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 expand the Abstract, Links and BibTex record for each paper.
2025
B, C. E. Pardo; R, O. I. Iglesias; A, M. D. León; M., C. G. Quintero
EverydAI: Virtual Assistant for Decision-Making in Daily Contexts, Powered by Artificial Intelligence Journal Article
In: Systems, vol. 13, no. 9, 2025, ISSN: 20798954 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Behavioral Research, Decision making, Decisions makings, Digital avatar, Digital avatars, Information overloads, Informed decision, Interactive computer graphics, Language Model, Large language model, large language models, Natural language processing systems, Natural languages, Object Detection, Object recognition, Objects detection, recommendation systems, Recommender systems, Three dimensional computer graphics, Virtual assistants, Virtual Reality, web scraping, Web scrapings
@article{pardo_b_everydai_2025,
title = {EverydAI: Virtual Assistant for Decision-Making in Daily Contexts, Powered by Artificial Intelligence},
author = {C. E. Pardo B and O. I. Iglesias R and M. D. León A and C. G. Quintero M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017115803&doi=10.3390%2Fsystems13090753&partnerID=40&md5=475327fffcdc43ee3466b4a65111866a},
doi = {10.3390/systems13090753},
issn = {20798954 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Systems},
volume = {13},
number = {9},
abstract = {In an era of information overload, artificial intelligence plays a pivotal role in supporting everyday decision-making. This paper introduces EverydAI, a virtual AI-powered assistant designed to help users make informed decisions across various daily domains such as cooking, fashion, and fitness. By integrating advanced natural language processing, object detection, augmented reality, contextual understanding, digital 3D avatar models, web scraping, and image generation, EverydAI delivers personalized recommendations and insights tailored to individual needs. The proposed framework addresses challenges related to decision fatigue and information overload by combining real-time object detection and web scraping to enhance the relevance and reliability of its suggestions. EverydAI is evaluated through a two-phase survey, each one involving 30 participants with diverse demographic backgrounds. Results indicate that on average, 92.7% of users agreed or strongly agreed with statements reflecting the system’s usefulness, ease of use, and overall performance, indicating a high level of acceptance and perceived effectiveness. Additionally, EverydAI received an average user satisfaction score of 4.53 out of 5, underscoring its effectiveness in supporting users’ daily routines. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {Artificial intelligence, Augmented Reality, Behavioral Research, Decision making, Decisions makings, Digital avatar, Digital avatars, Information overloads, Informed decision, Interactive computer graphics, Language Model, Large language model, large language models, Natural language processing systems, Natural languages, Object Detection, Object recognition, Objects detection, recommendation systems, Recommender systems, Three dimensional computer graphics, Virtual assistants, Virtual Reality, web scraping, Web scrapings},
pubstate = {published},
tppubtype = {article}
}
In an era of information overload, artificial intelligence plays a pivotal role in supporting everyday decision-making. This paper introduces EverydAI, a virtual AI-powered assistant designed to help users make informed decisions across various daily domains such as cooking, fashion, and fitness. By integrating advanced natural language processing, object detection, augmented reality, contextual understanding, digital 3D avatar models, web scraping, and image generation, EverydAI delivers personalized recommendations and insights tailored to individual needs. The proposed framework addresses challenges related to decision fatigue and information overload by combining real-time object detection and web scraping to enhance the relevance and reliability of its suggestions. EverydAI is evaluated through a two-phase survey, each one involving 30 participants with diverse demographic backgrounds. Results indicate that on average, 92.7% of users agreed or strongly agreed with statements reflecting the system’s usefulness, ease of use, and overall performance, indicating a high level of acceptance and perceived effectiveness. Additionally, EverydAI received an average user satisfaction score of 4.53 out of 5, underscoring its effectiveness in supporting users’ daily routines. © 2025 Elsevier B.V., All rights reserved.
2024
Do, M. D.; Dahlem, N.; Paulus, M.; Krick, M.; Steffny, L.; Werth, D.
“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture Proceedings Article
In: J., Wei; G., Margetis (Ed.): Lect. Notes Comput. Sci., pp. 196–210, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303160457-7 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design
@inproceedings{do_furnish_2024,
title = {“Furnish Your Reality” - Intelligent Mobile AR Application for Personalized Furniture},
author = {M. D. Do and N. Dahlem and M. Paulus and M. Krick and L. Steffny and D. Werth},
editor = {Wei J. and Margetis G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196202642&doi=10.1007%2f978-3-031-60458-4_14&partnerID=40&md5=017510be06c286789867235cfd98bb36},
doi = {10.1007/978-3-031-60458-4_14},
isbn = {03029743 (ISSN); 978-303160457-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {14737 LNCS},
pages = {196–210},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial intelligence, Augmented Reality, Augmented reality applications, Electronic commerce, Generative AI, generative artificial intelligence, Human computer interaction, Human computer interfaces, LiDAR, Mobile augmented reality, Mobile human computer interface, Mobile Human Computer Interfaces, Personalized product design, Personalized products, Phygital customer journey, Physical environments, Product design, Recommender system, Recommender systems, Sales, User centered design, User interfaces, User-centered design},
pubstate = {published},
tppubtype = {inproceedings}
}
Today’s online retailers are faced with the challenge of providing a convenient solution for their customers to browse through a wide range of products. Simultaneously, they must meet individual customer needs by creating unique, personalized, one-of-a-kind items. Technological advances in areas such as Augmented Reality (AR), Artificial Intelligence (AI) or sensors (e.g. LiDAR), have the potential to address these challenges by enhancing the customer experience in new ways. One option is to implement “phygital” commerce solutions, which combines the benefits of physical and digital environments to improve the customer journey. This work presents a concept for a mobile AR application that integrates LiDAR and an AI-powered recommender system to create a unique phygital customer journey in the context of furniture shopping. The combination of AR, LiDAR and AI enables an accurate immersive experience along with personalized product designs. This concept aims to deliver benefits in terms of usability, convenience, time savings and user experience, while bridging the gap between mass-produced and personalized products. The new possibilities for merging virtual with physical environments hold immense potential, but this work also highlights challenges for customers as well as for online platform providers and future researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.