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.
more »
« less
This content will become publicly available on March 7, 2025
Integrating Natural Language Processing in Middle School Science Classrooms: An Experience Report
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.
more »
« less
- PAR ID:
- 10496815
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE)
- ISBN:
- 9798400704239
- Page Range / eLocation ID:
- 639 - 645
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Artificial Intelligence (AI) and Natural Language Processing (NLP) have become increasingly relevant across multiple fields, creating a necessity for young learners to understand these concepts. However, resources enabling learners to apply AI and NLP, particularly in middle school science, remain limited. To address this gap, we present the early development of NLP4Science, an interactive visualization application facilitating the integration of NLP concepts such as sentiment analysis and keyword extraction into middle school science. We adopted an iterative co-design process starting with a professional development workshop with four teachers, followed by a 2-day pilot study with 48 eighth graders, and concluding with a 5-day study involving 50 sixth graders. This poster presents an overview of NLP4Science, highlighting its key features, and sharing insights gained from the iterative design process, demonstrating the potential of NLP4Science to transform AI and NLP learning within middle school science classrooms.more » « less
-
This research explores a novel human-in-the-loop approach that goes beyond traditional prompt engineering approaches to harness Large Language Models (LLMs) with chain-of-thought prompting for grading middle school students’ short answer formative assessments in science and generating useful feedback. While recent efforts have successfully applied LLMs and generative AI to automatically grade assignments in secondary classrooms, the focus has primarily been on providing scores for mathematical and programming problems with little work targeting the generation of actionable insight from the student responses. This paper addresses these limitations by exploring a human-in-the-loop approach to make the process more intuitive and more effective. By incorporating the expertise of educators, this approach seeks to bridge the gap between automated assessment and meaningful educational support in the context of science education for middle school students. We have conducted a preliminary user study, which suggests that (1) co-created models improve the performance of formative feedback generation, and (2) educator insight can be integrated at multiple steps in the process to inform what goes into the model and what comes out. Our findings suggest that in-context learning and human-in-the-loop approaches may provide a scalable approach to automated grading, where the performance of the automated LLM-based grader continually improves over time, while also providing actionable feedback that can support students’ open-ended science learning.more » « less
-
In the face of the rising prevalence of artificial intelligence (AI) in daily life, there is a need to integrate lessons on AI literacy into K12 settings to equitably engage young adolescents in critical and ethical thinking about AI technologies. This exploratory study reports findings from a teacher professional development project designed to advance teacher AI literacy in preparation for teaching an AI curriculum in their inclusive middle school classrooms. Analysis compares the learning experiences of 30 participating teachers (including Computer Science, Science, Math, English, and Social Studies teachers). Results suggest Science teachers’ understanding of AI concepts, particularly logic structures, is on average higher than their non-Science teacher counterparts. Teacher interviews reveal several thematic differences in Science teachers’ learning from the AI PD as compared to their counterparts, namely learning from reflective discourse with diverse groups. Findings offer insights on the depth and quality of Science teacher AI literacy after participating in an AI teacher PD, with implications for future research in the integration of AI education into Science teachers’ inclusive K12 classrooms.more » « less
-
In the face of the rising prevalence of artificial intelligence (AI) in daily life, there is a need to integrate lessons on AI literacy into K12 settings to equitably engage young adolescents in critical and ethical thinking about AI technologies. This exploratory study reports findings from a teacher professional development project designed to advance teacher AI literacy in preparation for teaching an AI curriculum in their inclusive middle school classrooms. Analysis compares the learning experiences of 30 participating teachers (including Computer Science, Science, Math, English, and Social Studies teachers). Results suggest Science teachers’ understanding of AI concepts, particularly logic structures, is on average higher than their non-Science teacher counterparts. Teacher interviews reveal several thematic differences in Science teachers’ learning from the AI PD as compared to their counterparts, namely learning from reflective discourse with diverse groups. Findings offer insights on the depth and quality of Science teacher AI literacy after participating in an AI teacher PD, with implications for future research in the integration of AI education into Science teachers’ inclusive K12 classrooms.more » « less