Collaboration is a key use case for conversational AI agents. Yet we know little about how agents’ collaborative effort affects users’ reciprocal effort and how this relates to perceptions of agent conversational capability. Through an online director-matcher task (n = 267) whereby participants interacted with agents that varied in their collaborative effort, we found that users rated agents that were less communicatively collaborative as less competent partners. Yet, contrary to the division of labour principle in communication, users only increased their own collaborative effort as speakers when communicating with more collaborative agents, whilst also benefitting more as listeners when interacting with such agents. We discuss the implications of these findings bringing together partner modelling and division of labour principles in driving human-agent collaborative communication in both speaker and listener effort, and consider the strategic application of agent collaborative effort in the design of conversational AI.

The ARTICULATE project is an ambitious and interdisciplinary initiative funded by the CHIST-ERA call 2025. Its vision is to revolutionize science education and democratize scientific knowledge beyond academia and English-speaking audiences through the integration of AI with self-regulated learning. The aim is to translate science not just across language but across language style, to create engaging spoken digital experiences. We present an introduction to this project, an overview of the consortium and research approach, and a number of expected impacts.

With the continuous growth of multimedia archives, there is significant interest in developing search-effective methods to locate relevant content which are natural and intuitive to users. Conversational search presents an opportunity to address these challenges by improving multimedia search effectiveness and reducing the cognitive load on users. In this study, we conduct an experiment to investigate multi-stage conversational assistance for multimedia search, incorporating multimodal clarification questions through a text-based chat interface. In our study, we compare user experience for our conversational approach to an equivalent conventional “one-shot” text-based search approach. We find that users rate their search experience statistically signif-icantly more positively in our conversational filtering condition compared to the one shot search condition. They rate it as more attractive (p <. 001), efficient (p =. 0036), dependable (p =. 001), stimulating (p <. 01) and novel (p <. 01). Our results indicate that conversational filtering has the potential to offer a more engaging and efficient way to search, particularly in image retrieval scenarios.

Conversational agents (CAs) are a tempting type of computer interface for assisting people’s mental health due to their ability to simulate human-like interactions, however their integration within the broader social context of mental health management remains largely under-explored. Recognizing that managing one’s mental health is often a social rather than individual activity involving close persons such as partners, family, and friends, our research takes a social-orientation to mental health management. Utilizing design cards that depict fictional, yet plausible CA concepts, we present the analysis of an interview study with 24 young adults to understand their views on CAs for both their own use and for a close person. Participants viewed CAs as potentially valuable complements to human support, but expressed concerns about over-reliance and replacement. Our analysis reveal key tensions, design considerations, and opportunities for integrating CAs into mental health ecosystems in ways that respect and enhance existing social support structures.

Recent work has looked to understand user perceptions of speech agent capabilities as dialogue partners (termed partner models), and how this affects user interaction. Yet, partner model effects are currently inferred from language production as no metrics are available to quantify these subjective perceptions more directly. Through three phases of work, we develop and validate the Partner Modelling Questionnaire (PMQ): an 18-item self-report semantic differential scale designed to reliably measure people’s partner models of non-embodied speech interfaces. Through confirmatory factor analysis, we confirm that the PMQ scale consists of three factors: communicative competence and dependability, human-likeness in communication and communicative flexibility. Our studies show that the measure consistently demonstrates good internal reliability, strong test-retest reliability over 4- and 12-week intervals, and predictable convergent/divergent validity. Based on our findings, we discuss the multidimensional nature of partner models, while identifying key future research avenues that the development of the PMQ facilitates. Notably, this includes the need to identify the activation, sensitivity, and dynamism of partner models in speech interface interaction.

