AI-Powered Release Notes : Turning System Updates into Learning Moments

Sammanfattning

Release notes (RN) are documents distributed alongside software updates, summarizing new features, bug fixes, and other changes important for users to understand how their software has evolved. This thesis investigates the potential of artificial intelligence (AI) to give users a better experience with RN, with the help of personalized learning (PL) methodologies. RN are critical for users to understand updates, but previous studies show that traditional delivery messages are difficult to understand, too detailed or contain irrelevant information. In collaboration with Scania, a prototype AI-chat solution was developed to address these issues, providing customized, interactive, and user-specific information on software updates. User testing of the prototype was conducted and analyzed using thematic analysis, to answer the research questions. The results showed that conversational AI enhanced user engagement and created a greater learning opportunity. Key findings highlight the preference of concise, context-specific information and flexible delivery formats. Users especially appreciated the ability to get a brief and contextual overview first and then be able to ask follow-up questions. Although the AI solution was perceived positively, concerns were raised about trustworthiness in cases where incorrect information was identified. Another finding was that an AI solution might require more time to use than current systems, but it provides greater value to the users for the invested time. This research demonstrates that integrating AI with PL principles substantially enhances the perceived effectiveness of the RN. This suggests broader applicability of the research results across similar contexts.