This paper describes a framework that serves as a reference for classifying user interfaces supporting multiple targets, or multiple contexts of use in the field of context-aware computing. In this framework, a context of use is decomposed in three facets: the end users of the interactive system, the hardware and software computing platform with which the user have to carry out their interactive tasks and the physical environment where they are working. Therefore, a context-sensitive user interface is a user interface that exhibits some capability to be aware of the context (context awareness) and to react to changes of this context. This paper attempts to provide a unified understanding of context-sensitive user interfaces rather than a prescription of various ways or methods of tackling different steps of development. Rather, the framework structures the development life cycle into four levels of abstraction: task and concepts, abstract user interface, concrete user interface and final user interface. These levels are structured with a relationship of reification going from an abstract level to a concrete one and a relationship of abstraction going from a concrete level to an abstract one. Most methods and tools can be more clearly understood and compared relative to each other against the levels of this framework. In addition, the framework expresses when, where and how a change of context is considered and supported in the context-sensitive user interface thanks to a relationship of translation. In the field of multi-target user interfaces is also introduced, defined, and exemplified the notion of plastic user interfaces. These user interfaces support some adaptation to changes of the context of use while preserving a predefined set of usability propertie

Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions. We evaluated twelve models on dimensions including confidence-based accuracy, missing answer recall, and unknown recall. Despite high accuracy on multiple-choice questions, our study revealed significant metacognitive deficiencies across all tested models. Models consistently failed to recognize their knowledge limitations and provided confident answers even when correct options were absent. In this work, we show that current models exhibit a critical disconnect between perceived and actual capabilities in medical reasoning, posing significant risks in clinical settings. Our findings emphasize the need for more robust evaluation frameworks that incorporate metacognitive abilities, essential for developing reliable Large Language Model enhanced clinical decision support systems.

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

Fusion engines are fundamental components of multimodal inter-active systems, to interpret input streams whose meaning can vary according to the context, task, user and time. Other surveys have considered multimodal interactive systems; we focus more closely on the design, specification, construction and evaluation of fusion engines. We first introduce some terminology and set out the major challenges that fusion engines propose to solve. A history of past work in the field of fusion engines is then presented using the BRETAM model. These approaches to fusion are then classified. The classification considers the types of application, the fusion principles and the temporal aspects. Finally, the challenges for future work in the field of fusion engines are set out. These include software frameworks, quantitative evaluation, machine learning and adaptation.