2026-09-21T12:53:42Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127516 2026-02-03 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

Designing AI-driven conversational agents to support older adults’ virtual reality learning and lifelong growth Cheng, Qiyuan 2024-12-11 Gupta, Avinash Ai-driven Conversational Agents Virtual Reality Older Adults Participatory Design Human-ai Interaction Design For Aging Learning Support Technology Adoption eng This research investigates how AI-driven conversational agents (CAs) can support older adults in learning and engaging with virtual reality (VR) technology. Through semi-structured interviews with three participants aged 50+ and a participatory design workshop, we explored the challenges older adults face when learning VR, their specific support needs, and their expectations for AI assistance. Our findings reveal a complex set of challenges in VR learning, including physical barriers, interface complexity, and social-contextual factors. Participants expressed a need for personalized, adaptive support that respects their autonomy while offering consistent guidance. Their expectations for AI-driven CAs emphasized natural interaction, context awareness, and appropriate emotional engagement, while maintaining clear professional boundaries. Based on these insights, we offer design recommendations for AI-driven CAs to effectively support older adults’ VR learning. These include balancing functionality with usability, integrating emotional intelligence with professional boundaries, and ensuring seamless adaptation to diverse social and environmental contexts. This research contributes to the growing field of age-inclusive technology design and provides practical guidelines for developing AI-supported VR learning systems that foster lifelong growth and engagement. 2024-12 Thesis https://hdl.handle.net/2142/127516 Copyright 2024 Qiyuan Cheng application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01 The student, Qiyuan Cheng, accepted the attached license on 2024-12-10 at 14:43. The student, Qiyuan Cheng, submitted this Thesis for approval on 2024-12-10 at 15:44. This Thesis was approved for publication on 2024-12-11 at 16:13. DSpace SAF Submission Ingestion Package generated from Vireo submission #21563 on 2025-03-28 at 14:57:08 Industrial&Enterprise Sys Eng Industrial Engineering University of Illinois at Urbana-Champaign M.S. Thesis