Daily Encounters with AI: An Inquiry into Users’ Sensemaking

We live in times of rapid change, where unprecedented and unexpected events unfold with increasing speed and complexity, often disrupting familiar patterns and norms. In the midst of this uncertainty, AI plays a growing role, shaping how we interact, work, and live. To navigate such ambiguity, humans rely on sensemaking —a process of interpreting the unknown, settling on plausible explanations, and adapting their actions accordingly. Despite AI’s growing influence on nearly every aspect of life, studies repeatedly show that users often have a limited understanding of it, leading to misconceptions and unrealistic expectations. This lack of understanding not only results in frustration when AI systems fail to meet users’ needs but also hinders effective interaction and collaboration with these technologies. This dissertation views AI as a socio-technical umbrella term to explore how users make sense of it across three application domains: AI-assisted decisionmaking, AI-mediated social platforms, and agentic AI technologies. Guided by three research questions, it focuses on (1) how users make sense of AI in everyday encounters, (2) the empowerment needs that arise from these interactions, and (3) how design can support sensemaking and foster user agency. Recognizing the importance of context and the situated nature of sensemaking, this work combines various qualitative methods, such as semi-structured interviews, role-playing workshops, and experience sampling. Building on Weick’s sensemaking framework, the findings reveal that sensemaking of AI shares familiar characteristics—it’s enactive, driven by plausibility, inherently social, and often triggered by unexpected events. But AI sensemaking also has unique aspects, such as the influence of AI "folk concepts"—users’ assumptions and expectations about AI shaped by cultural narratives and societal definitions. These perceptions are also influenced by human traits like trust and intelligence and vary depending on stakeholder roles. AI sensemaking not only guides users’ actions but also informs counteractions—how people push back against or adapt to algorithmic systems. To empower users, this research takes a holistic approach, exploring their needs along three dimensions: feeling, knowing, and doing. It highlights how users’ sensemaking is influenced by both episodic power—arising from immediate, specific interactions—and systemic power, which operates at a structural level through institutional norms and opaque algorithmic designs. For designing AI systems that better support users’ sensemaking, the dissertation emphasizes two critical aspects. First, it frames sensemaking as an interactional element, suggesting that AI systems should foster continuous, context-specific engagement by helping users build competencies. Second, it stresses the importance of diversifying user participation in the design process. Involving users as co-creators and knowledge-makers empowers them to engage not just in sensemaking, but also in sense-unmaking, sense-giving, and sense-breaking—creating a more collaborative and inclusive design process that addresses both individual needs and systemic challenges. Finally, this dissertation argues for moving beyond Weick’s retrospective sensemaking framework to apply the concept of prospective sensemaking in the context of AI. This approach emphasizes designing systems that not only help users make sense of past interactions but also enable them to anticipate, adapt to, and shape the uncertainties inherent in their ongoing and future engagements with AI technologies.