Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States

Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their ability to use digital daily management tools. For example, adding an event to a digital calendar requires multiple steps that may act as barriers to independent use for individuals with AD/ADRD. This paper presents AI-Care, a conversational agentic artificial intelligence (AI) layer built on top of a remote caregiving platform co-designed with people with AD/ADRD. AI-Care is designed to reduce the cognitive load on individuals with AD/ADRD when managing everyday tasks such as setting calendar reminders and organizing to-do lists through natural-language interaction with a voice-first chatbot. The system uses a LangGraph-based stateful orchestration approach in which each request passes through sanitization, intent classification, context loading, safety checks, deterministic slot collection, tool execution, and response composition. Safety-critical responses, particularly around medications and allergies, are grounded in caregiver-verified records rather than free-form model generation. The system does not make autonomous medical or treatment decisions. Incomplete or ambiguous requests are handled through controlled multi-turn clarification rather than silent failure or guessing. The system supports both typed and spoken input, with voice output through ElevenLabs text-to-speech. Longer responses are chunked before synthesis to avoid rushed playback. A preliminary pilot with four individuals with mild-to-moderate AD/ADRD showed that users found the system trustworthy, competent, and likable, and were able to complete the evaluated coordination tasks through conversation. We describe the design goals, system architecture, safety controls, and findings from this formative evaluation.

Artificial intelligence (AI) is increasingly deployed to aid professional judgment, yet its implications for the development of human expertise remain unclear. We examine how the availability of AI assistance during training and task execution shapes novices' individual and collective judgment in the context of cancer diagnosis. Across two field experiments at a large teaching hospital, 576 medical novices learned to diagnose lung cancer from CT scans. Study 1 used a 2 * 2 factorial design that randomized AI assistance during training, task execution, both, or neither. Novices who received AI assistance during training achieved higher diagnostic accuracy in both AI-assisted and unaided task execution, with a larger improvement in the former. However, training-phase assistance was associated with more common errors than task-execution-phase assistance, suggesting that while the trainer effect improves individual accuracy, it limits the benefits of aggregating judgments. Study 2 shows that AI assistance during earlier task execution improved subsequent unaided performance and reduced common errors in the unaided phase of task execution, thus revealing a surprisingly beneficial "tool-as-trainer" effect. These findings suggest that, contingent on the right deployment strategy, AI assistance can support individual expertise development and improve collective judgment by reducing common errors across individuals.

As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through interfaces-that supports usability, trust, and safety. Existing resources offer high-level principles ("be transparent," "maintain user control") or low-level UI patterns, but there is a lack of mid-level design knowledge bridging the two. Through landscape and artifact analysis of 60 commercial AI applications, we introduce a framework defining human-AI coordination as the interplay of three dimensions: salience (how prominently AI is presented), involvement (what users can do to engage AI), and activity (what AI actually does). We contribute mid-level tools including coordination zones (done-for-me, done-under-me, done-with-me, done-without-me), an input taxonomy (prompted, sparked, inferred, layered), coordination curves for mapping user journeys, and design patterns demonstrating the generative capacity of the framework. The framework can be applied generatively to design experiences, analytically to evaluate existing ones, and communicatively to articulate ideas across stakeholders.