Having a free-speech conversation with avatars in a virtual environment can be desirable in virtual reality applications, such as virtual therapy and serious games. However, recognizing and processing free speech seems too ambitious to realize with the current technology. As an alternative, pre-scripted conversations with keyword detection can handle a number of goal-oriented situations, as well as some scenarios in which the conversation content is of secondary importance. This is, for example, the case in virtual exposure therapy for the treatment of people with social phobia, where conversation is for exposure and anxiety arousal only. A drawback of pre-scripted dialog is the limited scope of the user's answers. The system cannot handle a user's response that does not match the pre-defined content, other than by providing a default reply. A new method, which uses priming material to restrict the possibility of the user's response, is proposed in this paper to solve this problem. Two studies were conducted to investigate whether people can be guided to mention specific keywords with video and/or picture primings. Study 1 was a two-by-two experiment in which participants (n = 20) were asked to answer a number of open questions. Prior to the session, participants watched priming videos or unrelated videos. During the session, they could see priming pictures or unrelated pictures on a whiteboard behind the person who asked the questions. The results showed that participants tended to mention more keywords both with priming videos and pictures. Study 2 shared the same experimental setting but was carried out in virtual reality instead of in the real world. Participants (n = 20) were asked to answer questions of an avatar when they were exposed to priming material, before and/or during the conversation session. The same results were found: the surrounding media content had a guidance effect. Furthermore, when priming pictures appeared in the environment, people sometimes forgot to mention the content they typically would mention. © 2013 by the Massachusetts Institute of Technology.