Publications

@inproceedings{shi_caring-ai_2025,

title = {CARING-AI: Towards Authoring Context-aware Augmented Reality INstruction through Generative Artificial Intelligence},

author = {J. Shi and R. Jain and S. Chi and H. Doh and H. -G. Chi and A. J. Quinn and K. Ramani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005725461&doi=10.1145%2f3706598.3713348&partnerID=40&md5=e88afd8426e020155599ef3b2a044774},

doi = {10.1145/3706598.3713348},

isbn = {979-840071394-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {Context-aware AR instruction enables adaptive and in-situ learning experiences. However, hardware limitations and expertise requirements constrain the creation of such instructions. With recent developments in Generative Artificial Intelligence (Gen-AI), current research tries to tackle these constraints by deploying AI-generated content (AIGC) in AR applications. However, our preliminary study with six AR practitioners revealed that the current AIGC lacks contextual information to adapt to varying application scenarios and is therefore limited in authoring. To utilize the strong generative power of GenAI to ease the authoring of AR instruction while capturing the context, we developed CARING-AI, an AR system to author context-aware humanoid-avatar-based instructions with GenAI. By navigating in the environment, users naturally provide contextual information to generate humanoid-avatar animation as AR instructions that blend in the context spatially and temporally. We showcased three application scenarios of CARING-AI: Asynchronous Instructions, Remote Instructions, and Ad Hoc Instructions based on a design space of AIGC in AR Instructions. With two user studies (N=12), we assessed the system usability of CARING-AI and demonstrated the easiness and effectiveness of authoring with Gen-AI. © 2025 Copyright held by the owner/author(s).},

keywords = {'current, Application scenario, AR application, Augmented Reality, Context-Aware, Contextual information, Generative adversarial networks, generative artificial intelligence, Humanoid avatars, In-situ learning, Learning experiences, Power},

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=08816dd8d41bc34a0dc2d355985e2cc4},

doi = {10.1145/3706599.3719787},

isbn = {979-840071395-8 (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 Copyright held by the owner/author(s).},

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{noauthor_fostering_2024,

title = {Fostering Personalized Learning in Data Science: Integrating Innovative Tools and Strategies for Diverse Pathways},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001849041&doi=10.1109%2fICEED62316.2024.10923798&partnerID=40&md5=cfec507f601df5ffc3b07db0df6d80a7},

doi = {10.1109/ICEED62316.2024.10923798},

isbn = {979-835036741-6 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {IEEE Int. Conf. Eng. Educ.: Dissem. Adv. Eng. Educ. using Artif. Intell., ICEED},

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

abstract = {This paper introduces an innovative teaching approach in data science tailored for students in non-computer science pathways, specifically Business Information Technology (BIT) and Computing and Information Technology (CIT). Over a five-year period, a unique teaching approach has been developed incorporating a virtual reality (VR) game event and ChatGPT-4 as a generative artificial intelligence (AI) tool. To address the inherent complexities of learning data science, particularly the diverse prerequisite skills, this study introduces a framework including a diagnostic assessment centered around a specific education research question: 'How can the learning experiences of individual students be customized to address the multifaceted challenges of data science education?' Through a diagnostic assessment process, conducted via a survey completed by students, this framework identifies students' unique requirements and skill areas facilitating the delivery of personalized content recommendations within the initial week of teaching. By fostering a culture of self-directed learning, the approach aims to enable students to concentrate on essential customized learning materials. This paper also highlights the overall student satisfaction with the module averaged 4.5 out of 5 with a standard deviation of 0.9 indicating a high level of contentment with the teaching approach. The discussion encompasses the framework's implications for teaching and its alignment with educational theories. This paper contributes to the computing education field by addressing the research question and offering insights for future research and teaching practices. © 2024 IEEE.},

keywords = {Adversarial machine learning, ChatGPT-4, Content recommendation, Content recommendations, Contrastive Learning, Data Science, Data science education, Federated learning, Individualized learning, Individualized learning experience framework, Learning experiences, Prerequisite skill identification, Science education, Self-directed learning, Teaching approaches, Virtual environments, 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{arrigo_task-interaction_2024,

title = {A Task-Interaction Framework to Monitor Mobile Learning Activities Based on Artificial Intelligence and Augmented Reality},

author = {M. Arrigo and M. Farella and G. Fulantelli and D. Schicchi and D. Taibi},

editor = {De Paolis L.T. and Arpaia P. and Sacco M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204618733&doi=10.1007%2f978-3-031-71707-9_26&partnerID=40&md5=8969f18ab0f10dcddf37e54265d10518},

doi = {10.1007/978-3-031-71707-9_26},

isbn = {03029743 (ISSN); 978-303171706-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

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

volume = {15027 LNCS},

pages = {325–333},

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

abstract = {The complexity behind the analysis of mobile learning activities has requested the development of specifically designed frameworks. When students are involved in mobile learning experiences, they interact with the context in which the activities occur, the content they have access to, with peers and their teachers. The wider adoption of generative artificial intelligence introduces new interactions that researchers have to look at when learning analytics techniques are applied to monitor learning patterns. The task interaction framework proposed in this paper explores how AI-based tools affect student-content and student-context interactions during mobile learning activities, thus focusing on the interplay of Learning Analytics and Artificial Intelligence advances in the educational domain. A use case scenario that explores the framework’s application in a real educational context is also presented. Finally, we describe the architectural design of an environment that leverages the task interaction framework to analyze enhanced mobile learning experiences in which structured content extracted from a Knowledge Graph is elaborated by a large language model to provide students with personalized content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Activity-based, Adversarial machine learning, Analytic technique, Augmented Reality, Contrastive Learning, Federated learning, Generative AI, Interaction framework, Learning Activity, Learning analytic framework, Learning Analytics Framework, Learning experiences, Learning patterns, Mobile Learning, Teachers'},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gao_datalivr_2024,

title = {DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements},

author = {H. Gao and H. Huai and S. Yildiz-Degirmenci and M. Bannert and E. Kasneci},

editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213525613&doi=10.1109%2fISMAR62088.2024.00026&partnerID=40&md5=abdeba7ecfecc8b1d715d633a29bd11d},

doi = {10.1109/ISMAR62088.2024.00026},

isbn = {979-833151647-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR},

pages = {120–129},

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

abstract = {Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.},

keywords = {Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, 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}

}