The Speculative Future of Conversational AI for Neurocognitive Disorder Screening: a Multi-Stakeholder Perspective
2026-04-10 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how conversational AI (CAI) could help screen for neurocognitive disorders (NCDs) like Alzheimer's in a way that people find comfortable and engaging. They interviewed doctors, at-risk individuals, and caregivers to understand different views on using CAI for these screenings at home or in the community. They found some common hopes, such as reducing stress, but also tensions, like users wanting emotional support while clinicians prefer standardized tests. The authors also compared current manual screening methods with the potential CAI approach and suggested design ideas to make CAI tools more user-friendly and effective.
Neurocognitive disordersAlzheimer's diseaseConversational AINCD screeningUser-centered designClinician perspectivesEmotional supportMedical consultationHome-based screeningCaregiver involvement
Authors
Jiaxiong Hu, Ruowen Niu, Qiuxin Du, Chenzhuo Xiang, Yirui Zuo, Jihong Jeung, Xiaojuan Ma
Abstract
Neurocognitive disorders (NCDs), such as Alzheimer's disease, are globally prevalent and require scalable screening methods for proactive management. Prior research has explored the potential of technologies like conversational AI (CAI) to administer NCD screening tests. However, challenges remain in designing CAI-based solutions that make routine NCD screening socially acceptable, engaging, and capable of encouraging early medical consultation. In this study, we conducted interviews with 36 participants, including clinicians, individuals at risk of NCDs, and their caregivers, to explore the speculative future of adopting CAI for NCD screening. Our findings reveal shared expectations, such as deploying CAI in home or community settings to reduce social stress. Nonetheless, conflicts emerged among stakeholders, for example, users' need for emotional support may conflict with clinicians' preference for CAI's professional and standardized administration. Then, we look into the user journey of NCD screening based on the current practice of manual screening and the expected CAI-supported screening. Finally, leveraging the human-centered approach, we provide actionable implications for future CAI design in NCD screening.