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Artificial Intelligence Methods for Human-Computer Interaction
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Artificial Intelligence Methods for Human-Computer Interaction

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 20033

Special Issue Editors

Dr. Alessia Amelio

E-Mail Website
Guest Editor
1. Department InGeo, University "G. d'Annunzio" Chieti-Pescara, 65127 Pescara, Italy
2. Research Laboratory "Hugo Gernsback", Telematic University Leonardo da Vinci, 66010 Torrevecchia Teatina, Italy
Interests: artificial intelligence; pattern recognition; data analysis; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Arcangelo Merla

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared imaging; medical imaging; neuroimaging; psychophysiology; human–machine interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Cardone

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: artificial intelligence methods; robotics and affective computing; human–machine interaction; processing methods and analysis of biomedical images and physiological signals; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. David Perpetuini

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared thermography; functional infrared spectroscopy (fNIRS); electroencephalography (EEG); photoplethysmography (PPG); wearable sensors; affective computing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human–computer interaction (HCI) is a field of research that focuses on the creation, assessment, and application of interactive computer systems for human use, as well as the investigation of significant phenomena related to these areas. Since HCI is mostly related to communication between humans and machines, its study is inherently multidisciplinary. Thus, researchers from all backgrounds can contribute to the success of this field. Specifically, investigation HCI involves operating systems, programming languages, development environments, and computer graphics on the machine side. Relevant fields in the humanities include communication theory, linguistics, graphic and industrial design, social sciences, cognitive and social psychology, and any disciplines capable of integrating human factors like computer user satisfaction.

In this context, the recent progress of digital transition and disruptive technologies has been mostly focused on the introduction of innovative intelligent systems, capable of interacting with humans, in order to accomplish specific relevant needs. In particular, generative AI systems, emotion recognition techniques, human–robot approaches, AI methods for personalized healthcare and telemedicine, and intelligent education systems have flourished in recent times, posing new challenges in deep learning algorithms development, affective digital communication, digital security, privacy of personal data, ethics of information and green and sustainable policies of using AI methods for HCI.

Therefore, this Special Issue aims to collect high-quality original research and survey papers from academics and industrial researchers in the fields of artificial intelligence, big data, knowledge management, cybersecurity, affective computing, cognitive science, sustainable technological development and digital transition. We are particularly concerned with research on the development of machine learning and deep learning models for HCI, ethics of information in intelligent HCI, intelligent human–machine interfaces, as well as user-centered evaluation of intelligent HCI systems and approaches of intelligent HCI for healthcare and education. Contributors should use the most advanced methods and applications.

 Topics of interest include, but are not limited to, the following:

  • Generative AI systems;
  • Security and privacy for intelligent HCI systems;
  • Intelligent HCI for personalized medicine and telemedicine;
  • Affective communication methods;
  • Human–robot approaches;
  • Ethics of information in intelligent HCI;
  • User satisfaction metrics for intelligent HCI;
  • Green methodologies for generative AI;
  • Communication systems and protocols for intelligent HCI;
  • Interaction design and user-centered design in intelligent HCI;
  • Intelligent education systems;
  • Prosocial agents’ development;
  • Human factors in HCI.

Dr. Alessia Amelio
Prof. Dr. Arcangelo Merla
Dr. Daniela Cardone
Dr. David Perpetuini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • affective communication
  • human–robot approaches
  • generative artificial intelligence
  • deep learning
  • human-computer interfaces
  • cognitive science
  • telemedicine

