While ACL is seeking solutions across a broad range of use cases and contexts, below are case examples of the types of AI tools that could fit within the design of the challenge.
Track 1 Case Examples: AI Tools Supporting Caregivers
- Virtual assistants: A voice-activated AI assistant that helps a caregiver manage schedules, medication reminders, and daily care tasks hands-free.
- Enhanced communication tools: Real-time language translation to support communication between caregivers and care recipients who speak different languages, or tools that support nonverbal communication.
- Memory and cognitive support: An app that uses AI to prompt a person with early dementia through daily routines using personalized cues and reminders, reducing the need for caregiver reminders.
- Care coordination and navigation: An AI tool that helps caregivers track appointments, share updates with family members, and communicate with providers in one place.
- Decision support tools: An AI system that helps caregivers decide when to manage a symptom at home versus when to contact a clinician based on observed changes.
- Conversation companions: An AI companion that engages a person with dementia in familiar, personalized conversations based on their life history and interests, helping maintain communication and providing calming interaction during periods of agitation while giving caregivers short breaks.
- Remote monitoring and risk prediction: AI-enabled tools integrated with sensors that track movement and sleep patterns to identify fall risk or health decline and notify caregivers early.
- Robotics and assistive devices: An AI-enabled robotic device that helps with safe transfers or delivers items around the home to reduce physical strain on caregivers.
- Caregiver training and education: An AI-guided training tool that teaches caregivers how to perform care tasks (e.g., wound care, safe lifting) with step-by-step guidance.
- Emotional support tools for caregivers: An AI-based check-in tool that offers stress-management strategies, connects caregivers to peer support, and flags burnout risk.
Track 2 Case Examples: AI Tools to Extend the Workforce
- Matching and scheduling: An AI tool that matches direct care workers to clients based on skills, location, language, transportation, and availability, and automatically adjusts schedules to reduce missed shifts and overtime.
- Documentation and reporting: An AI tool that converts voice notes or care observations into structured documentation, reducing paperwork time for workers and improving data quality.
- Training and upskilling: An AI-enabled training system that provides short, role-specific learning modules and refreshers based on a worker’s experience level and care tasks.
- Real-time coaching and support: A mobile AI assistant that gives direct care workers step-by-step guidance during care tasks (e.g., safe transfers, dementia-related behaviors) and flags when supervisory support may be needed.
- Predicting care needs and risks: An AI system that analyzes visit data and client health indicators to predict increased care needs, fall risk, or hospitalization risk, allowing earlier intervention.
- Remote monitoring and risk prediction: AI-enabled tools integrated with sensors that track movement and sleep patterns to identify fall risk or health decline.
- Data aggregation: Combining data from the home environment with care notes to provide a clearer picture of care needs while reducing manual reporting.
- Workforce management activities: A platform that uses AI to help agencies forecast staffing needs, track turnover risk, and plan coverage during high-demand periods.
- Administrative task reduction: Automated systems that use AI to handle time tracking, billing, compliance reporting, and quality monitoring, allowing workers to focus more time on direct care.