Sycophancy, the tendency of LLM-based chatbots to express excessive agreement with their users, even when inappropriate, is emerging as a significant risk in human-AI interactions. However, the extent to which this affects human-LLM collaboration in complex problem-solving tasks is not well quantified, especially among novices who are prone to misconceptions. We created two LLM chatbots, one with high sycophancy and one with low sycophancy, and conducted a within-subjects experiment (n = 24) in the context of debugging machine learning models to investigate the effect of sycophancy on users’ mental models, workflows, reliance behaviors, and perceptions of the chatbots. Our findings show that users of the high sycophancy chatbot were less likely to correct their misconceptions and spent more time over-relying on unhelpful LLM responses, leading them to significantly worse performance in the task. Despite these impaired outcomes, a majority of users were unable to detect the presence of excessive sycophancy.

Digital self-control tools (DSCTs) help people control their time and attention on digital devices, using interventions like distraction blocking or usage tracking. Most studies of DSCTs’ effectiveness have focused on whether a single intervention reduces time spent on a single device. In reality, people may require combinations of DSCTs to achieve more subjective goals across multiple devices. We studied how DSCTs can address individual needs of university students (n = 280), using a workshop where students reflect on their goals before exploring relevant tools. At 1-3 month follow-ups, 95% of respondents still used at least one type of DSCT, typically applied across multiple devices, and there was substantial variation in the tool combinations chosen. We observed a large increase in self-reported digital self-control, suggesting that providing a space to articulate goals and self-select appropriate DSCTs is a powerful way to support people who struggle to self-regulate digital device use.

Online social platforms afford users vast digital spaces to share and discuss current events. However, scholars have concerns both over their role in segregating information exchange into ideological echo chambers, and over evidence that these echo chambers are nonetheless over-stated. In this work, we investigate news-sharing patterns across the entirety of Reddit and find that the platform appears polarized macroscopically, especially in politically right-leaning spaces. On closer examination, however, we observe that the majority of this effect originates from small, hyper-partisan segments of the platform accounting for a minority of news shared. We further map the temporal evolution of polarized news sharing and uncover evidence that, in addition to having grown drastically over time, polarization in hyper-partisan communities also began much earlier than 2016 and is resistant to Reddit's largest moderation event. Our results therefore suggest that social polarized news sharing runs narrow but deep online. Rather than being guided by the general prevalence or absence of echo chambers, we argue that platform policies are better served by measuring and targeting the communities in which ideological segregation is strongest.

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.