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Ian Beacock

Publications and source records attributed to Ian Beacock.

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AI usage patterns are shaped by perceived gains in human agency

As conversational AI systems become more deeply integrated into daily life, the implications for human agency are increasingly urgent to understand. AI's potential to amplify capability sits alongside risks of individual and collective disempowerment, yet empirical, ecologically-valid evidence about cumulative usage is scarce. We analyze deep ethnographic data from a study of daily AI chatbot users (n = 51) in the United States, Germany, and Singapore to illuminate conversational AI usage in situated context as a sociotechnical practice. We show that people consistently link sustained AI usage to perceived gains in individual agency. Crucially, these perceived gains often outweigh concerns about accuracy, reliability, and consistency to shape usage patterns. Our findings challenge prevailing assumptions about how and why humans use AI systems over time, suggesting that traditional trust-based models are not sufficient for explaining human behavior with conversational AI. Finally, we expose a critical tension: immediate psychological boosts to perceived agency may not necessarily translate into material effects, structural empowerment, or long-term capacity. Our results help establish a new foundation for novel behavioral frameworks, measurement tools, and AI benchmarks to ensure conversational AI strengthens human agency in substantial, sustained ways.

cs.CY

AI and the Future of Digital Public Squares

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

cs.CY

Practicing Information Sensibility: How Gen Z Engages with Online Information

Assessing the trustworthiness of information online is complicated. Literacy-based paradigms are both widely used to help and widely critiqued. We conducted a study with 35 Gen Zers from across the U.S. to understand how they assess information online. We found that they tended to encounter -- rather than search for -- information, and that those encounters were shaped more by social motivations than by truth-seeking queries. For them, information processing is fundamentally a social practice. Gen Zers interpreted online information together, as aspirational members of social groups. Our participants sought information sensibility: a socially-informed awareness of the value of information encountered online. We outline key challenges they faced and practices they used to make sense of information. Our findings suggest that like their information sensibility practices, solutions and strategies to address misinformation should be embedded in social contexts online.

cs.HC