Voice Agents (VAs) are touted as being able to help users in complex tasks such as cooking and interacting as a conversational partner to provide information and advice while the task is ongoing. Through conversation analysis of 7 cooking sessions with a commercial VA, we identify challenges caused by a lack of contextual awareness leading to irrelevant responses, misinterpretation of requests, and information overload. Informed by this, we evaluated 16 cooking sessions with a wizard-led context-aware VA. We observed more fluent interaction between humans and agents, including more complex requests, explicit grounding within utterances, and complex social responses. We discuss reasons for this, the potential for personalisation, and the division of labour in VA communication and proactivity. Then, we discuss the recent advances in generative models and the VAs interaction challenges. We propose limited context awareness in VAs as a step toward explainable, explorable conversational interfaces.

Interest in unethical user interfaces has grown in HCI over recent years, with researchers identifying malicious design strategies referred to as “dark patterns”. While such strategies have been described in numerous domains, we lack a thorough understanding of how they operate in social networking services (SNSs). Pivoting towards regulations against such practices, we address this gap by offering novel insights into the types of dark patterns deployed in SNSs and people’s ability to recognise them across four widely used mobile SNS applications. Following a cognitive walkthrough, experts (N = 6) could identify instances of dark patterns in all four SNSs, including co-occurrences. Based on the results, we designed a novel rating procedure for evaluating the malice of interfaces. Our evaluation shows that regular users (N = 193) could differentiate between interfaces featuring dark patterns and those without. Such rating procedures could support policymakers’ current moves to regulate deceptive and manipulative designs in online interfaces.

With the remarkable improvement in technical systems for generating realistic virtual humans, there comes a requirement to quantify the effects that different aspects of realism can have on users. The study outlined here sought to advance research on emotion perception and virtual humans by assessing basic empathic responses to high fidelity emotionally expressive characters. We report findings on participants experiences of cognitive, affective and compassionate empathy, as well as measurements for the uncanny valley at two levels of visual realism. We find that the levels of emotion expressed by virtual humans within our study influenced ratings of empathy and the uncanny valley, with positive and negative valence having significant effects on empathic responses and perceived appeal. We discuss these findings in relation to studies which have measured empathy responses to animated characters, as well as notable differences in uncanny valley measurements.

Research in HCI has shown a growing interest in unethical design practices across numerous domains, often referred to as “dark patterns”. There is, however, a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users’ self-determination regarding control over personal data and time spent on SNSs. We collected over 16 hours of screen recordings from Facebook’s, Instagram’s, TikTok’s, and Twitter’s mobile applications to understand how dark patterns manifest in these SNSs. For this task, we turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: We observed which instances occur in SNSs and identified two strategies — engaging and governing — with five dark patterns undiscovered before.

The increased popularity of CUIs has motivated HCI work around specific approaches to research, design, and implementation, while also reflecting on these topics. However, current research is highly fragmented and lacks critical mass around topics such as theory, methods and design. Building this critical mass is a fundamentally multidisciplinary endeavour. CUIs involve language based interaction, either through speech or text, with another agent(s) or device(s). This type of interaction not only needs to engage with traditional HCI approaches, but also to embrace methods from communicative and social sciences. This is crucial for making progress towards human-centred conversational interfaces. Along with the recent ACM SIGCHI Conversational User Interfaces conference (ACM CUI), this special issue showcases research to further solidify the foundations of the field in these areas. Below we outline some key challenges faced by the field, describe the papers in this special issue, and then outline areas for future research.

Speech-enabled smart speakers are common devices used for numerous tasks in everyday life. While speech-enabled technologies are widespread, using one’s voice as a computing modality introduces new accessibility challenges for people with speech disfluencies such as stammering (also known as stuttering). This paper investigates the smart speaker user experiences of people who stammer over three weeks. We conducted diary studies and semi-structured interviews with 11 individuals to identify their daily routines, difficulties with successful interactions, and strategies to overcome these barriers. Our analysis demonstrates key factors such as device location, its affordances, and the structure of commands had a strong impact on user experience. Participants highlighted different linguistic strategies to try and overcome interaction difficulties and discussed the potential of using smart speakers for speech and language therapy. We emphasise the need to further understand the experiences of people who stammer in smart speaker design to increase their accessibility.

