This interactive lesson teaches students about artificial intelligence, machine learning, and bias.
Using video game-like modules, students will train a virtual robot to clean up the ocean with machine learning techniques.
Teaching Tips
Positives
Students will enjoy this fun and highly engaging resource.
Facts about ocean pollution pop up intermittently as students train the robot.
Additional Prerequisites
This resource is available in many different languages. Users can change the language at the bottom left-hand corner of the screen.
The lesson plan for this resource includes objectives, advice on lesson preparation, and a teaching guide.
Differentiation
Philosophy, ethics, and design classes could use this resource to discuss the way that human bias can influence machine learning.
Computer and technology classes could make a list of ways that artificial intelligence could be used to combat climate change.
Other resources on this topic include this video on the effect of plastic pollution in the ocean, this video on the hidden reservoirs of plastic in the ocean, and this podcast episode on environmental problems within the commercial fishing industry.
Scientist Notes
The world is becoming big data-driven. This resource introduces students to the power of AI technology and how it can be used to analyze environmental problems globally to inform better decision-making for natural resources management and conservation of marine resources. This is an ideal tool for effective policy delivery, and it is recommended for teaching.
Standards
Next Generation Science Standards (NGSS)
ETS1: Engineering Design
MS-ETS1-3 Analyze data from tests to determine similarities and differences among several design solutions to identify the best characteristics of each that can be combined into a new solution to better meet the criteria for success.
MS-ETS1-4 Develop a model to generate data for iterative testing and modification of a proposed object, tool, or process such that an optimal design can be achieved.
3-5-ETS1-3 Plan and carry out fair tests in which variables are controlled and failure points are considered to identify aspects of a model or prototype that can be improved.
