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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.25800 |
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| _version_ | 1866910089599778816 |
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| author | Juarez, Maddie Rai, Abha Ravi, Kristen E. Delaney, Margaret C. Olweean, Danny Klingensmith, Eric Banerjee, Swarnali Klingensmith, Neil Thiruvathukal, George K. |
| author_facet | Juarez, Maddie Rai, Abha Ravi, Kristen E. Delaney, Margaret C. Olweean, Danny Klingensmith, Eric Banerjee, Swarnali Klingensmith, Neil Thiruvathukal, George K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25800 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HeyFriend Helper: A Conversational AI Web-App for Resource Access Among Low-Income Chicago Residents Juarez, Maddie Rai, Abha Ravi, Kristen E. Delaney, Margaret C. Olweean, Danny Klingensmith, Eric Banerjee, Swarnali Klingensmith, Neil Thiruvathukal, George K. Human-Computer Interaction 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. |
| title | HeyFriend Helper: A Conversational AI Web-App for Resource Access Among Low-Income Chicago Residents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.25800 |