Voice-based Conversational Agents (CAs) are increasingly being used by children. Through a review of 38 research papers, this work maps trends, themes, and methods of empirical research on children and CAs in HCI research over the last decade. A thematic analysis of the research found that work in this domain focuses on seven key topics: ascribing human-like qualities to CAs, CAs? support of children?s learning, the use and role of CAs in the home and family context, CAs? support of children?s play, children?s storytelling with CA, issues concerning the collection of information revealed by CAs, and CAs designed for children with differing abilities. Based on our findings, we identify the needs to account for children’s intersectional identities and linguistic and cultural diversity and theories from multiple disciples in the design of CAs, develop heuristics for child-centric interaction with CAs, to investigate implications of CAs on social cognition and interpersonal relationships, and to examine and design for multi-party interactions with CAs for different domains and contexts.

We present RoomReader, a corpus of multimodal, multiparty conversational interactions in which participants followed a collaborative student-tutor scenario designed to elicit spontaneous speech. The corpus was developed within the wider RoomReader Project to explore multimodal cues of conversational engagement and behavioural aspects of collaborative interaction in online environments. However, the corpus can be used to study a wide range of phenomena in online multimodal interaction. The publicly-shared corpus consists of over 8 hours of video and audio recordings from 118 participants in 30 gender-balanced sessions, in the “in-the-wild” online environment of Zoom. The recordings have been edited, synchronised, and fully transcribed. Student participants have been continuously annotated for engagement with a novel continuous scale. We provide questionnaires measuring engagement and group cohesion collected from the annotators, tutors and participants themselves. We also make a range of accompanying data available such as personality tests and behavioural assessments. The dataset and accompanying psychometrics present a rich resource enabling the exploration of a range of downstream tasks across diverse fields including linguistics and artificial intelligence. This could include the automatic detection of student engagement, analysis of group interaction and collaboration in online conversation, and the analysis of conversational behaviours in an online setting.

Casual conversation, where people engage in social talk or phatic communion (Coupland et al., 1992) (i.e., non-task oriented talk) is acknowledged as important in facilitating collaboration among c...

Advancements in real-time rendering technology have continued to develop rapidly over the course of the last decade. Consequently, human likenesses have been represented virtually with increasingly impressive detail. There is evidence that this increased resemblance to real humans has an observable and wide-ranging set of effects on human perception, cognition and action in situations that involve digital characters. Studies that seek to advance the science of synthetic animated people have consistently aimed to measure and quantify changes in perceived emotional content mediated through artificial human likenesses. The present study has been built off of this work. The experiment outlined here was constructed to define and measure responses from human participants towards state-of-the-art photorealistic virtual humans under affected conditions. In particular, we sought evidence for changes in perceptions of human likeness, eeriness and attractiveness that could be observably dependent on conditions of photorealism and character representation.

Perceptions of system competence and communicative ability, termed partner models, play a significant role in speech interface interaction. Yet we do not know what the core dimensions of this concept are. Taking a psycholexical approach, our paper is the first to identify the key dimensions that define partner models in speech agent interaction. Through a repertory grid study (N=21), a review of key subjective questionnaires, an expert review of resulting word pairs and an online study of 356 users of speech interfaces, we identify three key dimensions that make up a users’ partner model: 1) perceptions towards partner competence and dependability; 2) assessment of human-likeness; and 3) a system’s perceived cognitive flexibility. We discuss the implications for partner modelling as a concept, emphasising the importance of salience and the dynamic nature of these perceptions.

