interface designers

Abstract: The development of the collaborative critique (CC) usability inspection method was driven by the need for systems, ranging from the simple to the complex, to lessen the workload on their users by being better collaborative partners. In the collaborative view of system-user interaction, the role of the system is expanded to that of a partner that must help its users achieve their goals. This perspective shifts the dynamic from focusing on the user's ability to make effective and efficient use of the system to the system's usefulness within a given task, user, and interaction context, thereby reducing the user's cognitive and physical efforts. The CC method consists of a walkthrough evaluation in which the questions answered at each step of the interaction assess the collaborative strength of the system. This method is further distinguished by the inclusion of questions that access the behavior of the system in error situations, which are known to be particularly burdensome to users of complex systems. In this paper, we provide a detailed description of the CC method and its application and demonstrate its usefulness in evaluating an interface. The results of a laboratory evaluation show that the method performed well in identifying the problems experienced by users across all severity levels and predicted almost all of the problems associated with error situations.

Abstract: With AI automating consequential tasks, human oversight is required for ethical, legal, and practical reasons. Yet these oversight tasks can be demanding and often make for poor work. Empirical evidence on overseers' lived work experiences remains underexplored, yet critical for designing ``good'' oversight. We provide longitudinal, in-the-wild insights through action research accompanying (N=8) tutors who oversaw an AI-supported learning assistant over three months. Our thematic analysis combines insights from twelve weekly meetings and two workshop iterations and was enriched by four repeated questionnaires. We identified stimulation, mastery, and relatedness as key challenges, stemming from the task's repetitiveness, unclear impact of interventions, and limited connections with students and peers. Informed by our initial findings, we deployed targeted UI modifications with mixed success. This paper documents the long-term evolution of the work characteristics of a real-world oversight task and discusses which oversight work characteristics can and cannot be supported by interface design.

Abstract: When we interact with large language models (LLMs), are we having a conversation? They are designed to invite us to treat them as intelligent interlocutors who remember, act, and make commitments. But appearances deceive. We introduce the artifactual stance, a framework that reconceives human-AI interaction as artifact-mediated exchanges of candidate texts. LLM outputs are candidate texts optimized for utility, not utterances bearing meaning or force. LLMs are sophisticated text generators, not speakers. Between sessions, nothing runs; between turns, no one remembers. What persists is a configuration and a transcript. The "conversation" is a user's solo performance, interpretive labour disguised by interface and artifact design. This shift dissolves recent philosophical puzzles. Questions about what 'I' and 'you' refer to in AI exchanges, about whether systems can lie or be held to promises, about the identity of our supposed interlocutors all rest on a false presupposition. There is no speaker behind the screen, hence no one to refer to, no one to hold responsible. What feels like dialogue with someone is interaction with an artifact that generates text at unprecedented scale and fit. By abandoning the conversational framing, we see these systems for what they are: immensely sophisticated artifacts that afford varied uses. The philosophical questions that matter are about the normative underpinnings of design, adoption, authorization, and human practices of use.

Abstract: Assessing human workload for technology-mediated tasks helps prevent task failure caused by poor technology design. Traditionally, workload is assessed retrospectively using the NASA Task Load Index (TLX) after humans complete a task. What if we could forecast workload before a human attempts a task using agent simulation? We introduce Synthetic TLX, a new paradigm for proactive workload estimation that predicts NASA TLX scores for a given task, unlocking novel interaction opportunities and evaluation methods. To understand its viability, we conducted three experiments comparing human and agent-generated scores to evaluate where they align and diverge. We found agent estimates align with human scores particularly when prompted with a human persona and active task simulation. However, agents and humans diverge in the sources of workload they are sensitive to. Based on our findings, we present three applications to showcase Synthetic TLX's potential and discuss the future of workload-aware human-AI interaction.

Abstract: Conversational Task Assistants (CTAs) are multimodal dialogue systems that support users in complex real-world tasks such as cooking and DIY through voice, text, image, and video interactions. Prior user studies have focused on controlled settings, leaving limited understanding of real-world CTA usage at scale. In this work, we present a large-scale study of CTA usage based on thousands of users in-the-wild. Our large-scale real-world data analysis unveils new understandings of (i) user-CTA interaction flows, (ii) user intents, (iii) user conversational traits, and (iv) behavioral factors associated with user satisfaction. Our findings reveal key opportunities for future research in CTAs, particularly in user interaction design and task engagement, concluding with concrete design guidelines.