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

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Research

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23 pages, 1121 KiB  
Article
Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations
by Adrián Sánchez-Mompó, Ioannis Mavromatis, Peizheng Li, Konstantinos Katsaros and Aftab Khan
Information 2025, 16(4), 281; https://doi.org/10.3390/info16040281 - 30 Mar 2025
Viewed by 324
Abstract
This study presents an empirical investigation into the energy consumption of discriminative and generative AI models within real-world MLOps pipelines. For discriminative models, we examine various architectures and hyperparameters during training and inference and identify energy-efficient practices. For generative AI, large language models [...] Read more.
This study presents an empirical investigation into the energy consumption of discriminative and generative AI models within real-world MLOps pipelines. For discriminative models, we examine various architectures and hyperparameters during training and inference and identify energy-efficient practices. For generative AI, large language models (LLMs) are assessed, with a focus primarily on energy consumption across different model sizes and varying service requests. Our study employs software-based power measurements, ensuring ease of replication across diverse configurations, models, and datasets. We analyse multiple models and hardware setups to uncover correlations among various metrics, identifying key contributors to energy consumption. The results indicate that, for discriminative models, optimising architectures, hyperparameters, and hardware can significantly reduce energy consumption without sacrificing performance. For LLMs, energy efficiency depends on balancing model size, reasoning complexity, and request-handling capacity, as larger models do not necessarily consume more energy when utilisation remains low. This analysis provides practical guidelines for designing green and sustainable ML operations, emphasising energy consumption and carbon-footprint reductions while maintaining performance. This paper can serve as a benchmark for accurately estimating total energy use across different types of AI models. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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15 pages, 1989 KiB  
Article
Exploring the Potential of BCI in Education: An Experiment in Musical Training
by Raffaella Folgieri, Claudio Lucchiari, Sergej Gričar, Tea Baldigara and Marisa Gil
Information 2025, 16(4), 261; https://doi.org/10.3390/info16040261 - 23 Mar 2025
Viewed by 510
Abstract
Brain–computer interfaces (BCIs) have gained significant attention in recent years for various applications, including education and skill development: studies have shown that BCIs can boost memory, concentration, and even creativity and can improve learning and memory retention in healthy people. In our current [...] Read more.
Brain–computer interfaces (BCIs) have gained significant attention in recent years for various applications, including education and skill development: studies have shown that BCIs can boost memory, concentration, and even creativity and can improve learning and memory retention in healthy people. In our current study, we investigated the effectiveness of real-time feedback provided by a BCI system for improving performance on a specific task. A total of 20 participants completed a pre-training assessment, followed by a training period with the BCI system and a post-training assessment. The BCI system provided real-time feedback based on the participants’ level of accuracy, with positive feedback given for scores above 70%. Results showed a significant improvement in accuracy scores from pre- to post-training, with an average improvement of 15%. Participants also reported high levels of satisfaction with the feedback provided by the BCI system. These findings suggest that real-time feedback provided by a BCI system can be an effective tool for skill development and education, particularly when tailored to the specific needs of individual learners. Further research is needed to explore the potential of BCIs for a wide range of educational applications. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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28 pages, 5792 KiB  
Article
Enabling Perspective-Aware Ai with Contextual Scene Graph Generation
by Daniel Platnick, Marjan Alirezaie and Hossein Rahnama
Information 2024, 15(12), 766; https://doi.org/10.3390/info15120766 - 2 Dec 2024
Viewed by 1674
Abstract
This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and [...] Read more.
This paper advances contextual image understanding within perspective-aware Ai (PAi), an emerging paradigm in human–computer interaction that enables users to perceive and interact through each other’s perspectives. While PAi relies on multimodal data—such as text, audio, and images—challenges in data collection, alignment, and privacy have led us to focus on enabling the contextual understanding of images. To achieve this, we developed perspective-aware scene graph generation with LLM post-processing (PASGG-LM). This framework extends traditional scene graph generation (SGG) by incorporating large language models (LLMs) to enhance contextual understanding. PASGG-LM integrates classical scene graph outputs with LLM post-processing to infer richer contextual information, such as emotions, activities, and social contexts. To test PASGG-LM, we introduce the context-aware scene graph generation task, where the goal is to generate a context-aware situation graph describing the input image. We evaluated PASGG-LM pipelines using state-of-the-art SGG models, including Motifs, Motifs-TDE, and RelTR, and showed that fine-tuning LLMs, particularly GPT-4o-mini and Llama-3.1-8B, improves performance in terms of R@K, mR@K, and mAP. Our method is capable of generating scene graphs that capture complex contextual aspects, advancing human–machine interaction by enhancing the representation of diverse perspectives. Future directions include refining contextual scene graph models and expanding multi-modal data integration for PAi applications in domains such as healthcare, education, and social robotics. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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19 pages, 2108 KiB  
Article
Innovative Transitions: Exploring Demand for Smart City Development in Novi Sad as a European Capital of Culture
by Minja Bolesnikov, Mario Silić, Dario Silić, Boris Dumnić, Jelena Ćulibrk, Maja Petrović and Tamara Gajić
Information 2024, 15(11), 730; https://doi.org/10.3390/info15110730 - 15 Nov 2024
Cited by 1 | Viewed by 1193
Abstract
