,

title = {Elevating Medical Training: A Synergistic Fusion of AI and VR for Immersive Anatomy Learning and Practical Procedure Mastery},

author = {S. Harinee and R. Vimal Raja and E. Mugila and I. Govindharaj and V. Sanjaykumar and T. Ragavendhiran},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000334626&doi=10.1109%2FICSCAN62807.2024.10894451&partnerID=40&md5=ae7a491686ade8cebdc276f585a6f4f0},

doi = {10.1109/ICSCAN62807.2024.10894451},

isbn = {9798331510022 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. Syst., Comput., Autom. Netw., ICSCAN},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Virtual reality with its 3D visualization have brought an overwhelming change in the face of medical education, especially for courses like human anatomy. The proposed virtual reality system to bring massive improvements in the education received by a medical student studying for their degree courses. The project puts forward the text-to-speech and speech-to-text aligned system that simplifies the usage of a chatbot empowered by OpenAI GPT-4 and allows pupils to vocally speak with Avatar, the set virtual assistant. Contrary to the current methodologies, the setup of virtual reality is powered by avatars and thus covers an enhanced virtual assistant environment. Avatars offer students the set of repeated practicing of medical procedures on it, and the real uniqueness in the proposed product. The developed virtual reality environment is enhanced over other current training techniques where a student should interact and immerse in three-dimensional human organs for visualization in three dimensions and hence get better knowledge of the subjects in greater depth. A virtual assistant guides the whole process, giving insights and support to help the student bridge the gap from theory to practice. Then, the system is essentially Knowledge based and Analysis based approach. The combination of generative AI along with embodied virtual agents has great potential when it comes to customized virtual conversation assistant for much wider range of applications. The study brings out the value of acquiring hands-on skills through simulated medical procedures and opens new frontiers of research and development in AI, VR, and medical education. In addition to assessing the effectiveness of such novel functionalities, the study also explores user experience related dimensions such as usability, task loading, and the sense of presence in proposed virtual medical environment. © 2025 Elsevier B.V., All rights reserved.},

keywords = {'current, Anatomy education, Anatomy educations, Computer interaction, Curricula, Embodied virtual assistant, Embodied virtual assistants, Generative AI, Human- Computer Interaction, Immersive, Intelligent virtual agents, Medical computing, Medical education, Medical procedure practice, Medical procedures, Medical training, Personnel training, Students, Teaching, Three dimensional computer graphics, Usability engineering, Virtual assistants, Virtual environments, Virtual Reality, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{samson_fostering_2024,

title = {Fostering a Co-creation Process for the Development of an Extended Reality Healthcare Education Resource},

author = {J. Samson and P. Lameras and N. Taylor and R. Kneafsey},

editor = {Auer M.E. and Tsiatsos T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189759614&doi=10.1007%2f978-3-031-56075-0_20&partnerID=40&md5=6ae832882a2e224094c1beb81c925333},

doi = {10.1007/978-3-031-56075-0_20},

isbn = {23673370 (ISSN); 978-303156074-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Networks Syst.},

volume = {937 LNNS},

pages = {205–212},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {The aim of this research is to create an immersive healthcare education resource using an extended reality (XR) platform. This platform leverages an existing software product, incorporating virtual patients with conversational capabilities driven by artificial intelligence (AI). The initial stage produced an early prototype focused on assessing an elderly virtual patient experiencing frailty. This scenario encompasses the hospital admission to post-discharge care at home, involving various healthcare professionals such as paramedics, emergency clinicians, diagnostic radiographers, geriatricians, physiotherapists, occupational therapists, nurses, operating department practitioners, dietitians, and social workers. The plan moving forward is to refine and expand this prototype through a co-creation with diverse stakeholders. The refinement process will include the introduction of updated scripts into the standard AI model. Furthermore, these scripts will be tested against a new hybrid model that combines generative AI. Ultimately, this resource will be co-designed to create a learning activity tailored for occupational therapy and physiotherapy students. This activity will undergo testing with a cohort of students, and the outcomes of this research are expected to inform the future development of interprofessional virtual simulated placements (VSPs). These placements will complement traditional clinical learning experiences, offering students an immersive environment to enhance their skills and knowledge in the healthcare field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Artificial intelligence, Co-creation, Creation process, Diagnosis, Education computing, Education resource, Extended reality, Health care education, Hospitals, Immersive, Inter professionals, Interprofessional Healthcare Education, Software products, Students, Virtual patients},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kapadia_evaluation_2024,

