Abstract Interventions utilizing autobiographical memory (AM) frequently depend on verbal remembrance; nevertheless, their effectiveness may be constrained in the absence of sensory stimuli and emotional reinforcement. Socially Assistive Robots (SARs) provide a multimodal option; yet, there is limited understanding of how robotic contact influences users’ emotional experiences during autobiographical memory recall. We established a pipeline wherein the humanoid robot Pepper performs life-span autobiographical interviews and produces synthetic visuals from real-time speech transcripts. Fifteen adults participated in two counterbalanced conditions during a single session: (i) grasping the robot hand while recounting two memories, and (ii) recounting two memories without tactile interaction. The results show that touch markedly improved affective valence and resulted in a more substantial post-session decline in Negative Affect, mostly due to reductions in evaluations of "nervous" and "Hostile." Arousal and dominance exhibited stability. Participants assessed the system as amiable and intelligent, despite its only mild anthropomorphic qualities. These findings suggest that robotic touch can enhance the enjoyment of robot-mediated memory and specifically alleviate anxiety without modifying the recalled content. The use of a tactile channel enhances the emotional effectiveness of SAR-based AM treatments, establishing a foundation for longitudinal studies including older persons and cognitively at-risk groups.

In the contemporary metaverse landscape, comprehending the intricacies of human interaction is imperative for enhancing communication within Virtual Reality (VR) experiences. At the core of meaningful social relationships lie empathy and trust, pivotal elements nurtured by the capacity to comprehend both self and others’ thoughts and intentions. Conventional face-to-face interactions heavily rely on non-verbal cues, such as body language and facial expressions, to convey messages and display empathy.

This paper introduces "How Are You Today?" (HAYT), a mobile application developed to support the diagnosis and treatment of anxiety and depression. HAYT integrates a digital diary and Natural Language Processing (NLP) to analyze emotional states and predict anxiety or panic attacks. Users document their experiences and emotions while completing a questionnaire based on Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria, helping clinicians monitor symptoms of depression and anxiety. The app combines real-time feedback, Cognitive Behavioral Therapy (CBT) interventions, and direct communication with mental health professionals via a secure messaging system. Preliminary findings from a feasibility study using synthetic data show a significant correlation between sentiment analysis of diary entries and self-reported depressive symptoms. This suggests HAYT’s potential for improving mental health care accessibility and effectiveness by providing continuous monitoring and personalized support.

The application of artificial intelligence (AI) in medical imaging has significantly improved diagnostic accuracy. However, the reliance on black-box models remains a barrier to its widespread adoption in clinical settings. This article compares the main techniques of explainable AI (XAI), such as Grad-CAM, LIME, and SHAP, evaluating their effectiveness in interpreting deep learning models used in medical imaging. Two case studies are analyzed, comparing the three methods and highlighting their strengths and weaknesses. The results of this analysis show that Grad-CAM provides intuitive visualizations; LIME offers excellent flexibility in application; and SHAP delivers complete and accurate explanations, despite its high computational load.

Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system’s potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement.

Technological evolution has enabled the development of new artificial intelligence (AI) models with generative capabilities. Among them, one of the most discussed is the virtual agent ChatGPT. This chatbot may occasionally produce fake information, as also declared by the producer OpenAI. Such a model may provide very useful support in several tasks, ranging from text summarization to programming. The research community has marginally investigated the impact that fake information created by AI models has on the users’ perceptions and on their belief in AI. We analyzed the impact of the fake information produced by AI on user perceptions, specifically trust and satisfaction, by performing a user study on ChatGPT. An additional issue is assessing whether the early or late knowledge of the possibility of the tool generating fake information has a different impact on the users’ perceptions. We conducted an experiment, involving 62 university students, a category of users who may employ tools such as ChatGPT extensively. The experiment consisted of a guided interaction with ChatGPT. Some of the participants experienced the failure of the chatbot, while a control group only received correct and reliable answers. We collected participants’ perceptions of trust, satisfaction, and usability, together with the net promoter score (NPS). The results demonstrated a statistically significant difference in trust and satisfaction between the users who early experienced fake information production compared to those who discovered ChatGPT’s faulty behaviors later during the interaction. Also, there is no statistically significant difference among the users who received the late fake information and the control group (no fake information). Usability and the NPS also resulted higher when the fake news was detected in the late interaction. When users are aware of the fake information generated by ChatGPT their trust and satisfaction decrease, especially when they impact on this at the early stage of use of the chatbot. Nevertheless, the perception of trust and satisfaction still remains high, as some of the users are still enthusiastic; others consider a more conscious use of the tool in terms of support to be verified. A useful strategy could be to favor a critical use of ChatGPT, letting young people to verify the provided information. This should be a new way to perform learning activities.