HeyFriend Helper: A Conversational AI Web-App for Resource Access Among Low-Income Chicago Residents

2026-03-26

AI Summary

This work addresses the persistent barriers faced by low-income individuals in job seeking—namely limited digital literacy, lack of resume feedback and interview practice, and fragmented access to supportive resources. To bridge these gaps, we developed a conversational AI–powered web platform tailored for low-income residents of Chicago, integrating resume construction, mock interviews, mental health support, labor market insights, and localized service navigation. The system innovatively combines a dialog-based user interface, natural language interaction, geolocation awareness, and multimodal recommendation techniques to enable, for the first time, an interactive and multidimensional integration of localized employment resources. Empirical evaluation demonstrates that the platform significantly enhances users’ career preparedness and efficiency in accessing relevant services, offering a scalable and accessible solution for comprehensive employment support among underserved populations.

Abstract

Low-income individuals can face multiple challenges in their ability to seek employment. Barriers to employment often include limited access to digital literacy resources, training, interview preparation and resume feedback. Prior work has largely focused on targeted social service or healthcare applications that address needs individually, with little emphasis on conversational AI-driven systems that integrate multiple localized digital resources to provide comprehensive support. This work presents HeyFriend Helper, a web-based platform designed to support low-income residents in Chicago through an interactive conversational assistant that provides personalized support and guidance. HeyFriend Helper integrates multiple tools, including resume building and feedback, interview practice, mindfulness and well-being resources, employment trend and career outcome information, language learning support, and location-based access to community services. This work represents an interdisciplinary collaboration between social work, computer science, and engineering that addresses the multifaceted needs of low-income individuals. The findings demonstrate the importance of career-readiness tools and conversational user interface (CUIs) in providing holistic support.

Problem

• employment barriers
• low-income individuals
• digital literacy
• resource access
• career readiness

Innovation

• Conversational AI
• Digital Resource Integration
• Career Readiness
• Conversational User Interface (CUI)
• Low-Income Support