Social robots are widely used for educational activities, especially to attract children's attention. As a side effect, pupils' excitement can suppress their focus on the tutors and the content being approached. A potential solution to tackle this issue is to equip the robot with storytelling strategies, which have been growing remarkably in recent years thanks to advances in Large Language Models. However, few studies are still addressing the resulting application in real-world conditions. In this work, we are exploring the GPT-3.5 model for story generation based on content to be approached in maker-space classes. To achieve our goals, we implemented a web application for content insertion that connects to the robot through ROS. The proposal was validated in two phases: a first phase of interviews with 5 tutors of maker-space to present our solution and get their feedback, and two 90-minute sessions with pupils for real-world validation. Results suggested the proposal has high potential for supporting multiple languages and generating suitable stories for diverse contexts. Furthermore, adding social behaviors, as encouragement and sentiment analysis, can help in the students' expectation handling.