Editorial: Human-Centered Ar ficial Intelligence in Interac on Processes

Maria Chiara Caschera ^1* Patrizia Grifoni ¹ Francesca Cordella ²

• ¹ National Research Council (CNR), Roma, Italy
• ² Research Unit of Advanced Robotics and Human-centred Technologies, Campus Bio-Medico University, Rome, Lazio, Italy

This is achieved through methods and algorithms that learn from human input and collabora on, con nuously improving by understanding human behavior, emo ons, and language. The term Human-Machine Interac on (HMI) encompasses both Human-Computer Interac on (HCI) and Human-Robot Interac on (HRI). The goal is to create effec ve, efficient, safe, reliable, natural, and intui ve interac on between humans and machines. To achieve a human-centered interac on process, several key aspects must be taken into account. Firstly, the system should be equipped with mul modal interfaces that can be easily adapted to specific contexts, user needs, and preferences, making HMI as natural and intui ve as possible. Secondly, AI should have the capability to operate in dynamic, non-determinis c, and par ally unknown environments. Thirdly, it is essen al to foster mutual understanding and learning to ensure transparent and explainable interac ons. Lastly, systems should be designed to recognize and respond to human gestures, speech, and other non-verbal cues in a way that feels familiar and comfortable to humans.Having a comprehensive understanding of the design and development processes of current AI solu ons is essen al for shaping future AI-based systems. The introduc on of human-centered AI aims to bridge the gap between machines and humans, making AI genuinely useful in enhancing human capabili es. This approach priori zes users by considering their needs, contexts, and expecta ons while adap ng to changes in their interac on behaviors. Human-centered AI is complex and encompasses various factors such as social and cultural behaviors, users' abili es, preferences, and limita ons. As a result, it requires the involvement of users in the design process, learning from them, and collabora ng to create an accessible, effec ve, and sustainable interac on paradigm.The goal has been to provide an overview of AI techniques applied to human-machine interac on (HCI and HRI), with a par cular focus on a human-centric approach. This research topic has explored the latest challenges in AI-driven systems that interact with humans. It aims to iden fy current advancements in the field to achieve more effec ve and reliable human-machine interac on.A total of eight contribu ons were selected for publica on within this research topic.The ar cles in this research topic underline the importance of integra ng AI thinking into workflows and adop ng a human-centric approach for the future development of corporate work environments by exploring the impact of AI tools on the daily tasks of designers in corporate environments, with a focus on the crea on and evalua on processes of design briefs. [Zhu Z, Lee H, Pan Y and Cai P (2024) AI assistance in enterprise UX design workflows: enhancing design brief crea on for designers. Front. Ar f. Intell. 7:1404647. doi: 10.3389/frai.2024.1404647]. onally, the role of user-centered and personalized approaches in ar ficial intelligence has been examined to objec vely and quan ta vely measure the effec veness of explainable AI (XAI) systems, par cularly in terms of their "informa on power." [Matarese M, Rea F, Rohlfing KJ and Sciu A (2025) How informa ve is your XAI? Assessing the quality of explana ons through informa on power. Front. Comput. Sci. 6:1412341. doi: 10.3389/fcomp.2024.1412341].The AI techniques applied to human-machine interac on have been addressed by inves ga ng research on machine learning applica ons in scanpath analysis for passive gaze-based interac on [Mohamed Selim A, Barz M, Bha OS, Alam HMT and Sonntag D (2024) A review of machine learning in scanpath analysis for passive gaze-based interac on. Front. Ar f. Intell. 7:1391745. doi: 10.3389/frai.2024.1391745]. Furthermore, the poten al of ChatGPT 4 in the assessment of personality traits based on wri en texts has been inves gated [Piastra M and Catellani P (2025) On the emergent capabili es of ChatGPT 4 to es mate personality traits. Front. Ar f. Intell. doi: 10.3389/frai.2025.1484260]. In addi on, the impacts of and visual feedback from assis ve driving systems on drivers have been assessed to enhance the theore cal founda on in the field of automo ve user interface design, par cularly concerning the design of auditory func ons [Zou Z, Khan A, Lwin M, Alnajjar F and Mubin O (2025) Inves ga ng the impacts of auditory and visual feedback in advanced driver assistance systems: a pilot study on driver behavior and emo onal response. Front. Comput. Sci. 6:1499165. doi: 10.3389/fcomp.2024.1499165].Within this research topic, the impact of AI in various sectors of society has been addressed by understanding the factors influencing AI adop on [Ibrahim F, Münscher J-C, Daseking M and Telle N-TThe technology acceptance model and adopter type analysis in the context of ar ficial intelligence. Front. Ar f. Intell. 7:1496518. doi: 10.3389/frai.2024.1496518]. In addi on, the impact of AI tools has been explored on the daily tasks of designers in corporate environments, with a focus on the crea on and evalua on processes of design briefs, by indica ng that AI tools significantly enhance both opera onal experience and subjec ve percep ons across most tasks [Zhu Z, Lee H, Pan Y and Cai P (2024) AI assistance in enterprise UX design workflows: enhancing design brief crea on for designers. Front. Ar f. Intell. 7:1404647. doi: 10.3389/frai.2024.1404647]. Moreover, the impact of AI dimensions has been analysed on family communica on by inves ga ng the mul faceted effects of AI on family communica on [Alfeir NM (2024) Dimensions of ar ficial intelligence on family communica on. Front. Ar f. Intell. 7:1398960. doi: 10.3389/frai.2024.1398960]. The role of AI has been also addressed in the spread of misinforma on on social media by inves ga ng on how AI contributes to the crea on of decep ve war imagery [García-Huete E, Ignacio-Cerrato S, Pacios D, Vázquez-Pole JL, Pérez-Serrano MJ, Donofrio A, Cesarano C, Schetakis N and Di Iorio A (2025) Evalua ng the role of genera ve AI and color pa erns in the dissemina on of war imagery and disinforma on on social media. Front. Ar f. Intell. doi:Finally, the impacts of AI dimensions on family communica on have been analyzed by inves ga ng the mul faceted effects of AI on family communica on [Alfeir NM (2024) Dimensions of ar ficial intelligence on family communica on. Front. Ar f. Intell. 7:1398960. doi: 10.3389/frai.2024.1398960].This Research Topic has collected a cross-disciplinary perspec ve on human-machine interac on and its influence in several contexts.