In the USA, 22–40% of youth who have been accepted to college do not enroll. Researchers call this phenomenon summer melt, which disproportionately affects students from disadvantaged backgrounds. A major challenge is providing enough mentorship with the limited number of available college counselors. The purpose of this study is to present a case study of a design and user study of a chatbot (Lilo), designed to provide college advising interactions.
This study adopted four primary data sources to capture aspects of user experience: daily diary entries; in-depth, semi-structured interviews; user logs of interactions with the chatbot; and daily user surveys. User study was conducted with nine participants who represent a range of college experiences.
Participants illuminated the types of interactions designs that would be particularly impactful for chatbots for college advising including setting reminders, brokering social connections and prompting deeper introspection that build efficacy and identity toward college-going.
As a growing body of human-computer interaction research delves into the design of chatbots for different social interactions, this study illuminates key design needs for continued work in this domain. The study explores the implications for a specific domain to improve college enrollment: providing college advising to youth.
