Aurelio Ravarini

As Generative artificial intelligence (GenAI) tools become increasingly integrated into clinical practice, their anthropomorphic features raise important questions about their role, reliability and implications for human-centred care. This discussion article examines the concept of anthropomorphic GenAI, highlighting how its human-like traits may influence trust, professional judgement and the dynamics of patient care. While such technologies can enhance efficiency and support clinical decision-making, their design often blurs the line between simulation and authentic human understanding. This paper identifies key gaps in the existing literature, particularly around emotional intelligence, ethical reasoning, specialisation-specific adoption and the long-term impact of AI on clinical practice and proposes a structured research agenda to guide future inquiry and responsible innovation. By drawing attention to the promises and pitfalls of anthropomorphic AI in healthcare, this discussion invites interdisciplinary reflection on how such systems should be designed, evaluated and integrated into healthcare and clinical contexts.

Purpose – This manuscript explores the evolving roles of HR professionals amidst global megatrends and organizational transitions, focusing on the Italian context, which has experienced disruptive adoption of new forms of work such as remote and hybrid work. In this challenging scenario, our research aims to uncover if and how HR professionals are transforming their roles or maintaining the status quo in navigating organizational changes, dealing with the upcoming working scenario, and challenging conventional perceptions of HR practitioners. Design/methodology/approach – The study employs the social-symbolic work lens, that contributes to a deeper understanding of how HR professionals work to construct organizational life, the identities of employees, and the societal norms and assumptions that provide the context for organizational action. This perspective highlights HR professionals' personal efforts, consisting of the emotional labor entailed in steering organizational transformations and, eventually, maintenance in a context where remote work has become prevalent. Data was collected through 16 online focus groups involving 76 HR professionals from Italian organizations. Findings – Our research offers two interrelated contributions to HR literature. First, we provide pieces of evidence on how HR practitioners act as agents of change in two emerging roles: the " Wannabe Hero " and the " Ordinary Hero ". This challenges the prevailing rhetorical discourse about the so-called HR business partner. Secondly, we delve into the persistent obstacles that hinder HR professionals from making a substantial impact in addressing radical changes. These findings will provide useful insights into effectively engaging HR practitioners as agents of change in organizational transformation, shedding light on praxis, structures, and their emotional work. Originality/value – The paper analyzes HR professionals’ social-symbolic work, which offers an original contribution to the comprehension of the activities they carry on in practice and the emotions they have been experiencing. These influence both the way HR professionals play their role and the organizational and institutional environment.

This paper explores the implementation of Game-Based Learning (GBL) in higher education, with a particular focus on the dual role of students as developers and players of educational games. In particular, the study investigates the extent to which GBL leverages intrinsic motivation, fosters engagement, consolidates prior knowledge, and cultivates essential 21st-century skills in undergraduate students that designed, developed, and played interactive digital gamebooks on STEM disciplines. A mixed method based on surveys and focus groups was leveraged to assess the diverse learning outcomes and the educational impact of the project. Findings highlightsthat theGBL approach significantly increased student engagement, fostered deeper learning in IT-related skills, and provided valuable experiences in competences such as project management and teamwork. However, the integration of advanced academic content in Mathematics and Statistics was perceived as less effective, posing challenges to knowledge acquisition and consolidation. This study shows that while GBL is highly effective in promoting motivation and skill development, further refinement is needed to align content complexity with learning objectives.

This study explores how prompt engineering training influences student performance and adaptability when using Generative AI (GenAI) chatbots in education. Focusing on first-year engineering management students, we examine the impact of targeted instruction on learning outcomes, linking AI interaction to academic self-efficacy, goal orientation, self-monitoring, study skills, and technology engagement. Using a mixed-methods approach, results show that prompt engineering significantly enhances academic performance and critical engagement with AI tools. Findings highlight the importance of integrating AI literacy into curricula to maximize GenAI’s educational potential and prepare students for AI-driven professional environments.

