Teenagers and Artificial Intelligence: Bootcamp Experience and Lessons Learned
Project Overview
The document explores the implementation of a three-day AI bootcamp for high school students, emphasizing the necessity of incorporating AI education into school curricula. It aimed to equip students with fundamental knowledge in artificial intelligence, including learning agents and AI ethics. Feedback from participants revealed high satisfaction levels and marked enhancements in their comprehension of AI concepts and programming skills. However, challenges emerged regarding the usability of the coding interface and the depth of the content presented. These findings highlight the critical need for more engaging and accessible AI educational resources for teenagers, suggesting that while the bootcamp was effective in improving students' understanding, further refinements are essential to optimize the learning experience and address existing barriers in AI education.
Key Applications
AI Bootcamp for high school students
Context: Cohort of 60 high school students aged 14-19, delivered in summer 2023
Implementation: A three-day bootcamp combining in-person instruction and online platform materials, using diverse modalities like videos, slides, playgrounds, and quizzes.
Outcomes: 91.4% satisfaction rate, 88.5% improved understanding of AI concepts, and 71.4% improved programming skills.
Challenges: Challenges included the use of Google Colab notebooks for coding, which many students found difficult.
Implementation Barriers
Technical Barrier
Students faced challenges using Google Colab notebooks for coding assignments, including running code cells and troubleshooting errors.
Proposed Solutions: Consideration of a more suitable coding interface for teaching AI and programming to teenagers.
Curriculum Design Challenge
The length and depth of the bootcamp were challenging for students, with suggestions to increase the number of days and reduce hours per day.
Proposed Solutions: Future bootcamps may need to adjust the duration and pacing of instruction.
Project Team
Uzay Macar
Researcher
Blake Castleman
Researcher
Noah Mauchly
Researcher
Michael Jiang
Researcher
Asma Aouissi
Researcher
Salma Aouissi
Researcher
Xena Maayah
Researcher
Kaan Erdem
Researcher
Rohith Ravindranath
Researcher
Andrea Clark-Sevilla
Researcher
Ansaf Salleb-Aouissi
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Uzay Macar, Blake Castleman, Noah Mauchly, Michael Jiang, Asma Aouissi, Salma Aouissi, Xena Maayah, Kaan Erdem, Rohith Ravindranath, Andrea Clark-Sevilla, Ansaf Salleb-Aouissi
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai