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In 1956, John McCarthy, a computer scientist at Stanford University, coined the term artificial intelligence (AI). He defined AI as the science and engineering of making intelligent machines, especially intelligent computer programs. Intelligence refers to the computational part of the ability to achieve goals in the world.
Computers may develop intelligence by simulating human intelligence or by studying the problems the world presents to intelligence and involving much more computing than people can perform. There are numerous real-world applications of AI today, including speech recognition (e.g., Siri) and virtual customer service (e.g., messaging bots on e-commerce sites with virtual agents and messaging apps, such as Slack and Facebook Messenger).
AI is already being used in K-12 education. Here are five examples of AI applications.
Intelligent tutoring systems (ITS)
ITS are computer programs that model learners’ psychological states to provide individualized instruction. Since the first appearance of ITS in 1970, students around the world have used ITS to learn a variety of subjects, including foreign languages, math, geography, and science. Research suggests that ITS are relatively effective tools for learning.
Automated essay scoring (AES)
AES is one of the most mature applications of AI in education. In the 1990s, the first commercial AES systems included Intellimetric, developed by Vantage Learning, and the e-rater engine developed by the Educational Testing Service (ETS). The primary motivation for developing AES applications was the need to score student writing. AES is increasingly used in K-12 education, as state standards for student knowledge have placed more emphasis on writing and communication. Some AES systems are designed to provide holistic scores, while others provide both scores and feedback on individual aspects of the writing, such as grammar, style, and mechanics.
While many AES systems have been proven to perform similarly to human scorers on standard writing tasks, research did show that it is possible to fool some AES systems to generate high scores with nonsensical writing. AES cannot replace the quality feedback provided by a good writing teacher who has the time to critique a student’s writing carefully and thoughtfully.
Early warning systems (EWS)
According to the U.S. Department of Education, an EWS is “a system based on student data to identify students who exhibit behavior or academic performance that puts them at risk of dropping out of school.” To help pinpoint student achievement patterns and school climate issues that may contribute to students dropping out of school, educators created EWS. They were taken from readily available student data to identify students at risk of missing key educational milestones, to diagnose the needs of at-risk students, and to identify interventions that may help at-risk students to graduate. A 2016 Department of Education report estimated that slightly more than half of public high schools in the U.S. had implemented such early warning systems.
An AI-driven EWS is often trained on digital data archived by districts from prior groups of students. The computer program allows machine-learning algorithms—the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed—to analyze student data and determine the most-relevant indicators and their weights in the model. One study found that for 10th-grade students, 75% of those with the highest risk scores estimated by the machine-learning model did not graduate on time.
Today’s schools have tons of data. School leaders need to make sound policies based on data and by using data in a holistic and coherent way. “The goal of AI in education is not to reinvent K-12, but rather to provide additional tools and resources to make the best recommendations and find the best pathways for students,” says PowerSchool, an organization that provides cloud-based software to connect students, teachers, administrators, and parents with the shared goal of improving student outcomes.
Using AI to assist overall district management includes both large and small areas. There has been an increase in chatbots—powered by natural-language processing—where students and parents can easily get a remedy or solution for their queries on-the-spot. The largest growth in AI in school management will be seen in technology with predictive capabilities that can transform information into actionable data.
Nearly all teens in 2022 have access to a smartphone, according to the Pew Research Center. By 2025, 97 million new roles will be created as humans, machines, and algorithms increasingly work together, according to the World Economic Forum.
In 2018, the Association for the Advancement of Artificial Intelligence and the Computer Science Teachers Association launched a joint initiative to develop national guidelines for teaching AI in K-12. In 2019, the Massachusetts Institute of Technology (MIT) developed an AI + ethics curriculum for middle school students aiming to bring awareness of the technology to the sector of the population which is growing surrounded by AI. In 2021, leaders in K-12 computer science education from 27 states and three districts/territories met at a virtual workshop to develop plans for introducing AI into their curricula.
Jinghong Cai is the senior research analyst at NSBA’s Center for Public Education.
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