Conversational interfaces have risen in popularity as businesses and users adopt a range of conversational agents, including chatbots and voice assistants. Although guidelines have been proposed, there is not yet an established set of usability heuristics to guide and evaluate conversational agent design. In this paper, we propose a set of heuristics for conversational agents adapted from Nielsen’s heuristics and based on expert feedback. We then validate the heuristics through two rounds of evaluations conducted by participants on two conversational agents, one chatbot and one voice-based personal assistant. We find that, when using our heuristics to evaluate both interfaces, evaluators were able to identify more usability issues than when using Nielsen’s heuristics. We propose that our heuristics successfully identify issues related to dialogue content, interaction design, help and guidance, human-like characteristics, and data privacy.

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”.

Designers commonly use gamification to improve the frequency of engagement with apps, but often fail to consider the impact of placement on reward value. As rewards tend to depreciate if delayed (termed temporal discounting), placing a reward further into the future can significantly affect its ability to motivate behaviour. We examine the most effective placement of gamified rewards so as to reduce discounting and to increase the frequency an application is used. In two online studies, users were asked to choose between fictional budget tracking applications that varied in the placement of either monetary (N=70) or gamified (N=70) rewards. In both experiments we found that people more frequently used the application that provided rewards before, rather than after, the task. As predicted by temporal discounting, our work suggests that placing rewards early in the interaction sequence leads to an improvement in the perceived value of that reward, motivating further selection. We discuss the findings in the context of designing effective reward structures to encourage more frequent app engagement.

In this paper, we tested 20 Brazilian Portuguese speakers at intermediate and advanced English proficiency levels to investigate the influence of Google Translate’s MT system on the mental processing of English as a second language. To this end, we employed a syntactic priming experimental paradigm using a pretest-priming design which allowed us to compare participants’ linguistic behaviour before and after a translation task using Google Translate. Results show that, after performing a translation task with Google Translate, participants more frequently described images in English using the syntactic alternative previously seen in the output of Google Translate, compared to the translation task with no prior influence of the MT output. Results also show that this syntactic priming effect is modulated by English proficiency levels.

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production.

Integrated into smartphones or smart speakers, Intelligent Personal Assistants (IPAs) have grown into the most prevalent example of speech-based interfaces today. Enabling hands-free completion of tasks such as checking the weather, playing music, or controlling smart devices, IPAs have become a central feature of millions of homes. Yet, end-users are faced with barriers to understanding their `black box' devices, and a lack of opportunity to engage in end-user development (EUD) activities. Our current work considers the state of existing tools and platforms for development of IPAs, the key challenges to be overcome, and the potential benefits of doing so.

Conversational agents promise conversational interaction but fail to deliver. Efforts often emulate functional rules from human speech, without considering key characteristics that conversation must encapsulate. Given its potential in supporting long-term human-agent relationships, it is paramount that HCI focuses efforts on delivering this promise. We aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semi-structured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasising the long-term dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. People fundamentally questioned the need for bond and common ground in agent communication, shifting to more utilitarian definitions of conversational qualities. Drawing on these findings we discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction.

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.

Abstract Speech interfaces are growing in popularity. Through a review of 99 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in the field of human–computer interaction (HCI). We find that studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes or developed systems. Measuring task and interaction was common, as was using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, design insight, modality comparison, experiences with interactive voice response systems, assistive technology and accessibility, user speech production, using speech technology for development, peoples’ experiences with intelligent personal assistants and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably taking into account technological advancements, the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research. RESEARCH HIGHLIGHTS Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems Questionnaires on usability and user attitudes often used but few were reliable or validated Thematic analysis showed nine primary research topics Challenges identified in theoretical approaches and design guidelines, engaging with technological advances, multiple user and in the wild contexts, critical research mass and barriers to building speech interfaces

Abstract Online consumer reviews are important for people wishing to make purchases online. However, not everyone contributes online reviews. This paper looks at consumer motivations of reviewing and rating behaviour in order to motivate the design of a mobile interface for online reviewing. An interview study found that people tend to contribute reviews and ratings based on their perception of whether they would be helpful or not to others as well as their own personal view of the usefulness of reviews and ratings when buying products. There also seems to be a cost-benefit trade-off that influences people’s decisions to review and rate: people tend to make a decision based on the perceived value of that review or rating to the community against the effort and costs of contributing. A mobile interface was designed that was intended both to reduce the cost of leaving reviews and to increase the perception of the usefulness of the reviews to others. An initial evaluation of this reviewing interface suggests that it could encourage more people to leave reviews.

