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Foundations of Autonomous Vehicles: A Curriculum Model for Developing Competencies in Artificial Intelligence and the Internet of Things for Grades 7–10
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Describes a one-week summer camp curriculum for grades 7–10 that combines AI (computer vision, Teachable Machine) and IoT (ESP32 camera, ultrasonic/infrared sensors) through an Elegoo robot car, piloted with 29 students.
Problem
K-12 students, especially in informal settings, lack structured AI literacy education that integrates AI with IoT; many states have only begun adding AI outcomes to K-12 curricula.
Approach
An experiential, Kolb-based curriculum that uses autonomous robot cars as a vehicle to cover all five big ideas of AI (perception, representation and reasoning, learning, natural interaction, societal impact) while integrating IoT sensors and a supervised image classifier controlling the car, built largely from free tools.
Key results
- Pilot with two cohorts (18 and 11 students, grades 7-10) in central Illinois; 90% of students reported positive feelings about the program at the end of the 5-day workshop.
- Every student group completed its chosen culminating project.
- Robot car kit cost about $55-$80; curriculum apart from the kit built on free platforms and tools.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train the image-classification model using images from the robot car camera instead of the computer camera | AI+IoT autonomous-vehicle curriculum · hand-gesture recognition with the robot car camera | accuracy | Helped |
Surprising findings
- Students' models performed worse on images from the car camera than on computer-camera images, illustrating distribution shift.
Limitations
- Evaluation is limited to a small pilot (29 students) with only a positive-feelings indicator; no learning outcome or survey-based assessment reported.
- Requires an instructor with a computer science background for code modification activities.
- Wi-Fi dependence limits real-time inference speed.
Experimental setup
Datasets: hand gesture images captured by students printed traffic sign images
Metrics: student self-reported positive feelings (90%)
Builds on: Kolb experiential learning model AI4K12 five big ideas Calypso / Cozmo AI education
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