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BEGIN:VEVENT
DTSTAMP:20261012T210903Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST135@linklings.com
SUMMARY:Usability of Artificial Intelligence Systems in Mental Health Sett
 ings
DESCRIPTION:Joshua Frankenfield (Embry-Riddle Aeronautical University) and
  Tiatyana Seabrooks and Briana Sobel (Embry Riddle Aeronautical University
 )\n\nThe growing use of AI systems as mental health aids has gained media 
 attention, often accompanied by concern over potential harms, including li
 nks to extreme outcomes. These risks highlight the need to improve AI safe
 ty while maintaining usability, especially for developers aiming to create
  effective tools. However, limited research has examined how usability is 
 influenced by an AI system’s perceived helpfulness in mental health contex
 ts. This study aims to address that gap by exploring the relationship betw
 een counseling quality and usability. Existing research has focused more o
 n clinician-AI interaction or user satisfaction rather than overall usabil
 ity. Prior findings suggest AI can provide general guidance but may lack d
 epth, empathy, and contextual understanding, limiting its effectiveness. T
 his study uses a custom GPT to generate transcripts of “helpful” and “unhe
 lpful” AI counseling interactions across common student concerns. Helpful 
 models emphasize psychoeducation, transparency, and cognitive reframing, w
 hile unhelpful models provide vague, overly agreeable responses. Participa
 nts evaluate these interactions based on counseling quality, usability, an
 d personal reactions. The findings aim to demonstrate that interaction qua
 lity directly influences usability perceptions. This research can guide de
 velopers and researchers in designing more effective, user-centered AI men
 tal health systems.\n\n
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