Working out a common task: design and evaluation of user-intelligent system collaboration

This paper describes the design and user evaluation of an intelligent user interface intended to mediate between users and an Adaptive Information Extraction (AIE) system. The design goal was to support a synergistic and cooperative work. Laboratory tests showed the approach was efficient and effective; focus groups were run to assess its ease of use. Logs, user satisfaction questionnaires, and interviews were exploited to investigate the interaction experience. We found that user’ attitude is mainly hierarchical with the user wishing to control and check the system’s initiatives. However when confidence in the system capabilities rises, a more cooperative interaction is adopted

A Review on Adaptive Menus

Intelligent User Interface (IUI) is an emerging interdisciplinary research area that focuses on improving the usability of existing user interfaces. Adaptive menus are the part of the IUI that is trying to improve existing menus' usability by reducing the selection time. This paper surveys the most relevant studies that are carried out in this field. First, it introduces Adaptive User Interface (AUI) and adaptive menus then describe various adaptation styles and adaptation policies that are being used in adaptive menus along with their benefits and drawbacks. It then lists the applications of adaptive systems and how they can be used, as well as the limitations and future direction of the work. Keywords: IUI (Intelligent UI), Adaptive UI, Adaptive Menus DOI: 10.7176/CTI/11-01 Publication date:October 31st 202

From ”Explainable AI” to ”Graspable AI”

Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from understanding and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML systems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.QC 20210526</p

Challenging the Need for Transparency, Controllability, and Consistency in Usable Adaptation Design

Adaptive applications constitute the basis for many ubiquitous computing scenarios as they can dynamically adapt to changing contexts. The usability design principles transparency, controllability, and consistency have been recommended for the design of adaptive interfaces. However, designing self-adaptive applications that may act completely autonomous is still a challenging task because there is no set of usability design guidelines. Applying the three principles in the design of the five different adaptations of the mobile adaptive application Meet-U revealed as difficult. Based on an analysis of the design problem space, we elaborate an approach for the design of usable adaptations. Our approach is based on a notification design concept which calculates the attention costs and utility benefits of notified adaptations by varying the design aspects transparency and controllability. We present several designs for the adaptations of Meet‑U. The results of a user study shows that the notification design approach is beneficial for the design of adaptations. Varying transparency and controllability is necessary to adjust an adaptation’s design to the particular context of use. This leads to a partially inconsistent design for adaptations within an application