Self-Talk with Superhero Zip: Supporting Children’s Socioemotional Learning with Conversational Agents

Large Language Models (LLMs) have the potential to fundamentally change the way people engage in computer programming. Agentbased modeling (ABM) has become ubiquitous in natural and social sciences and education, yet no prior studies have explored the potential of LLMs to assist it. We designed NetLogo Chat to support the learning and practice of NetLogo, a programming language for ABM. To understand how users perceive, use, and need LLM-based interfaces, we interviewed 30 participants from global academia, industry, and graduate schools. Experts reported more perceived benefits than novices and were more inclined to adopt LLMs in their workflow. We found significant differences between experts and novices in their perceptions, behaviors, and needs for human-AI collaboration. We surfaced a knowledge gap between experts and novices as a possible reason for the benefit gap. We identified guidance, personalization, and integration as major needs for LLMbased interfaces to support the programming of ABM.

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Promoting Equitable Learning Outcomes for Underserved Students in Open-Ended Learning Environments

Fonteles,

Akpanoko,

Wisniewski

et al. 2024

Proceedings of the 23rd Annual ACM Interaction Design and Children Conference

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Self-Talk with Superhero Zip: Supporting Children’s Socioemotional Learning with Conversational Agents

Cited by 4 publications

References 42 publications

Parent-Child Joint Media Engagement Within HCI: A Scoping Analysis of the Research Landscape

Parent-Child Joint Media Engagement Within HCI: A Scoping Analysis of the Research Landscape

Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat

Promoting Equitable Learning Outcomes for Underserved Students in Open-Ended Learning Environments

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