This study investigates the factors influencing the acceptance and implementation of smart city solutions, with a particular focus on smart mobility and digital services in Novi Sad, one of the leading urban centers in Serbia. Employing a quantitative methodology, the research encompasses citizens’ [...] Read more.
This study investigates the factors influencing the acceptance and implementation of smart city solutions, with a particular focus on smart mobility and digital services in Novi Sad, one of the leading urban centers in Serbia. Employing a quantitative methodology, the research encompasses citizens’ perceptions of the benefits of smart technologies, their level of awareness regarding smart solutions, the degree of engagement in using digital services, and their interest in smart mobility. The results indicate that these factors are crucial for the successful integration of smart technologies. Notably, awareness of smart city initiatives and the perceived benefits, such as improved mobility, reduced traffic congestion, increased energy efficiency, and enhanced quality of life, are highlighted as key prerequisites for the adoption of these solutions. Novi Sad, as the European Capital of Culture in 2022, presents a unique opportunity for the implementation of these technologies. Our findings point to the need for strategic campaigns aimed at educating and raising public awareness. The practical implications of this study could contribute to shaping policies that encourage the development of smart cities, not only in Novi Sad but also in other urban areas across Serbia and the region. This study confirms the importance of citizen engagement and technological literacy in the transformation of urban environments through smart solutions, underscoring the potential of these technologies to improve everyday life and achieve sustainable urban development. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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19 pages, 851 KiB  
Article
The Ethics and Cybersecurity of Artificial Intelligence and Robotics in Helping The Elderly to Manage at Home
by Jyri Rajamäki and Jaakko Helin
Information 2024, 15(11), 729; https://doi.org/10.3390/info15110729 - 15 Nov 2024
Cited by 1 | Viewed by 2198
Abstract
The aging population, combined with the scarcity of healthcare resources, presents significant challenges for our society. The use of artificial intelligence (AI) and robotics offers a potential solution to these challenges. However, such technologies also raise ethical and cybersecurity concerns related to the [...] Read more.
The aging population, combined with the scarcity of healthcare resources, presents significant challenges for our society. The use of artificial intelligence (AI) and robotics offers a potential solution to these challenges. However, such technologies also raise ethical and cybersecurity concerns related to the preservation of privacy, autonomy, and human contact. In this case study, we examine these ethical challenges and the opportunities brought by AI and robotics in the care of old individuals at home. This article aims to describe the current fragmented state of legislation related to the development and use of AI-based services and robotics and to reflect on their ethics and cybersecurity. The findings indicate that, guided by ethical principles, we can leverage the best aspects of technology while ensuring that old people can maintain a dignified and valued life at home. The careful handling of ethical issues should be viewed as a competitive advantage and opportunity, rather than a burden. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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13 pages, 185 KiB  
Article
Deep Learning and Knowledge
by Donald Gillies
Information 2024, 15(11), 720; https://doi.org/10.3390/info15110720 - 11 Nov 2024
Viewed by 818
Abstract
This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, [...] Read more.
This paper considers the question of what kind of knowledge is produced by deep learning. Ryle’s concept of knowledge how is examined and is contrasted with knowledge with a rationale. It is then argued that deep neural networks do produce knowledge how, but, because of their opacity, they do not in general, though there may be some special cases to the contrary, produce knowledge with a rationale. It is concluded that the distinction between knowledge how and knowledge with a rationale is a useful one for judging whether a particular application of deep learning AI is appropriate. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
22 pages, 3063 KiB  
Article
AI Impact on Hotel Guest Satisfaction via Tailor-Made Services: A Case Study of Serbia and Hungary
by Ranko Makivić, Dragan Vukolić, Sonja Veljović, Minja Bolesnikov, Lóránt Dénes Dávid, Andrea Ivanišević, Mario Silić and Tamara Gajić
Information 2024, 15(11), 700; https://doi.org/10.3390/info15110700 - 4 Nov 2024
Viewed by 6818
Abstract
This study examines the level of implementation of artificial intelligence (AI) in the personalization of hotel services and its impact on guest satisfaction through an analysis of tourists’ attitudes and behaviors The focus of the research is on how personalized recommendations for food [...] Read more.
This study examines the level of implementation of artificial intelligence (AI) in the personalization of hotel services and its impact on guest satisfaction through an analysis of tourists’ attitudes and behaviors The focus of the research is on how personalized recommendations for food and beverages, activities, and room services, delivered by trustworthy AI systems, digital experience, and the perception of privacy and data security, influence overall guest satisfaction. The research was conducted in Serbia and Hungary, using structural models to assess and analyze direct and indirect effects. The results show that AI personalization significantly contributes to guest satisfaction, with mediating variables such as trust in AI systems and technological experience playing a key role. A comparative analysis highlights differences between Hungary, a member of the European Union, and Serbia, a country in transition, shedding light on specific regulatory frameworks and cultural preferences in these countries. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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21 pages, 1323 KiB  
Article
Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden Ring
by Fatemeh Dehghani and Loutfouz Zaman
Information 2024, 15(9), 573; https://doi.org/10.3390/info15090573 - 18 Sep 2024
Viewed by 2695
Abstract
Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers [...] Read more.
Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models’ outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful topics. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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Review

