Fostering children’s creativity through LLM-driven storytelling with a social robot
Open accessAbstract
Creativity is an important skill that is known to plummet in children when they start school education that limits their freedom of expression and their imagination. On the other hand, research has shown that integrating social robots into educational settings has the potential to maximize children’s learning outcomes. Therefore, our aim in this work was to investigate stimulating children’s creativity through child-robot interactions. We fine-tuned a Large Language Model (LLM) to exhibit creative behavior and non-creative behavior in a robot and conducted two studies with children to evaluate the viability of our methods in fostering children’s creativity skills. We evaluated creativity in terms of four metrics: fluency, flexibility, elaboration, and originality. We first conducted a study as a storytelling interaction between a child and a wizard-ed social robot in one of two conditions: creative versus non-creative with 38 children. We investigated whether interacting with a creative social robot will elicit more creativity from children. However, we did not find a significant effect of the robot’s creativity on children’s creative abilities. Second, in an attempt to increase the possibility for the robot to have an impact on children’s creativity and to increase the fluidity of the interaction, we produced two models that allow a social agent to autonomously engage with a human in a storytelling context in a creative manner and a non-creative manner respectively. Finally, we conducted another study to evaluate our models by deploying them on a social robot and evaluating them with 103 children. Our results show that children who interacted with the creative autonomous robot were more creative than children who interacted with the non-creative autonomous robot in terms of the fluency, the flexibility, and the elaboration aspects of creativity. The results highlight the difference in children’s learning performance when inetracting with a robot operated at different autonomy levels (Wizard of Oz versus autonoumous). Furthermore, they emphasize on the impact of designing adequate robot’s behaviors on children’s corresponding learning gains in child-robot interactions.
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Script and story generation translates children's imagination into performable texts, improving reading fluency, narrative detail, and creative self-efficacy.
“Script and story generation translates children's imagination into performable texts, improving reading fluency, narrative detail, and creative self-efficacy [115,116].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [115]
Section 3.4.5.1 reports that children in the creative-robot conditions gave more elaborate story ideas than those in the non-creative conditions, supporting the narrative-detail part. But its fluency measure is fluency of story ideas, not reading fluency; the paper does not establish improved creative self-efficacy or describe generating performable scripts. The findings concern a robot-assisted storytelling game, not script generation generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics.
“In visual arts, learners employ Midjourney, DALL·E, and related text-to-image tools, repeatedly adjusting prompts to refine visual detail and aesthetics [111-116,132,143]”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [115]
The Background (§2.3; page unavailable) mentions AI-generated drawings and paintings, but does not discuss learners using text-to-image tools. Searches for Midjourney, DALL·E, and iterative prompt refinement found no passage in our copy supporting the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSome students make no substantive revisions after receiving AI feedback.
“This shallow interaction shows up when some students make no substantive revisions after receiving AI feedback [115], or when teacher feedback is taken up more than AI feedback [130].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [115]
The Methodology (§3.2) and Discussion (§4; page numbers unavailable) describe children co-creating stories with a robot, including frustration with some of its story ideas. A search for feedback and subsequent revisions found no passage reporting that students made no substantive revisions after receiving AI feedback. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Jafari, M. K., & Taheri, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2398-6_50
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Scaffolding students-AI dialogue for safe educational interactions
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Large language model attributes and perceived moral agency of artificial moral advisors
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- Authors
- Elgarf, M., Salam, H., & Peters, C.
- Affiliations
- New York University Abu Dhabi (AE)
- KTH Royal Institute of Technology (SE)
- Corresponding author
- Elgarf, Maha
- Venue
- Frontiers in Robotics and AI
- Publisher
- Frontiers Media SA
- Published
- 2024-12-13
- Volume
- 11
- Pages
- 1457429-1457429
- ISSN
- 2296-9144 (ISSN-L)
- PMC
- PMC11671368
- OpenAlex
- W4405383493
- Cited by
- 28
- References
- 78
- Citations by year
- 2026: 15 · 2025: 13
- FWCI
- 13.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Robotics and AI (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Framework Programme
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Keywords
- children's creativity, large language models, social robots, storytelling, child-robot interaction, robot autonomy, Wizard of Oz, creativity metrics, educational robotics
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