One potential method of improving the efficiency of human-computer interaction is to display information subliminally. Such information cannot be recalled consciously, but has some impact on the perceiver. However, it is not yet clear whether people can extract meaning from subliminal presentation of information in mobile contexts. We therefore explored subliminal semantic priming on smartphones. This builds on mixed evidence for subliminal priming across HCI in general, and mixed evidence for the effect of subliminal affective priming on smartphones. Our semi-controlled experiment (n=103) investigated subliminal processing of numerical information on smartphones. We found evidence that concealed transfer of information is possible to a very limited extent, but little evidence of a semantic effect. Overall, the impact is effectively negligible for practical applications. We discuss the implications of our results for real-world deployments and outline future research themes as HCI moves beyond mobile.

Digital behaviour change interventions, particularly those using pervasive computing technology, hold great promise in supporting users to change their behaviour. However, most interventions fail to take habitual behaviour into account, limiting their potential impact. This failure is partly driven by a plethora of overlapping behaviour change theories and related strategies that do not consider the role of habits. We critically review the main theories and models used in the research to analyse their application to designing effective habitual behaviour change interventions. We highlight the potential for Dual Process Theory, modern habit theory, and Goal Setting Theory, which together model how users form and break habits, to drive effective digital interventions. We synthesise these theories into an explanatory framework, the Habit Alteration Model, and use it to outline the state of the art. We identify the opportunities and challenges of habit-focused interventions.

Graphical realism and the naturalness of the voice used are important aspects to consider when designing a virtual agent or character.In this work, we evaluate how synthetic speech impacts people's perceptions of a rendered virtual character.Using a controlled experiment, we focus on the role that speech, in particular voice expressiveness in the form of personality, has on the assessment of voice level and character level perceptions.We found that people rated a real human voice as more expressive, understandable and likeable than the expressive synthetic voice we developed.Contrary to our expectations, we found that the voices did not have a significant impact on the character level judgments; people in the voice conditions did not significantly vary on their ratings of appeal, credibility, humanlikeness and voice matching the character.The implications this has for character design and how this compares with previous work are discussed.

Intelligent Personal Assistants (IPAs) are widely available on devices such as smartphones. However, most people do not use them regularly. Previous research has studied the experiences of frequent IPA users. Using qualitative methods we explore the experience of infrequent users: people who have tried IPAs, but choose not to use them regularly. Unsurprisingly infrequent users share some of the experiences of frequent users, e.g. frustration at limitations on fully hands-free interaction. Significant points of contrast and previously unidentified concerns also emerge. Cultural norms and social embarrassment take on added significance for infrequent users. Humanness of IPAs sparked comparisons with human assistants, juxtaposing their limitations. Most importantly, significant concerns emerged around privacy, monetization, data permanency and transparency. Drawing on these findings we discuss key challenges, including: designing for interruptability; reconsideration of the human metaphor; issues of trust and data ownership. Addressing these challenges may lead to more widespread IPA use.

Subliminal priming has the potential to influence people's attitudes and behaviour, making them prefer certain choices over others. Yet little research has explored its feasibility on smartphones, even though the global popularity and increasing use of smartphones has spurred interest in mobile behaviour change interventions. This paper addresses technical, ethical and design issues in delivering mobile subliminal priming. We present three explorations of the technique: a technical feasibility study, and two participant studies. A pilot study (n=34) explored subliminal goal priming in-the-wild over 1 week, while a semi-controlled study (n=101) explored the immediate effect of subliminal priming on 3 different types of stimuli. We found that although subliminal priming is technically possible on smartphones, there is limited evidence of impact on changes in how much stimuli are preferred by users, with inconsistent effects across stimuli types. We discuss the implications of our results and directions for future research.

A common observation in dialogue research is that people tend to entrain, or align, linguistically with their interlocutors. This phenomenon offers a potentially important way to shape user behavior in human-computer dialogue interactions but little is known about the mechanisms that underlie it and how they may be affected by interlocutor design. We report a Wizard of Oz study that explored how voice anthropomorphism impacts lexical alignment in speech-based human-computer dialogue. In a referential communication task, speakers showed a very strong tendency to align lexical choices with their interlocutors, whether human or computer, but this tendency was not affected by voice anthropomorphism. These results highlight the robustness of lexical alignment effect in speech based human-computer dialogues, and suggest that these effects may be impervious to at least some design cues. They also suggest that automatic priming may be an influential mechanism in explaining why we align lexically with automated dialogue partners.

The impact of simulated embodied agent emotion has been explored in short-term studies, but no work to date has examined its impact in longer interactions that involve multiple interactions with agents. We present an embodied agent (Rachael) that simulates a health professional and attempts to help people improve their fruit and vegetable consumption. Emotional and unemotional versions of the agent were developed to examine how user perceptions of the agent changed over an intervention period of 49 days and in turn how this influenced fruit and vegetable consumption. Results found that whilst participants consumed more daily portions of fruit and vegetables over the intervention period and reduced their consumption gains post-intervention, there was no significant difference in consumption gains over time between those who interacted with the emotional or unemotional agents. Qualitative feedback, however, highlighted a strong preference for the emotional agent. A novelty effect was also observed where the agents were perceived more positively initially and less so over time.

Although wikis are common in both the workplace and in Higher Education, little research has studied the wiki user experience. Recent literature highlights that users may be anxious about editing wiki content; yet in most of this research this anxiety has not been measured quantitatively. Although computer anxiety metrics exist to measure anxiety towards technology, they lack specificity and relevance to the wiki editing context. This paper reports two studies used to research the validity and reliability of the wiki anxiety inventory-editing (WAI-E), an inventory developed and used to measure anxiety in wiki editing (Study 1) and to explore the factor structure of the WAI-E and the validity and reliability of the resulting subscales (Study 2). Study 1 shows that the WAI-E, when used as a uni-dimensional structure, shows high reliability and validity. The principal component analysis conducted in Study 2 showed that the measure converged on a three-factor solution with factors measuring positive affect, editability anxiety and contribution judgement anxiety. The subscales showed high reliability and validity. It therefore seems that although the validity and reliability of using the WAI-E as a uni-dimensional construct are high, the use of the metric as such hides the true structure and nuances of the concept of wiki anxiety.

With the advent of Web 2.0, the number of IT systems used in university courses is growing. Yet research consistently shows that a significant proportion of students are anxious about computer use. The quality of first experience with computers has been consistently mentioned as a significant contributor to anxiety onset. However the effect of users’ first experience on system related anxiety has not to the authors’ knowledge been researched using controlled experiments. Indeed little experiment based research has been conducted on the wiki user experience, specifically users’ evaluations and emotional reactions towards editing. This research uses usability engineering principles to engineer four different wiki experiences for novice wiki users and measures the effect each has on usability, anxiety during editing and on anxiety about future wiki editing. Each experience varied in the type of training spaces available before completing six live wiki editing tasks. We found that anxiety experienced by users was not related to computer anxiety but was wiki specific. Users in the in-built tutorial conditions also rated the usability of the editing interface higher than users in the non-tutorial conditions. The tutorial conditions also led to a significant reduction in wiki anxiety during interaction but did not significantly affect future editing anxiety. The findings suggest that the use of an in-built tutorial reduces emotional and technological barriers to wiki editing and that controlled experiments can help in discovering how aspects of the system experience can be designed to affect usability and anxiety towards editing wikis.