We present an extension of Triangolazioni (a Cultural Heritage webapp) to enrich curated content with context-dependent, external information provided by Large Language Models (LLMs) within a loosely-coupled architecture agnostic to the LLM. The system supports context-dependent information search and presentation within an architecture agnostic to the exploited LLM.

Travel experiences can challenge individuals with Autism Spectrum Disorder (ASD) before embarking on a trip, because of the complexity of planning, but also during the trip, due to sensory overstimulation while visiting places. Artificial Intelligence (AI) could help overcome some obstacles. However, it might represent a barrier for people with mid-functioning autism if their needs for assistance are not considered when designing tourism applications. Moreover, most trip planners fully control the generation of solutions, undermining users’ decision autonomy. To address these challenges, we explored the development of itinerary planning technologies through a human-centered approach. We aimed to balance guiding users in their decision-making with empowering them to plan tours independently. The result is CARES, an Artificial Intelligence-driven trip planner designed for autistic adults, which we present in this article. CARES applies a collaborative approach to developing itineraries, based on the exploitation of (i) AI technologies for information filtering and interactive itinerary planning that are robust to the data scarcity characterizing the autism domain; (ii) a user interface that reduces information overload and decision fatigue in information exploration. We tested CARES with 14 autistic adults, gathering insights to guide future design and improve the usability of Artificial Intelligence for neurodivergent users. The results indicate that our approach provides an accessible solution for enhancing the travel experiences of mid- to high-functioning autistic adults. In doing so, it contributes to more inclusive tourism. • We found that, despite difficulties, autistic adults wish to plan trips autonomously. • We propose a Human-Centered AI trip planner for mid/high-functioning ASD users. • Step-by-step guidance helps autistic users plan trips with limited effort. • The app empowers users to make choices while AI guides the process, ensuring balanced control. • The app balances user guidance and decision-making control.

User experience and trust in AI-assisted technologies are key factors in controlling their adoption. We investigate these aspects in an Agentic AI platform that integrates routine productivity services and exhibits different levels of autonomy: a manual baseline that lacks AI-driven automation, an Agentic AI with medium autonomy that requires user confirmation before acting, and an Agentic AI with high autonomy that acts proactively for low-stakes tasks. The study, involving 230 participants with heterogeneous professional backgrounds, examines how autonomy of the system affects user activity, user workload, perceived support, and trust. We found that both Agentic AI systems outperformed the baseline in user productivity. In task execution, they achieved a precision of over 82%, higher than the baseline’s 65%. The recall of the Agentic AI system with high autonomy was 63%. This denotes much higher throughput than the system without AI-driven automation (14%). The Agentic AI systems outperformed the baseline in workload reduction (NASA-TLX Aggregate score) with a statistically significant difference. Both AI-driven systems received equivalent or slightly higher trust than the baseline. However, the system with medium autonomy was the best at balancing productivity gains and user preferences for control. Specifically, the correlations between individual user characteristics (Desirability of Control and Propensity to Trust) and the resulting trust in the systems suggest that the influence of personal traits on system evaluation is least pronounced when automation is combined with explicit user intervention. These results encourage the adoption of user-controllable Agentic AI architectures in multitasking support.