Empowering Employees

Considering the rapid development and spread of AI technologies, this work examines the challenges and opportunities of building a basic understanding of AI, especially large language models. The focus is on the question of how the communication implemented in AI demonstrators must be designed to effectively convey knowledge about what AI-based language models are and how they work. Using an experimental online survey that presents participants with an explanatory AI language model demonstrator, the effects of different communication approaches - narrative vs. argument-based communication - on factual knowledge about AI language models are examined.
The results of the pre-post study show a significant improvement in the participants’ level of knowledge, regardless of the communication approach, and underline the role of Involvement (in form of personal relevance) on the topic of AI as a decisive factor for the increase in knowledge. The work makes an important contribution to research by opening the topic as a field of research for communication science and knowledge management and proving that both the direct engagement with AI technologies and the target group-specific adaptation of the communication are essential factors for the successful transfer of AI competencies. Based on these findings, recommendations for optimizing the explanatory communication for AI demonstrators are presented.