Year: 2025
Type: dissertation
Abstract: This bachelor’s thesis explores how everyday users, specifically non-experts in higher education, interact with AI-based chatbots through natural language prompts. While prior research has focused on improving model performance and understanding prompt mechanics, little attention has been given to how users without technical expertise experience and shape these interactions. The study aimed to explore how different ways of phrasing prompts influence chatbot responses and user perceptions, ultimately generating actionable recommendations to enhance the prompting experience. Using a qualitative research design, data were collected from six university students through a workshop and interviews. Thematic analysis revealed six key findings highlighting that prompt formulation is not a static act but a dynamic, iterative process. Users engaged in trial-and-error, adjusted tone and detail, assigned roles to the chatbot, and applied emotional or stylistic framing to guide responses. Prompting emerged as a form of active sensemaking and problem-solving, where users learned to refine inputs to achieve better outcomes. Drawing on Sensemaking theory, the study concludes that users are not passive recipients but active co-creators in AI interactions. The bachelor’s thesis provides practical recommendations such as encouraging iterative prompting, meta-prompting, emotional framing, goal alignment, and the use of templates to support more effective chatbot use.
Authors: Brown, Kasper, Elf, Arvid, Henriksson, Edvin
Language: English
Topic: AI in Service Interactions
Subfield: Marketing
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7010444106