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.

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers’ mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers’ coding preferences and environments, ensuring seamless integration into diverse workflows. • Users want flexible triggers: balance control with smart automation • Inline works for short code; sidebar/chatbot for longer suggestions • Start minimal, let users expand from single lines to full files • Keep explanations short, contextual, opened by click or shortcut • Let users tune timing, style, detail level, and coding format

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 reviews the current role of Large Language Models (LLMs) in the treatment and support of people with autism spectrum disorder (ASD). We explore applications of LLM-based systems designed to improve communication, social skills, and emotional learning for individuals with ASD, highlighting their ability to generate personalized conversational interactions and simulate social scenarios. Current research demonstrates promising results in different domains, such as dialogue interventions, emotional recognition training, and work-related communication assistance. However, significant challenges remain, including ethical concerns about overreliance on AI, personalization, and privacy. This review synthesizes recent findings, highlights gaps in the existing literature, and proposes directions for future investigations. It particularly underscores the need for real-world applications and long-term studies to assess the efficacy of LLM efficacy in interventions for ASD.

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.