AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Bendarkawi, J.; Ponce, A.; Mata, S. C.; Aliu, A.; Liu, Y.; Zhang, L.; Liaqat, A.; Rao, V. N.; Monroy-Hernández, A.
ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 979-840071395-8 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Augmented Reality (AR), Embodied agent, Embodied Agents, Language learning, Language Model, Large language model, large language models (LLMs), Population dynamics, Second language, Second Language Acquisition, Second language learners, Social dynamics, Turn-taking
@inproceedings{bendarkawi_conversar_2025,
title = {ConversAR: Exploring Embodied LLM-Powered Group Conversations in Augmented Reality for Second Language Learners},
author = {J. Bendarkawi and A. Ponce and S. C. Mata and A. Aliu and Y. Liu and L. Zhang and A. Liaqat and V. N. Rao and A. Monroy-Hernández},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005746128&doi=10.1145%2f3706599.3720162&partnerID=40&md5=8330d3e0cb735caffa828b848ab9a110},
doi = {10.1145/3706599.3720162},
isbn = {979-840071395-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. © 2025 Copyright held by the owner/author(s).},
keywords = {Augmented Reality, Augmented Reality (AR), Embodied agent, Embodied Agents, Language learning, Language Model, Large language model, large language models (LLMs), Population dynamics, Second language, Second Language Acquisition, Second language learners, Social dynamics, Turn-taking},
pubstate = {published},
tppubtype = {inproceedings}
}
Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise complex turn-taking skills, and experience group social dynamics in a target language. However, most existing Augmented Reality (AR)-based conversational learning tools focus on dyadic interactions rather than group dialogues. Although research has shown that AR can help reduce speaking anxiety and create a comfortable space for practicing speaking skills in dyadic scenarios, especially with Large Language Model (LLM)-based conversational agents, the potential for group language practice using these technologies remains largely unexplored. We introduce ConversAR, a gpt-4o powered AR application, that enables L2 learners to practice contextualized group conversations. Our system features two embodied LLM agents with vision-based scene understanding and live captions. In a system evaluation with 10 participants, users reported reduced speaking anxiety and increased learner autonomy compared to perceptions of in-person practice methods with other learners. © 2025 Copyright held by the owner/author(s).
2024
Gujar, P.; Paliwal, G.; Panyam, S.
Generative AI and the Future of Interactive and Immersive Advertising Proceedings Article
In: D., Rivas-Lalaleo; S.L.S., Maita (Ed.): ETCM - Ecuador Tech. Chapters Meet., Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835039158-9 (ISBN).
Abstract | Links | BibTeX | Tags: Ad Creation, Adversarial machine learning, Advertising Technology (AdTech), Advertizing, Advertizing technology, Augmented Reality, Augmented Reality (AR), Generative adversarial networks, Generative AI, Immersive, Immersive Advertising, Immersive advertizing, Interactive Advertising, Interactive advertizing, machine learning, Machine-learning, Marketing, Mixed reality, Mixed Reality (MR), Personalization, Personalizations, User Engagement, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{gujar_generative_2024,
title = {Generative AI and the Future of Interactive and Immersive Advertising},
author = {P. Gujar and G. Paliwal and S. Panyam},
editor = {Rivas-Lalaleo D. and Maita S.L.S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211805262&doi=10.1109%2fETCM63562.2024.10746166&partnerID=40&md5=179c5ceeb28ed72e809748322535c7ad},
doi = {10.1109/ETCM63562.2024.10746166},
isbn = {979-835039158-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ETCM - Ecuador Tech. Chapters Meet.},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative AI is revolutionizing interactive and immersive advertising by enabling more personalized, engaging experiences through advanced technologies like VR, AR, and MR. This transformation is reshaping how advertisers create, deliver, and optimize content, allowing for two-way communication and blurring lines between digital and physical worlds. AI enhances user engagement through predictive analytics, real-time adaptation, and natural language processing, while also optimizing ad placement and personalization. Future trends include integration with emerging technologies like 5G and IoT, fully immersive experiences, and hyper-personalization. However, challenges such as privacy concerns, transparency issues, and ethical considerations must be addressed. As AI continues to evolve, it promises to create unprecedented opportunities for brands to connect with audiences in meaningful ways, potentially blurring the line between advertising and interactive entertainment. The industry must proactively address these challenges to ensure AI-driven advertising enhances user experiences while respecting privacy and maintaining trust. © 2024 IEEE.},
keywords = {Ad Creation, Adversarial machine learning, Advertising Technology (AdTech), Advertizing, Advertizing technology, Augmented Reality, Augmented Reality (AR), Generative adversarial networks, Generative AI, Immersive, Immersive Advertising, Immersive advertizing, Interactive Advertising, Interactive advertizing, machine learning, Machine-learning, Marketing, Mixed reality, Mixed Reality (MR), Personalization, Personalizations, User Engagement, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Generative AI is revolutionizing interactive and immersive advertising by enabling more personalized, engaging experiences through advanced technologies like VR, AR, and MR. This transformation is reshaping how advertisers create, deliver, and optimize content, allowing for two-way communication and blurring lines between digital and physical worlds. AI enhances user engagement through predictive analytics, real-time adaptation, and natural language processing, while also optimizing ad placement and personalization. Future trends include integration with emerging technologies like 5G and IoT, fully immersive experiences, and hyper-personalization. However, challenges such as privacy concerns, transparency issues, and ethical considerations must be addressed. As AI continues to evolve, it promises to create unprecedented opportunities for brands to connect with audiences in meaningful ways, potentially blurring the line between advertising and interactive entertainment. The industry must proactively address these challenges to ensure AI-driven advertising enhances user experiences while respecting privacy and maintaining trust. © 2024 IEEE.