This study investigates how Generative AI (GenAI) chatbots mediate sensemaking in organizations, particularly in contexts characterized by equivocality. Drawing on the enactment model by Weick and colleagues (2005), we analyze how GenAI technology interacts with workers during sensemaking. Our findings reveal that GenAI chatbots act as assistants, collaborators, and facilitators, by both introducing as well as reducing equivocality in an organizational context through iterative dialogue and contextual adaptation. The chatbots do not only transform cues into actionable insights, they also introduce novel ways of structuring and interpreting information. Furthermore, workers’ presumption of the chatbot behavior shapes their interaction with it, influencing its role in navigating equivocality during sensemaking. By extending theoretical boundaries of technology-mediated sensemaking, this research provides actionable insights into leveraging GenAI for organizational resilience and adaptability in dynamic environments.

This study offers a comprehensive understanding of Generative AI’s role in knowledge management and knowledge retention within SMEs, proposing a robust framework that integrates social and technical factors to promote continuous learning and innovation. Despite substantial research on the general impact of AI on knowledge management, there is a notable scarcity of literature focusing specifically on Generative AI and its role in enhancing knowledge retention in SMEs. This gap is critical, given SMEs’ unique challenges, such as limited resources and high employee turnover rates. The research question guiding this study is: “To what extent does Generative AI impact knowledge retention in SMEs?”.
The article employs a socio-technical systems framework to analyze the variables influencing knowledge sharing, emphasizing the interplay between individual motivation, organizational support and culture, and technological capability. This approach is complemented by the concept of human-centric digital technology, which focuses on enhancing human capabilities and experiences through technology. Generative AI enhances knowledge management practices by automating processes, analyzing large datasets, and generating new content, thus improving the efficiency and quality of knowledge sharing. It also reduces the administrative burden on employees, fosters a collaborative culture, and provides personalized feedback, thereby increasing individual motivation for knowledge sharing. Furthermore, Generative AI can address the inherent challenges SMEs face in knowledge retention by making critical knowledge easily accessible, reducing the learning curve for new employees, and maintaining operational efficiency. However, it is crucial to manage employees’ perceptions of Generative AI to avoid resistance or sabotage due to fears of redundancy.

Driven by the growing availability and accessibility of data and processing power, businesses across all sectors are developing and implementing increasingly "intelligent" systems that are empowered by a wave of artificial intelligence (AI) technologies. This paper examines the state of AI adoption in small and medium-sized enterprises (SMEs) in the manufacturing sector, an area of research that has received relatively little attention. Three SMEs, whose main offices are located in Northern Italy, have been studied to understand how they deal with the issue of AI adoption and what problems they typically encounter. An articulated picture emerges where SMEs struggle between the desire to realize the promises of innovation and the ability to build the appropriate organizational setting to pursue it.

All'interno di un quadro sociale ed economico sempre più articolato e dinamico, le università hanno il compito di educare – non solo formare – i giovani di oggi: i lavoratori e i cittadini di domani, indirizzandoli verso un apprendimento permanente e di valore, centrato sulla persona. Per fare questo, sono chiamate a stare al passo con l'innovazione, se non addirittura a farsene portavoce, rivedendo costantemente il proprio modello di business, i processi e le prassi didattiche nell'ottica di una sempre più efficace realizzazione del proprio mandato. Il volume, sulla base di una dettagliata analisi dei principali trend della higher education, offre una panoramica sulle dimensioni chiave dell'azione didattica. In particolare, il modello didattico di LIUC– Università Cattaneo ivi presentato mette in luce l'importanza di investire in un processo di miglioramento continuo, per supportare gli studenti nell'affrontare una sfida tanto delicata e complessa: imparare ad imparare, per saper sfruttare le sfide che li aspettano come occasioni per reinventarsi.

In the era of digital transformation, understanding the interplay between Artificial Intelligence (AI) and human decision-makers is crucial. This paper examines the nuanced dynamics of AI in decision-making processes, focusing on its role as a decision-maker, an enabler, and its impact on work paradigms and organizational structures. Through a comprehensive literature review and qualitative case studies across diverse sectors, the study introduces a novel analytical framework that aligns AI's role with decision-making stages. This framework aims to identify optimal organizational structures, ranging from full human to full AI delegation, including hybrid models. By highlighting the human-centric approach and the interdependence of human and AI in decision-making, this research contributes significantly to the evolving field of organizational behavior and decision science, providing insights into effective AI integration and the transformative role of Generative AI in professional landscapes.