Shark AI uses fossil shark teeth to get middle school kids interested in paleontology and computer vision

Most children have a natural curiosity about sharks – especially their sharp and loyal teeth. Our team had the idea of ​​using this attractive predator attractiveness to teach how scientists used artificial intelligence.

We are researchers in Amnesty International Literacy and STEM Education from Help create a series of lessons that use fossil sharks to show the strength and risk of artificial intelligence.

The curriculum directs middle school students and teachers by building and evaluating computer vision models that can be reliably classified as fossil sharks. Computer seeing is a type of artificial intelligence that uses algorithms and a lot of image data to classify and identify objects. that it The same technology that allows Google lens to Determining plant species In pictures or Self -driving cars To get to know people, cars and bicycles.

our Free penishing curriculum It has five units, which correspond to the standards of national science education and states. These standards determine the main knowledge and skills that students must learn at each class level. Lessons are designed to develop students’ interest in artificial intelligence, data science and excavation science The nature of science.

The general goal of Shark Ai is to show that it should not be the computer world to use, teach or learn artificial intelligence. We believe that all teachers can and they should be ready to teach about artificial intelligence in order to facilitate the integration that is important for technology in K-12 education.

Teaching science with artificial intelligence

Artificial intelligence already Convert our life At an amazing pace.

To help prepare children to live and work in the world of artificial intelligence, it is important for them to know technology in school. most Resources available for teaching artificial intelligence In the classroom K-12 Focus on technology itself. As a result, these lessons may only be presented in specialized classes such as computer science and engineering, which may not be presented to all students in all schools.

The systematic integration of Amnesty International in education is relatively new, a lot Teacher preparation programs I just started merging it. Vocational learning opportunities for teachers who are already working in schools are needed to learn about artificial intelligence.

Our research appears Science teachers have a variety of pre -concepts about artificial intelligence. In addition, Many teachers feel anxious About teaching something they have no experience with. Nearly half of the teachers told Adweik In a survey “It is not comfortable with artificial intelligence technology”, indicating that they are unlikely to add artificial intelligence lessons to its already packed approach.

Shark teeth sorting

To break this unfamiliar, the curriculum begins by introducing different types of artificial intelligence, such as processing natural language, identifying automatic speech and computer vision. Students then work with fossil groups that contain 15 real fossil sharks and three -dimensional megaloid teeth. Migalodon was shark She wandered in water starting from 20 million years and is now extinct. Students sort in the teeth in any way they want – such as size, color or shape. After that, they learn how scientists usually classify fossils and practice tooth sorting with sharks and what they eat.

After that, students use Google is a teaching machineFree online tool that uses the powerful Tensorflow.js Automated learning form trained in millions of pictures. This creates their computer vision model to classify fossil sharks. The data they use can be pictures that they take from the real teeth in their collections or images they download from databases such as Smithsonian National Museum for Natural Digital Experimental History or Idigbio.

Models offending the classification of teeth sometimes, which creates an opportunity for teachers to discuss Perceptual vision and restrictionsLike the importance of training artificial intelligence on many high -quality and varied images.

The Shark AI curriculum with students concludes, displaying and discussing their computer vision models.

Through these activities, students learn about the concepts of artificial intelligence, such as the strengths and weaknesses of artificial intelligence compared to human intelligence along with the concepts of excavation science like Fossil Or the information that can be learned about the history of life on Earth by studying excavations.

Teachers have flexibility in modifying the activities, sequences and time they want to spend on the curriculum. This allows them to highlight the aspects that make the needs and goals very educational.

To prepare teachers to use Shark Ai, we host a weekly vocational educational session in which teachers learn how to use artificial intelligence in science. They also practice activities in the curriculum, and leave a lot of time to discuss to remove mystery of technology.

One of the important training components is to create a community for teachers. They have opportunities to record access, brainstorming and explore and repair errors throughout the year.

Shark Ai staff is also frequently fulfilled with teachers individually to provide custom support, usually exploring and repairing errors around Google Teachable Machine. Teachers meet as a group once every few months to build society.

With this support, we found that teachers can change their understanding and beliefs about artificial intelligence, feeling comfortable and willing Teaching science With artificial intelligence methods.

This article has been republished from ConversationAn independent, non -profit news organization brings you facts and trusted analysis to help you understand our complex world. Written by: Christine and Luclicoand Florida University and Pavlo Antoninkoand Florida University

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Christine and Luclico works for the University of Florida and receives funding from NSF.

Pavlo Antonino works at the University of Florida and receives funding from NSF.