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17 pages, 267 KiB  
Review
Balancing Technology, Ethics, and Society: A Review of Artificial Intelligence in Embryo Selection
by Roberto Aufieri and Francesco Mastrocola
Information 2025, 16(1), 18; https://doi.org/10.3390/info16010018 - 2 Jan 2025
Viewed by 1788
Abstract
The introduction of artificial intelligence (AI) in embryo selection during in vitro fertilization presents distinct ethical and societal challenges compared to the general implementation of AI in healthcare. This narrative review examines ethical perspectives and potential societal implications of implementing AI-driven embryo selection. [...] Read more.
The introduction of artificial intelligence (AI) in embryo selection during in vitro fertilization presents distinct ethical and societal challenges compared to the general implementation of AI in healthcare. This narrative review examines ethical perspectives and potential societal implications of implementing AI-driven embryo selection. The literature reveals that some authors perceive AI as an extension of a technocratic paradigm that commodifies embryos, considering that any embryo selection methods undermine the dignity of human life. Others, instead, contend that prioritizing embryos with the highest viability is morally permissible while cautioning against discarding embryos based solely on unproven AI assessments. The reviewed literature identified further potential ethical concerns associated with this technique, including possible bias in the selection criteria, lack of transparency in black-box algorithms, risks of “machine paternalism” replacing human judgment, privacy issues with sensitive fertility data, equity of access, and challenges in maintaining human-centered care. These findings, along with the results of the only randomized controlled trial available, suggest that the introduction of AI-driven embryo selection in clinical practice is not currently scientifically and ethically justified. Implementing and deploying ethical and responsible AI in embryo selection would be feasible only if the ethical and societal concerns raised are adequately addressed. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)
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