title = {Evaluation of Large Language Model Generated Dialogues for an AI Based VR Nurse Training Simulator},

author = {N. Kapadia and S. Gokhale and A. Nepomuceno and W. Cheng and S. Bothwell and M. Mathews and J. S. Shallat and C. Schultz and A. Gupta},

editor = {Chen J.Y.C. and Fragomeni G.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196200653&doi=10.1007%2f978-3-031-61041-7_13&partnerID=40&md5=8890a8d0c289fdf6e7ab82e105249097},

doi = {10.1007/978-3-031-61041-7_13},

isbn = {03029743 (ISSN); 978-303161040-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {14706 LNCS},

pages = {200–212},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This paper explores the efficacy of Large Language Models (LLMs) in generating dialogues for patient avatars in Virtual Reality (VR) nurse training simulators. With the integration of technology in healthcare education evolving rapidly, the potential of NLP to enhance nurse training through realistic patient interactions presents a significant opportunity. Our study introduces a novel LLM-based dialogue generation system, leveraging models such as ChatGPT, GoogleBard, and ClaudeAI. We detail the development of our script generation system, which was a collaborative endeavor involving nurses, technical artists, and developers. The system, tested on the Meta Quest 2 VR headset, integrates complex dialogues created through a synthesis of clinical expertise and advanced NLP, aimed at simulating real-world nursing scenarios. Through a comprehensive evaluation involving lexical and semantic similarity tests compared to clinical expert-generated scripts, we assess the potential of LLMs as suitable alternatives for script generation. The findings aim to contribute to the development of a more interactive and effective VR nurse training simulator, enhancing communication skills among nursing students for improved patient care outcomes. This research underscores the importance of advanced NLP applications in healthcare education, offering insights into the practicality and limitations of employing LLMs in clinical training environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Bard, ChatGPT, ClaudeAI, Clinical research, Computational Linguistics, Dialogue Generation, Dialogue generations, Education computing, Extended reality, Health care education, Healthcare Education, Language Model, Language processing, Large language model, large language models, Natural Language Processing, Natural language processing systems, Natural languages, Nurse Training Simulation, Nursing, Patient avatar, Patient Avatars, Semantics, Students, Training simulation, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jia_nivta_2024,

title = {NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE},

author = {Y. Jia and Z. P. T. Sin and X. E. Wang and C. Li and P. H. F. Ng and X. Huang and J. Dong and Y. Wang and G. Baciu and J. Cao and Q. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211447638&doi=10.1109%2FMetaCom62920.2024.00023&partnerID=40&md5=123c4e543f778f928741ddbe174f4c79},

doi = {10.1109/MetaCom62920.2024.00023},

isbn = {9798331515997 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {57–64},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 Elsevier B.V., All rights reserved.},

keywords = {Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liu_beyond_2024,

title = {Beyond Traditional Teaching: Large Language Models as Simulated Teaching Assistants in Computer Science},

author = {M. Liu and F. M'Hiri},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189289344&doi=10.1145%2F3626252.3630789&partnerID=40&md5=7b0f42d4bf0e1706de8691200e4a0e00},

doi = {10.1145/3626252.3630789},

isbn = {9798400704246 (ISBN); 9798400704239 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {SIGCSE - Proc. ACM Tech. Symp. Comput. Sci. Educ.},

volume = {1},

pages = {743–749},

publisher = {Association for Computing Machinery, Inc},

abstract = {As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in education warrants exploration. In this study, we investigate the feasibility of employing GPT-3.5 from OpenAI, as an LLM teaching assistant (TA) or a virtual TA in computer science (CS) courses. The objective is to enhance the accessibility of CS education while maintaining academic integrity by refraining from providing direct solutions to current-semester assignments. Targeting Foundations of Programming (COMP202), an undergraduate course that introduces students to programming with Python, we have developed a virtual TA using the LangChain framework, known for integrating language models with diverse data sources and environments. The virtual TA assists students with their code and clarifies complex concepts. For homework questions, it is designed to guide students with hints rather than giving out direct solutions. We assessed its performance first through a qualitative evaluation, then a survey-based comparative analysis, using a mix of questions commonly asked on the COMP202 discussion board and questions created by the authors. Our preliminary results indicate that the virtual TA outperforms human TAs on clarity and engagement, matching them on accuracy when the question is non-assignment-specific, for which human TAs still proved more reliable. These findings suggest that while virtual TAs, leveraging the capabilities of LLMs, hold great promise towards making CS education experience more accessible and engaging, their optimal use necessitates human supervision. We conclude by identifying several directions that could be explored in future implementations. © 2024 Elsevier B.V., All rights reserved.},

