Design of Junior High School Artificial Intelligence Curriculum Project Based on STEAM

With the rapid development of artificial intelligence technology, integrating it into the field of education has become an important trend in global educational reform. STEAM education, as an integrated educational model of Science, Technology, Engineering, Art, and Mathematics, provides a new perspective for the design and implementation of artificial intelligence courses. This paper, based on artificial intelligence courses and integrating the STEAM educational concept, combines China's excellent traditional culture with artificial intelligence technology to design the interdisciplinary project "Shadow Puppetry Video Generation System Based on Alphapose Multi-person Pose Recognition and Tracking". The project utilizes deep learning technology to track the posture of performers in videos through the Alphapose model and SORT algorithm, extracting their skeletal keypoints into digital signals, and using computer graphics technology to automatically generate shadow puppetry videos from performer videos. Through the teaching practice of the project, it has increased students' interest in learning artificial intelligence, enhanced students' interdisciplinary practical abilities, innovation abilities, cooperation abilities, and problem-solving abilities, and is conducive to cultivating students' core literacy in artificial intelligence.

Authors

Institutions

Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/cste64638.2025.11092287
Primary Topic
Innovative Educational Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design of Junior High School Artificial Intelligence Curriculum Project Based on STEAM

Kangman Li, Jiao Ge, Lei Zeng, Guangyong Zheng et al.
Innovative Educational Techniques
article

Design of Junior High School Artificial Intelligence Curriculum Project Based on STEAM

Kangman Li, Jiao Ge, Lei Zeng, Guangyong Zheng, Yingjie Jiang, Lingcheng Zeng
article en

Abstract

With the rapid development of artificial intelligence technology, integrating it into the field of education has become an important trend in global educational reform. STEAM education, as an integrated educational model of Science, Technology, Engineering, Art, and Mathematics, provides a new perspective for the design and implementation of artificial intelligence courses. This paper, based on artificial intelligence courses and integrating the STEAM educational concept, combines China's excellent traditional culture with artificial intelligence technology to design the interdisciplinary project "Shadow Puppetry Video Generation System Based on Alphapose Multi-person Pose Recognition and Tracking". The project utilizes deep learning technology to track the posture of performers in videos through the Alphapose model and SORT algorithm, extracting their skeletal keypoints into digital signals, and using computer graphics technology to automatically generate shadow puppetry videos from performer videos. Through the teaching practice of the project, it has increased students' interest in learning artificial intelligence, enhanced students' interdisciplinary practical abilities, innovation abilities, cooperation abilities, and problem-solving abilities, and is conducive to cultivating students' core literacy in artificial intelligence.

Middle East Institute (US), Hengyang Normal University (CN), Qidong Liver Cancer Prevention Research (CN), University of South China (CN)
Quality Education
Openalex Percentile: Top 78%
Innovative Educational Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.