BOARD # 396: NSF AISL: Incorporating Linear Algebra in An AI LiteracyCurriculum in Informal and Formal Learning Settings

Ping Wang; Shichun Huang

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: NSF Grantees Poster Session II
Tagged Topic: NSF Grantees Poster Session
Page Count: 4
Permanent URL: https://peer.asee.org/55770

Paper Authors

biography

Ping Wang The University of Tennessee, Knoxville

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Ping Wang is a research associate in the Earth and Planetary Sciences Department at the University of Tennessee, Knoxville. Her research focuses on AI literacy, AI for science, AI for education, and applying AI in earth and planetary sciences.

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biography

Shichun Huang The University of Tennessee, Knoxville

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Shichun Huang is the Gerald D. Sisk Associate Professor of Petrology in the Department of Earth, Environmental, and Planetary Sciences at the University of Tennessee, Knoxville. He uses elemental and isotopic tracers, together with petrology and mineralogy, to study the Earth’s mantle and the early Solar System.

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Abstract

This abstract presents the preliminary findings of Planet+AI, an informal learning experience aimed at teaching AI concepts in the context of elemental fingerprinting to high school-aged youths and the public at large. With support from a previous NASA grant and the current NSF AISL grant (2023-2026), our research team iteratively develops and tests a sequence of interdisciplinary, Python-based and application-focused lessons through a Community of Practice. Participating students are involved in inquiry-driven, open-ended research projects by collecting drinking water samples, planning and implementing analysis and data interpretation by using the UTK ICP-MS. The dataset(s) generated for drinking water samples, the elemental composition datasets of Apollo lunar rocks, and petrographic images of Apollo lunar thin sections are integrated into a Public AI literacy curriculum.

The objectives are (1) to create an interdisciplinary learning experience, integrating basic Linear Algebra, basic Python programming language, the ICP-MS, planetary data, elemental fingerprinting with basic AI concepts, (2) to introduce computational exercises, data reduction techniques, and AI techniques that solve real-world problems, emphasizing the importance in connecting fundamental and applied concepts in elemental composition data, information, knowledge, and AI, and (3) to incorporate open-ended research activities that motivate students to learn math, programming, planetary data, and AI. The research uses a mixed-methods design, combining quantitative surveys with qualitative written reflections and class observations.

The implementation guidelines adopted in this three-year project include (1) taking advantage of modern technologies, including YouTube videos and Colab notebooks, when introducing new concepts to assist learning, (2) incorporating public webinars and lecture series so that students benefit from both organized learning activities but also informal learning, (3) building strong integration of math, programming, elemental fingerprinting, and AI, (4) incorporating lab activities and research activities which motivate students and also enhance students’ STEM learning, and (5) focusing on developing student’s agile mindset, ability to adapt and improvise, through tinkering.

The 3-week Planet+AI summer program was implemented during the 2024 summer and 47 students participated in the program. PD workshops will be provided, and we anticipate high school teachers will incorporate (part of) our lessons in their high-school classrooms. Preliminary findings from observations of student performance and feedback suggest that: (1) students welcome the opportunity to learn linear algebra, Python programming, and AI with planetary data by using Google Colab notebooks, (2) students appreciate the practical relevance of linear algebra which helps understand data reduction, neural networks and deep learning in AI, (3) students appreciate the ICP-MS for drinking water samples, planetary data, and elemental fingerprinting research, and (4) students welcome the opportunity to learn emerging technologies, particularly, using emerging AI techniques to solve real-world problems.

Citation
Format

Wang, P., & Huang, S. (2025, June), BOARD # 396: NSF AISL: Incorporating Linear Algebra in An AI LiteracyCurriculum in Informal and Formal Learning Settings Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55770

TY  - CPAPER
AB  - This abstract presents the preliminary findings of Planet+AI, an informal learning experience aimed at teaching AI concepts in the context of elemental fingerprinting to high school-aged youths and the public at large. With support from a previous NASA grant and the current NSF AISL grant (2023-2026), our research team iteratively develops and tests a sequence of interdisciplinary, Python-based and application-focused lessons through a Community of Practice. Participating students are involved in inquiry-driven, open-ended research projects by collecting drinking water samples, planning and implementing analysis and data interpretation by using the UTK ICP-MS. The dataset(s) generated for drinking water samples, the elemental composition datasets of Apollo lunar rocks, and petrographic images of Apollo lunar thin sections are integrated into a Public AI literacy curriculum. 

The objectives are (1) to create an interdisciplinary learning experience, integrating basic Linear Algebra, basic Python programming language, the ICP-MS, planetary data, elemental fingerprinting with basic AI concepts, (2) to introduce computational exercises, data reduction techniques, and AI techniques that solve real-world problems, emphasizing the importance in connecting fundamental and applied concepts in elemental composition data, information, knowledge, and AI, and (3) to incorporate open-ended research activities that motivate students to learn math, programming, planetary data, and AI. The research uses a mixed-methods design, combining quantitative surveys with qualitative written reflections and class observations.

The implementation guidelines adopted in this three-year project include (1) taking advantage of modern technologies, including YouTube videos and Colab notebooks, when introducing new concepts to assist learning, (2) incorporating public webinars and lecture series so that students benefit from both organized learning activities but also informal learning, (3) building strong integration of math, programming, elemental fingerprinting, and AI, (4) incorporating lab activities and research activities which motivate students and also enhance students’ STEM learning, and (5) focusing on developing student’s agile mindset, ability to adapt and improvise, through tinkering.

The 3-week Planet+AI summer program was implemented during the 2024 summer and 47 students participated in the program. PD workshops will be provided, and we anticipate high school teachers will incorporate (part of) our lessons in their high-school classrooms. Preliminary findings from observations of student performance and feedback suggest that: (1) students welcome the opportunity to learn linear algebra, Python programming, and AI with planetary data by using Google Colab notebooks, (2) students appreciate the practical relevance of linear algebra which helps understand data reduction, neural networks and deep learning in AI, (3) students appreciate the ICP-MS for drinking water samples, planetary data, and elemental fingerprinting research, and (4) students welcome the opportunity to learn emerging technologies, particularly, using emerging AI techniques to solve real-world problems.
AU  - Ping Wang
AU  - Shichun Huang
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD # 396: NSF AISL: Incorporating Linear Algebra in An AI LiteracyCurriculum in Informal and Formal Learning Settings
UR  - https://peer.asee.org/55770
ER  -