Presentation
36. Exploring Learning Paths: Understanding the Learning Strategies of Artificial Intelligence System Users and Their Involvement in Social Forums
SessionPoster Session 2
DescriptionAlthough AI and automation has advanced substantially, systems remain plagued with errors and unpredictable behavior. This is especially likely to impact inexperienced users, who are unlikely to be able to predict or avoid problems. Linja et al (2021) found that users of the Tesla Full Self-Driving (FSD) system often turn to social forums to provide previews of problems they may experience, explanations and confirmation of problems, and workarounds. Such social forums have evolved into active platforms with enthusiastic communities that replace or augment limited vendor training. The present study characterizes the processes and sources by which FSD users learn about the technology. The research used a semi-structured technique to investigate how and what users learned from others about the operation of the system. Results uncover several modes and sources of social learning of FSD users, such as acquaintances and social media, and reveal further advantages derived from these social platforms.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Aging
Augmented Cognition
Children's Issues
Communications
Cybersecurity
Education
Environmental Design
General Sessions
Human AI Robot Teaming (AI)
Macroergonomics
Occupational Ergonomics
Student Forum
Surface Transportation
Sustainability
System Development