Xu, F.; Nguyen, T.; Du, J.
Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-To-Action Journal Article
In: Journal of Construction Engineering and Management, vol. 150, no. 4, 2024, ISSN: 07339364 (ISSN).
Abstract | Links | BibTeX | Tags: Artificial intelligence systems, Augmented Reality, Augmented Reality (AR), ChatGPT, Complex sequences, Computational Linguistics, Diverse fields, Human like, Language Model, Maintenance, Maintenance tasks, Operations and maintenance, Optical character recognition, Sensor technologies, Virtual Reality
@article{xu_augmented_2024,
title = {Augmented Reality for Maintenance Tasks with ChatGPT for Automated Text-To-Action},
author = {F. Xu and T. Nguyen and J. Du},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183669638&doi=10.1061%2fJCEMD4.COENG-14142&partnerID=40&md5=6b02d2f4f6e74a8152adf2eb30ee2d88},
doi = {10.1061/JCEMD4.COENG-14142},
issn = {07339364 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of Construction Engineering and Management},
volume = {150},
number = {4},
abstract = {Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and increasingly are being employed in diverse fields. One such promising application is in operations and maintenance (OM). OM tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or in high-stress situations. By combining the advantages of superimposing virtual objects onto the physical world and generating human-like text using GPT, we can revolutionize OM operations. This study introduces a system that combines AR, optical character recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in OM tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=30) was conducted to illustrate the findings and answer the research questions. The Multidimensional Measurement of Trust (MDMT) was applied to understand the complexity of trust engagement with such a human-like system. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggest a reduction in cognitive load when executing the same operations using the AR and AI system. A divergence of trust was observed concerning capability and ethical dimensions. © 2024 American Society of Civil Engineers.},
keywords = {Artificial intelligence systems, Augmented Reality, Augmented Reality (AR), ChatGPT, Complex sequences, Computational Linguistics, Diverse fields, Human like, Language Model, Maintenance, Maintenance tasks, Operations and maintenance, Optical character recognition, Sensor technologies, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Advancements in sensor technology, artificial intelligence (AI), and augmented reality (AR) have unlocked opportunities across various domains. AR and large language models like GPT have witnessed substantial progress and increasingly are being employed in diverse fields. One such promising application is in operations and maintenance (OM). OM tasks often involve complex procedures and sequences that can be challenging to memorize and execute correctly, particularly for novices or in high-stress situations. By combining the advantages of superimposing virtual objects onto the physical world and generating human-like text using GPT, we can revolutionize OM operations. This study introduces a system that combines AR, optical character recognition (OCR), and the GPT language model to optimize user performance while offering trustworthy interactions and alleviating workload in OM tasks. This system provides an interactive virtual environment controlled by the Unity game engine, facilitating a seamless interaction between virtual and physical realities. A case study (N=30) was conducted to illustrate the findings and answer the research questions. The Multidimensional Measurement of Trust (MDMT) was applied to understand the complexity of trust engagement with such a human-like system. The results indicate that users can complete similarly challenging tasks in less time using our proposed AR and AI system. Moreover, the collected data also suggest a reduction in cognitive load when executing the same operations using the AR and AI system. A divergence of trust was observed concerning capability and ethical dimensions. © 2024 American Society of Civil Engineers.