[This retracts the article DOI: 10.1007/s00779-021-01531-6.].

The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Intelligent systems, such as chatbots, are likely to strike new qualities of UX that are not covered by instruments validated for legacy human–computer interaction systems. A new validated tool to evaluate the interaction quality of chatbots is the chatBot Usability Scale (BUS) composed of 11 items in five subscales. The BUS-11 was developed mainly from a psychometric perspective, focusing on ranking people by their responses and also by comparing designs’ properties (designometric). In this article, 3186 observations (BUS-11) on 44 chatbots are used to re-evaluate the inventory looking at its factorial structure, and reliability from the psychometric and designometric perspectives. We were able to identify a simpler factor structure of the scale, as previously thought. With the new structure, the psychometric and the designometric perspectives coincide, with good to excellent reliability. Moreover, we provided standardized scores to interpret the outcomes of the scale. We conclude that BUS-11 is a reliable and universal scale, meaning that it can be used to rank people and designs, whatever the purpose of the research.

In the growing field of sonic interaction design, increasing emphasis is being placed on walking-based interactions within an array of applications, including virtual reality, interactive media, and rehabilitation. Our study focuses on recreating the aural experience of wading through water, specifically the challenge of eliciting accurate and natural movement-sound associations for wading, both in interactive and listening-only contexts. We engineered a real-time wading simulation using a digital Foley technique that maps lower limb angular velocity contours to the intensity of steady-state flowing sounds. Our first iteration was evaluated in a perceptual experiment involving 16 participants, as well as an interactive test with 9 participants, which revealed the need for additional sensors along with time-domain preprocessing to ensure a consistently natural sound envelope across walking cadences (step tempi). We then refined the mapping function and incorporated more sound layers. In our subsequent listening test, 55 participants compared the realism of the initial and refined versions with real-life wading sounds at various step cadences. While the refined version demonstrated a notable improvement over the initial one and was deemed fairly realistic overall, it fell just short of the authenticity of the real-life recordings at faster cadences, suggesting room for further improvement of our simulation. Nevertheless, this research marks a stride in the evolution of walking-based sonic interactions, instigating wider acceptance and application of such systems in the future.

The proliferation of sensors and smart Internet of Things (IoT) devices in our everyday environments is reshaping our interactions with everyday objects. This change underlines the need to empower non-expert users to easily configure the behaviour of these devices to align with their preferences and habits. At the same time, recent advances in generative transformers, such as ChatGPT, have opened up new possibilities in a variety of natural language processing tasks, enhancing reasoning capabilities and conversational interactions. This paper presents RuleBot + + , a conversational agent that exploits GPT-4 to assist the user in the creation and modification of trigger-action automations through natural language. After an introduction to motivations and related work, we present the design and implementation of RuleBot + + and report the results of the user test in which users interacted with our solution and Home Assistant, one of the most used open-source tools for managing smart environments.

In this paper, we propose a reduced reality [RR] concept of less-is-more that virtual reality [VR] designers can use to create technological frameworks which reduce sensory overload and allow for better concentration and focus, less stress, and novel scenarios. We question the approach taken by scholars in the field of extended reality [XR] research, where the focus is typically to design and use technology that adds sensory information to the user’s perceptual field, and we address some of the confusion related to the typical uses of the term reality. To address the latter terminological muddle, we define reality as our conscious experience of the environment, an emergent perception, and we use this definition as the basis for a discussion on the role of sound in balancing sensory information and in the construction of less cluttered and less stressful perceptual environments. We further discuss current auditory strategies that reduce and alter auditory stimuli, and we suggest potential future strategies for RR that use crossmodal design approaches, masking, and filtering techniques.

Abstract Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials.