With the increasing prevalence of large language models (LLMs) such as ChatGPT, there is a growing need to integrate natural language processing (NLP) into K-12 education to better prepare young learners for the future AI landscape. NLP, a sub-field of AI that serves as the foundation of LLMs and many advanced AI applications, holds the potential to enrich learning in core subjects in K-12 classrooms. In this experience report, we present our efforts to integrate NLP into science classrooms with 98 middle school students across two US states, aiming to increase students’ experience and engagement with NLP models through textual data analyses and visualizations. We designed learning activities, developed an NLP-based interactive visualization platform, and facilitated classroom learning in close collaboration with middle school science teachers. This experience report aims to contribute to the growing body of work on integrating NLP into K-12 education by providing insights and practical guidelines for practitioners, researchers, and curriculum designers.
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Designing a Collaborative Game-Based Learning Environment for AI-Infused Inquiry Learning in Elementary School Classrooms
Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence, a field of computer science, has with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore how to introduce AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios that enable students to gain experience with AI applied toward solving real-world life-science problems.
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- NSF-PAR ID:
- 10188722
- Date Published:
- Journal Name:
- Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
- Page Range / eLocation ID:
- 566 to 566
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:
how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students ?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members. -
With the increased attention on integrating computer science concepts into K-12 curricula, there has been a growing investment into professional development opportunities that prepare teachers to teach computer science principles. Yet, little research exists on design features of professional development that help teachers gain the computer science content, skills and teaching pedagogy that ultimately make an impact on student learning and participation in the classroom. In this work we present a professional development model for helping K-12 teachers integrate computer science principles across the curriculum in a variety of content areas. We subsequently investigate the ways in which the design features of the model promoted teacher learning of computer science content and pedagogy.more » « less
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As artificial intelligence (AI) technology becomes increasingly pervasive, it is critical that students recognize AI and how it can be used. There is little research exploring learning capabilities of elementary students and the pedagogical supports necessary to facilitate students’ learning. PrimaryAI was created as a 3rd-5th grade AI curriculum that utilizes problem-based and immersive learning within an authentic life science context through four units that cover machine learning, computer vision, AI planning, and AI ethics. The curriculum was implemented by two upper elementary teachers during Spring 2022. Based on pre-test/post-test results, students were able to conceptualize AI concepts related to machine learning and computer vision. Results showed no significant differences based on gender. Teachers indicated the curriculum engaged students and provided teachers with sufficient scaffolding to teach the content in their classrooms. Recommendations for future implementations include greater alignment between the AI and life science concepts, alterations to the immersive problem-based learning environment, and enhanced connections to local animal populations.more » « less
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The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341525.3393981