keywords = {Adaptive teaching, ChatGPT, Computational Linguistics, CS education, E-Learning, Education computing, Engineering education, GPT, Language Model, LLM, machine learning, Machine-learning, Novice programmer, novice programmers, Openai, Programming, Python, Students, Teaching, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{liu_classmeta_2024,

title = {ClassMeta: Designing Interactive Virtual Classmate to Promote VR Classroom Participation},

author = {Z. Liu and Z. Zhu and L. Zhu and E. Jiang and X. Hu and K. Peppler and K. Ramani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194868458&doi=10.1145%2f3613904.3642947&partnerID=40&md5=0592b2f977a2ad2e6366c6fa05808a6a},

doi = {10.1145/3613904.3642947},

isbn = {979-840070330-0 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Peer influence plays a crucial role in promoting classroom participation, where behaviors from active students can contribute to a collective classroom learning experience. However, the presence of these active students depends on several conditions and is not consistently available across all circumstances. Recently, Large Language Models (LLMs) such as GPT have demonstrated the ability to simulate diverse human behaviors convincingly due to their capacity to generate contextually coherent responses based on their role settings. Inspired by this advancement in technology, we designed ClassMeta, a GPT-4 powered agent to help promote classroom participation by playing the role of an active student. These agents, which are embodied as 3D avatars in virtual reality, interact with actual instructors and students with both spoken language and body gestures. We conducted a comparative study to investigate the potential of ClassMeta for improving the overall learning experience of the class. © 2024 Copyright held by the owner/author(s)},

keywords = {3D Avatars, Behavioral Research, Classroom learning, Collaborative learning, Computational Linguistics, Condition, E-Learning, Human behaviors, Language Model, Large language model, Learning experiences, Learning systems, pedagogical agent, Pedagogical agents, Students, Three dimensional computer graphics, Virtual Reality, VR classroom},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chan_board_2024,

title = {Board 24: Development of Multi-User-enabled, Interactive, and Responsive Virtual/Augmented Reality-based Laboratory Training System},

author = {A. Chan and J. A. Liu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202042560&partnerID=40&md5=9b1565ea2dd4336b4cc45594fe4f7900},

isbn = {21535965 (ISSN); 9780878232413 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ASEE Annu. Conf. Expos. Conf. Proc.},

publisher = {American Society for Engineering Education},

abstract = {The Unit Operations Laboratory (UOL) is a place where third-year chemical engineering students can apply their engineering and science concepts on pilot-scale equipment. However, the physical lab is resource-intensive, requiring protective equipment and constant supervision. In addition, due to limited units for large groups of students, students perform experiments according to the rolling program schedule, making alignment with lecture teaching and hands-on learning challenges. The research team focuses on increasing the accessibility of the UOL by using simulation gaming in standard, virtual reality and augmented reality modalities as an educational tool. The "Virtual Unit Ops Lab" application places students in an immersive simulated environment of the physical lab, where they can get practical experiences without the difficulties of an in-person lab by using specialized headsets and controllers, which allows the student to move and interact with various parts of the machine physically. Developed with Unity software, the application serves as a digital twin to an existing lab, which allows for an immersive simulation of the full-scale lab equipment, in addition to enhanced learning features such as the ability to display the current action performed by the user and to provide visual/audio feedback for correct and incorrect actions. The application also supports the use by multiple "players" (i.e., it has the "multiplayer" option), where multiple students can communicate and discuss their current step. As a work in progress, a non-player-character chatbot (generative AI responses) is being developed for existing applications using OpenAI's GPT-3.5, which provides designated information to a student in a conversational manner. Additionally, a supplemental "Augmented Unit Ops Lab" application uses Augmented Reality, which superimposes three-dimensional flow diagrams onto the Heat Exchanger through the view of a phone camera during the in-person labs. © 2024 Elsevier B.V., All rights reserved.},

keywords = {'current, Augmented Reality, Chemical engineering students, Concentration (process), Cooling towers, Degassing, Hands-on learning, Hydrodesulfurization, Immersive, Large groups, Liquid crystal displays, Multiusers, Nanoreactors, Personnel training, Pilot-scale equipment, Protective equipment, Students, Training Systems, Unit Operations Laboratory},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ayre_implementation_2023,

title = {IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE (AI) INSTRUCTIONAL SUPPORT SYSTEM IN A VIRTUAL REALITY (VR) THERMAL-FLUIDS LABORATORY},

author = {D. Ayre and C. Dougherty and Y. Zhao},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185393784&doi=10.1115%2fIMECE2023-112683&partnerID=40&md5=c2492592a016478a4b3591ff82a93be5},

doi = {10.1115/IMECE2023-112683},

isbn = {978-079188765-3 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {ASME Int Mech Eng Congress Expos Proc},

volume = {8},

publisher = {American Society of Mechanical Engineers (ASME)},

abstract = {Physical laboratory experiments have long been the cornerstone of higher education, providing future engineers practical real-life experience invaluable to their careers. However, demand for laboratory time has exceeded physical capabilities. Virtual reality (VR) labs have proven to retain many benefits of attending physical labs while also providing significant advantages only available in a VR environment. Previously, our group had developed a pilot VR lab that replicated six (6) unique thermal-fluids lab experiments developed using the Unity game engine. One of the VR labs was tested in a thermal-fluid mechanics laboratory class with favorable results, but students highlighted the need for additional assistance within the VR simulation. In response to this testing, we have incorporated an artificial intelligence (AI) assistant to aid students within the VR environment by developing an interaction model. Utilizing the Generative Pre-trained Transformer 4 (GPT-4) large language model (LLM) and augmented context retrieval, the AI assistant can provide reliable instruction and troubleshoot errors while students conduct the lab procedure to provide an experience similar to a real-life lab assistant. The updated VR lab was tested in two laboratory classes and while the overall tone of student response to an AI-powered assistant was excitement and enthusiasm, observations and other recorded data show that students are currently unsure of how to utilize this new technology, which will help guide future refinement of AI components within the VR environment. © 2023 by ASME.},

keywords = {Artificial intelligence, E-Learning, Education computing, Engineering education, Fluid mechanics, Generative AI, generative artificial intelligence, GPT, High educations, Instructional support, Laboratories, Laboratory class, Laboratory experiments, Physical laboratory, Professional aspects, Students, Support systems, Thermal fluids, Virtual Reality, Virtual-reality environment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gaikwad_smart_2023,

title = {Smart Training Framework and Assessment Strategies},

author = {T. Gaikwad and A. Kulkarni},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193969838&doi=10.1109%2FIEEECONF58110.2023.10520594&partnerID=40&md5=bb9990750ee4ea498a5f551c8bd382ce},

doi = {10.1109/IEEECONF58110.2023.10520594},

isbn = {9798350338522 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IEEE Eng. Informatics, EI},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The rapidly evolving landscape of technological advancements is significantly transforming the education sector. This integration of technology in the education sector has given rise to the edtech industry which is transforming as newer technologies are introduced. Training delivered to the learners, along with the assessment of the learners, are the fundamental components of the education sector. However, current methods of delivering training and assessing learners face numerous challenges, including skill shortage due to technology advancements, high costs, conducting complex training in high- risk environments. Similarly, assessment methods struggle with inflexible assessment strategies and limited personalized feedback to learners. Addressing these challenges in training and assessment, this study proposes a smart training and assessment framework (STAF) which leverages the benefits of augmented reality (AR) and artificial intelligence (AI) based large language models (LLMs) which stand out as a monumental leap in reshaping the training and assessment sector. As part of this study, an AR based training module was created and delivered to students. A survey was conducted of these students to gain insights about the adaptability of AR based trainings and potential to improve these trainings. It is concluded that along with AR in education, AI and LLMs with prompt engineering strategies should be integrated in the education domain for better interactivity and enhanced student performance. Currently, limited research is conducted on integration of LLMs in AR environments for the education sector and this paper provides an in-depth exploration of the immense potential of the applications of LLMs within the realm of training and assessment for improved learner performance. © 2024 Elsevier B.V., All rights reserved.},

keywords = {AR training, Assessment strategies, Augmented Reality, Augmented reality training, Computational Linguistics, Edtech, Education computing, Education sectors, Engineering education, Language Model, Large language model, large language models, Prompt engineering, Risk assessment, Smart assessment, Students, Training assessment, Training framework},

pubstate = {published},

tppubtype = {inproceedings}

}