CreativeBot: a Creative Storyteller Agent Developed by Leveraging Pre-trained Language Models

Maha Elgarf, Christopher E. Peters

Abstract: In an attempt to nurture children's creativity, we developed a creative conversational agent to be used in a collaborative storytelling context with a child. We presented a novel approach to develop creative Artificial Intelligence (AI). Our approach uses the four creativity measures: fluency, flexi-bility, elaboration and originality in order to generate creative behavior. We analyzed and annotated our previously collected storytelling data sets -collected with children- according to our four creativity measures. We then used the extracted and annotated data (636 statements) in order to fine-tune two pre-trained language models (Open AI GPT-3). The two models were aimed at generating creative versus non-creative behavior in a collaborative storytelling scenario. We developed the two models to be able to assess the results and compare them together. We conducted an evaluation to assess stories generated collaboratively between a human and both agents separately (n = 26). Adult Users rated the creativity of the agent according to the stories generated. Results showed that the creative agent was perceived as significantly more creative than the non-creative agent. With the experiment results confirming the validity of our system, we may therefore proceed with testing the effects of the creative behavior of the agent on children's creativity skills.