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When you think of artificial intelligence courses, you probably picture students writing code on a computer. But that’s not where educators should start, says Joseph South, chief learning officer of the International Society for Technology in Education.
Instead, teachers should help students approach decisions the way digital programming might — by working through information, finding patterns, and making a choice.
“At ISTE, we strongly believe that students need to learn how the digital world works,” said South. “These capabilities would be incomplete without an understanding of how these extremely powerful tools work.”
Computers and digital devices are as ubiquitous to today’s students as pencils and pens were to their grandparents. People usually call Siri to send a text message or allow Netflix to choose the next show to watch. Underlying these actions is artificial intelligence, or AI, which uses a system of data analysis and algorithms to help machines make decisions about what a “Star Wars” fan might want to watch based on the information available.
Experts believe that a crucial part of learning how AI technology works is that students should learn about machine learning and start doing it when they are young. As students grow older, the curriculum can then be expanded to include ethical issues such as B. AI bias or how data is collected and applied and who ultimately owns it.
Crucially, however, teachers need to understand the subject themselves in order to help students become fluent on their own.
Using machine learning to solve problems
As you teach students the basics of machine learning and how AI works, Michael Dalley, an associate professor and director of Center for Professional Development and Education Reform at the Warner School of Education at the University of Rochester, suggests that classes should learn how to apply a machine learning approach to problem solving.
In other words, they should learn to think like a computer.
“We know there’s an explosion of big data,” Daley said. “It’s an awareness that we think people should learn in school because their data is shared everywhere. We want them to understand what big data is and how big data is processed.”
This interest is what prompted Daley to start EAGER, a two-year project funded by the National Science Foundation to help build machine learning in K-12 students.
Part of the project is a program that converts data into a visual representation, such as a pictogram of a face. For example, the program can reflect the abundance of beetles in a certain region by making certain facial features more or less prominent in a visual representation. Eyebrows can represent the air temperature in a certain area, while the nose on the figure can show humidity and the ears could represent the number of bugs. These characteristics change with the data: for example, eyebrows may be thicker and ears smaller.
Students can then visualize the data without getting lost in the numbers. You can also drag the pictograms on top of each other to view clusters, e.g. B. Compare the faces with thinner eyebrows representing regions of lower air temperature. Patterns then appear, indicating evidence that can help students create a hypothesis.
“We think this kind of questioning is a new kind of science,” Daley said.
Standards – and laws – are required
South said ISTE started thinking about the rise of AI about five years ago, with a focus on how to support teachers to educate students on the technology and related ethical issues surrounding design for machines.
“We wanted students to participate in activities to help them understand how AI works and what the ethical issues are,” he said. “AI reflects human values and prejudices, and we wanted to expand the number and type of students designing AI.”
ISTE was developed for this purpose a partnership with GM In 2017, it started teaching educators how to teach the subject. To date, more than 1,500 teachers have been trained to think about machine learning — and in turn, they’ve taught over 3 million students, South said.
The program is free and is being launched with high schools in California, Texas and Michigan that serve populations with high levels of poverty or have populations that have historically been underrepresented in STEM subjects.
This approach stems from AI being viewed as a “justice issue,” said Nancye Blair Black, ISTE-GM AI Explorations Project Lead. The program has since expanded into instructional guides that focus on elementary school curricula, secondary schools, computer science, ethics and AI.
“Children, and people in general, deserve to know how their world works and have a say in how their world works,” Black said. “They should know the pros and cons of AI, what works well, what impact it has on society, and how AI can solve problems.”
A challenge — and motivation — for There was a lack of standards when creating an AI curriculum, experts said. While most states have computer science standards, creating computational models and understanding machine learning is a different field altogether.
But it should start with young students and not wait until high school, he said Yigal Rosena former technology and assessment researcher at Harvard University Graduate School of Education who is now developing AI learning tools for BrainPOP.
“Computational thinking, which is a way of thinking, has to start early,” Rosen said. “You need to build those skills from elementary school, rather than waiting until high school.”
New standards and ratings are also slowly appearing on the global stage. An assessment that PISA 2025 Learning in the digital worldwill examine how well prepared students are to use computer logic to solve problems, with results expected by December 2027.
Nations are also taking a close look at how AI is applied and used in everyday life. This is proposed in the EU EU AI Law will apply a scale to the risk of using artificial intelligence in specific industries, including AI scanning job applications or how AI can assign a social score that is then used by a government. The White House also recently released a proposed draft for one AI Bill of Rights to examine the regulation of AI in healthcare, financial services and other sectors.
to Patricia Scanlon, Ireland’s first AI Ambassador and Founder and CEO of SoapBox, a speech recognition tool for children’s voices, underpins this concern about how AI is involved in our lives, the need to ensure students learn at least a conceptual understanding of machine learning. Regardless of whether they end up developing artificial intelligence or are just committed global citizens, knowing how AI works is crucial.
“Not everyone needs to know every detail,” Scanlon said. “The people who will contribute to AI do not all have to be tech-savvy. But you have to know how to rate [AI], and how it is regulated. There are as many aspects as there is a part of our life and children live in this world.”