COVID-19 caused an abrupt switch from face-to-face to online teaching. This led to unknown challenges and consequences for students and lecturers. In the first semester after its outbreak, we developed a messenger-based chatbot to perform an experience sampling study to evaluate students’ well-being and experiences (n = 31) with the radical changes in higher education. Finding a decrease in students’ perceived motivation but an increase in productivity, we conducted a follow-up survey to compare the development a year later (n = 41). Our results revealed two main student profiles, one feeling severely impacted by the persisting social distance in their study performance and the other appreciating the flexibility and expended free time due to the changes in the teaching formats. Based on our findings, we introduce implications for the overall design of higher education and show the benefits and challenges of combining chatbot-enabled experience sampling with traditional surveys.

Whilst imbuing robots and voice assistants with personality has been found to positively impact user experience, little is known about user perceptions of personality in purely text-based chatbots. In a within-subjects study, we asked N=34 participants to interact with three chatbots with different levels of Extraversion (extraverted, average, introverted), each over the course of four days. We systematically varied the chatbots’ responses to manipulate Extraversion based on work in the psycholinguistics of human behaviour. Our results show that participants perceived the extraverted and average chatbots as such, whereas verbal cues transferred from human behaviour were insufficient to create an introverted chatbot. Whilst most participants preferred interacting with the extraverted chatbot, participants engaged significantly more with the introverted chatbot as indicated by the users’ average number of written words. We discuss implications for researchers and practitioners on how to design chatbot personalities that can adapt to user preferences.

We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistant’s role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command “Q&As”.

To give robots, which are black box systems for most users, feedback we have to implement interaction paradigms that users understand and accept, for example reward and punishment. In this paper we present the first HRI experience prototype which implements gradual destructive interaction, namely breaking a robot's leg as a punishment technique. We conducted an exploratory experiment (N=20) to investigate participants' behavior during the execution of three punishment techniques. Using a structured analysis of videos and interviews, we provide in-depth insights into participants' attitude towards these techniques. Participants preferred more abstract techniques and felt uncomfortable during human-like punishment interaction. Based on our findings, we raise questions how human-like technologies should be designed. A video documentation of the project can be found here: https://vimeo.com/348646727

Psychological targeting tries to influence and manipulate users' behaviour. We investigated whether users can protect themselves from being profiled by a chatbot, which automatically assesses users' personality. Participants interacted twice with the chatbot: (1) They chatted for 45 minutes in customer service scenarios and received their actual profile (baseline). (2) They then were asked to repeat the interaction and to disguise their personality by strategically tricking the chatbot into calculating a falsified profile. In interviews, participants mentioned 41 different strategies but could only apply a subset of them in the interaction. They were able to manipulate all Big Five personality dimensions by nearly 10%. Participants regarded personality as very sensitive data. As they found tricking the AI too exhaustive for everyday use, we reflect on opportunities for privacy protective designs in the context of personality-aware systems.

We present the first systematic analysis of personality dimensions developed specifically to describe the personality of speech-based conversational agents. Following the psycholexical approach from psychology, we first report on a new multi-method approach to collect potentially descriptive adjectives from 1) a free description task in an online survey (228 unique descriptors), 2) an interaction task in the lab (176 unique descriptors), and 3) a text analysis of 30,000 online reviews of conversational agents (Alexa, Google Assistant, Cortana) (383 unique descriptors). We aggregate the results into a set of 349 adjectives, which are then rated by 744 people in an online survey. A factor analysis reveals that the commonly used Big Five model for human personality does not adequately describe agent personality. As an initial step to developing a personality model, we propose alternative dimensions and discuss implications for the design of agent personalities, personality-aware personalisation, and future research.

This reflection paper takes the 25th IUI conference milestone as an opportunity to analyse in detail the understanding of intelligence in the community: Despite the focus on intelligent UIs, it has remained elusive what exactly renders an interactive system or user interface "intelligent", also in the fields of HCI and AI at large. We follow a bottom-up approach to analyse the emergent meaning of intelligence in the IUI community: In particular, we apply text analysis to extract all occurrences of "intelligent" in all IUI proceedings. We manually review these with regard to three main questions: 1) What is deemed intelligent? 2) How (else) is it characterised? and 3) What capabilities are attributed to an intelligent entity? We discuss the community's emerging implicit perspective on characteristics of intelligence in intelligent user interfaces and conclude with ideas for stating one's own understanding of intelligence more explicitly.

The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This article contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps, and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps’ algorithmic decision-making. We enriched this data with users’ coping and support strategies through a follow-up online survey (N = 286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output rather than processes.

The complex nature of intelligent systems motivates work on supporting users during interaction, for example through explanations. However, there is yet little empirical evidence on specific problems users face in such systems in everyday use. This paper investigates such problems as reported by users: We analysed 35,448 reviews of three apps on the Google Play Store (Facebook, Netflix and Google Maps) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps' algorithmic decision-making. We enriched this data with users' coping and support strategies through a follow-up online survey (N=286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output, not processes. Our work thus contributes empirical evidence to facilitate understanding of users' everyday problems with intelligent systems.

Emojis are commonly used as non-verbal cues in texting, yet may also lead to misunderstandings due to their often ambiguous meaning. User personality has been linked to understanding of emojis isolated from context, or via indirect personality assessment through text analysis. This paper presents the first study on the influence of personality (measured with BFI-2) on understanding of emojis, which are presented in concrete mobile messaging contexts: four recipients (parents, friend, colleague, partner) and four situations (information, arrangement, salutory, romantic). In particular, we presented short text chat scenarios in an online survey (N=646) and asked participants to add appropriate emojis. Our results show that personality factors influence the choice of emojis. In another open task participants compared emojis found as semantically similar by related work. Here, participants provided rich and varying emoji interpretations, even in defined contexts. We discuss implications for research and design of mobile texting interfaces.

The control of user interfaces while driving is a textbook example for driver distraction. Modern in-car interfaces are growing in complexity and visual demand, yet they need to stay simple enough to handle while driving. One common approach to solve this problem are multimodal interfaces, incorporating e.g. touch, speech, and mid-air gestures for the control of distinct features. This allows for an optimization of used cognitive resources and can relieve the driver of potential overload. We introduce a novel modality for in-car interaction: our system allows drivers to use facial expressions to control a music player.

Children can be a distraction to the driver during a car ride. With our work, we try to combine the possibility of facial expression recognition in the car with a game for children. The goal is that the parents can focus on the driving task while the child is busy and entertained. We conducted a study with children and parents in a real driving situation. It turned out that children can handle and enjoy games with facial recognition controls, which leads us to the conclusion that face recognition in the car as a entertaining system for children should be developed further to exploit its full potential.

Each step of statistical analysis requires researchers to make decisions based on both statistical knowledge and the knowledge of their own data. For novice analysts, this is cognitively demanding and can lead to mistakes and misinterpretations of the results. We present Statsplorer, a software that helps novices learn and perform inferential statistical tests. It lets the user kick-start data analysis from their research questions. Statsplorer automatically tests necessary statistical assumptions and uses visualizations to guide the user in both selecting statistical tests and interpreting the results. We compared Statsplorer with a statistics lecture and investigated how Statsplorer prepares novices for learning statistics in an AB/BA crossover experiment. The results indicates that using Statsplorer prior to the lecture leads to significantly better test scores in understanding statistical assumptions and choosing appropriate statistical tests. Statsplorer is open-source and is available online at: http://hci.rwth-aachen.de/statsplorer.