Publications

@inproceedings{dong_enhancing_2025,

title = {Enhancing Painting Exhibition Experiences with the Application of Augmented Reality-Based AI Video Generation Technology},

author = {Y. Dong},

editor = {Zaphiris P. and Ioannou A. and Ioannou A. and Sottilare R.A. and Schwarz J. and Rauterberg M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213302959&doi=10.1007%2f978-3-031-76815-6_18&partnerID=40&md5=35484f5ed199a831f1a30f265a0d32d5},

doi = {10.1007/978-3-031-76815-6_18},

isbn = {03029743 (ISSN); 978-303176814-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

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

volume = {15378 LNCS},

pages = {256–262},

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

abstract = {Traditional painting exhibitions often rely on flat presentation methods, such as walls and stands, limiting their impact. Augmented Reality (AR) technology presents an opportunity to transform these experiences by turning static, flat artwork into dynamic, multi-dimensional presentations. However, creating and integrating video or dynamic content can be time-consuming and challenging, requiring meticulous planning, design, and production. In the context of urban renewal and community revitalization, particularly in China’s first-tier cities where real estate development has saturated the market, there is a growing trend to repurpose traditional commercial and office spaces with cultural and artistic exhibitions. These exhibitions not only enhance the spatial quality but also elevate the user experience, making the spaces more competitive. However, these non-traditional exhibition venues often lack the amenities of professional galleries, relying on walls, windows, and corners for displays, and requiring quick setup times. For visitors, who are often office workers or shoppers with limited time, the use of personal mobile devices for interaction is common. WeChat, China’s most widely used mobile application, provides a platform for convenient digital interactive experiences through mini-programs, which can support lightweight AR applications. AI video generation technologies, such as Conditional Generative Adversarial Networks (ControlNet) and Latent Consistency Models (LCM), have seen significant advancements. These technologies now allow for the creation of 3D models and video content from text and images. Tools like Meshy and Pika provide the ability to generate various video styles and offer precise control over video content. New AI video applications like Stable Video further expand the possibilities by rapidly converting static images into dynamic videos, facilitating easy adjustments and edits. This paper explores the application of AR-based AI video generation technology in enhancing the experience of painting exhibitions. By integrating these technologies, traditional paintings can be transformed into interactive, engaging displays that enrich the viewer’s experience. The study demonstrates the potential of these innovations to make art exhibitions more appealing and competitive in various public spaces, thereby improving both artistic expression and audience engagement. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {3D modeling, AI-generated art, Art and Technology, Arts computing, Augmented Reality, Augmented reality technology, Digital Exhibition Design, Dynamic content, E-Learning, Education computing, Generation technologies, Interactive computer graphics, Knowledge Management, Multi dimensional, Planning designs, Three dimensional computer graphics, Video contents, Video generation},

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{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=44ec79c8f005f4551c820c61f5b5d435},

doi = {10.1145/3626252.3630789},

isbn = {979-840070423-9 (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 ACM.},

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{sarshartehrani_enhancing_2024,

title = {Enhancing E-Learning Experience Through Embodied AI Tutors in Immersive Virtual Environments: A Multifaceted Approach for Personalized Educational Adaptation},

author = {F. Sarshartehrani and E. Mohammadrezaei and M. Behravan and D. Gracanin},

editor = {Sottilare R.A. and Schwarz J.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196174389&doi=10.1007%2f978-3-031-60609-0_20&partnerID=40&md5=3801d0959781b1a191a3eb14f47bd8d8},

doi = {10.1007/978-3-031-60609-0_20},

isbn = {03029743 (ISSN); 978-303160608-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

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

volume = {14727 LNCS},

pages = {272–287},

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

abstract = {As digital education transcends traditional boundaries, e-learning experiences are increasingly shaped by cutting-edge technologies like artificial intelligence (AI), virtual reality (VR), and adaptive learning systems. This study examines the integration of AI-driven personalized instruction within immersive VR environments, targeting enhanced learner engagement-a core metric in online education effectiveness. Employing a user-centric design, the research utilizes embodied AI tutors, calibrated to individual learners’ emotional intelligence and cognitive states, within a Python programming curriculum-a key area in computer science education. The methodology relies on intelligent tutoring systems and personalized learning pathways, catering to a diverse participant pool from Virginia Tech. Our data-driven approach, underpinned by the principles of educational psychology and computational pedagogy, indicates that AI-enhanced virtual learning environments significantly elevate user engagement and proficiency in programming education. Although the scope is limited to a single academic institution, the promising results advocate for the scalability of such AI-powered educational tools, with potential implications for distance learning, MOOCs, and lifelong learning platforms. This research contributes to the evolving narrative of smart education and the role of large language models (LLMs) in crafting bespoke educational experiences, suggesting a paradigm shift towards more interactive, personalized e-learning solutions that align with global educational technology trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Adaptive Learning, Artificial intelligence, Artificial intelligence in education, Computer aided instruction, Computer programming, E - learning, E-Learning, Education computing, Embodied artificial intelligence, Engineering education, Immersive Virtual Environments, Learner Engagement, Learning experiences, Learning systems, Multi-faceted approach, Personalized Instruction, Traditional boundaries, Virtual Reality},

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{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=c23eba992e455b09829dd03d25fe567e},

doi = {10.1109/IEEECONF58110.2023.10520594},

isbn = {979-835033852-2 (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. © 2023 IEEE.},

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}

}

@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}

}