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This thesis explores potential strategies to enhance user control and satisfaction within the movie selection process, with a particular focus on the utilization of conversational generative artificial intelligence, such as ChatGPT, for personalized movie recommendations. The study adopts a qualitative user-centered design thinking approach, aiming to compre-hensively understand user needs, goals, and behavior.

In-depth interviews were conducted, utilizing the "Thinking aloud"method and trigger materials to elicit rich user feedback. Participants interacted with ChatGPT and various prototypes, providing valuable insights into their experiences. The study found that participants felt more in control when given the opportunity to specify wishes. In addition, the users found that the experience of receiving recommendations through ChatGPT was more satisfying than their usual way of receiving recommendations for movies. Furthermore, participants expressed a desire for additional information about recommended movies and more novel suggestions.

The prototypes, designed as triggers for user feedback, were generally well-received, providing an engaging and fun user experience. Despite some participants expressing challenges in specifying movie choices based on an emotion, this new approach to movie selection was viewed positively.

Despite limitations concerning the study’s validity, reliability, and testing situation, the findings suggest the potential of generative artificial intelligence conversational agents in enhancing the movie selection process. It is concluded that iterative design improvements and further research is necessary to fully leverage the potential of natural language processing technologies in recommendation systems. The study serves as a preliminary investigation into improving movie recommendations using generative artificial intelligence and offers valuable insights for future developments.