How is Machine Learning Changing the World of Education?

Machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize the education sector. While these technologies are poised to transform traditional classroom practices, there are concerns that much of the hype surrounding them may not be accompanied by concrete results. This article explores the various ways in which ML and AI are being applied in education and the potential that they hold for the future.

Promise and Pitfalls: A Historical Perspective

The idea that new technologies will transform education is not new. In the past, technologies like filmstrips and VHS cassettes were heralded as the next big thing in education. Yet, as we are old enough to remember, they ultimately fell short of their promises.

Personalized Learning: A Slippery Concept

One of the most discussed areas of application for ML and AI in education is personalized learning. Personalized learning is an approach in which educational technology is used to provide students with tailored curricular content, based on their individual pace and level of mastery. This concept is often considered "slippery" in its definition, as different observers may have different interpretations.

Traditional education methods, such as Individualized Education Plans (IEPs), can also be considered a form of personalized learning, but they do not necessarily require technological intervention. However, with the advent of advanced ML and AI technologies, the potential for education to become more individually tailored is increasingly promising. Tools such as the Summit Learning platform are paving the way for more personalized and efficient educational experiences.

Personalized Learning with ML/AI: Optimized Instruction

ML/AI-enhanced personalized learning aims to optimize and improve the efficiency of instruction. By utilizing robust systems of formative assessment, ML and AI can help teachers determine the optimal next lesson for each student, saving time and increasing overall student learning. This technology can provide teachers with actionable insights that help them deliver more targeted and effective instruction.

Predictive Analytics: Early Warning Systems in Education

Another area where ML and AI hold significant promise is in predictive analytics. These technologies can help predict student outcomes and identify students at risk of not graduating. For example, my research team at the American Institutes for Research developed an “early warning system” that uses a variety of data points (behavioral and others) to detect students at risk of not graduating. This system is now integrated into a larger suite of ML analytical tools used by school systems.

Data-Driven Education Assessment

Machine learning (ML) is also transforming the way we assess educational performance and outcomes. In the traditional model, a single end-of-year summative assessment was relied upon to measure student ability. However, at Imbellus, we use AI to analyze the difficulty level of our assessments, ensuring that they are fair for all test-takers. This helps in the accurate equating of test scores, ensuring that every student is measured on the same scale.

ML is also used to understand and better predict the psychometric performance of various assessment scenarios, helping to refine and optimize our testing methods. Furthermore, we are working towards an integrated system of formative and interim assessments that uses ML to measure student ability throughout the school year, significantly reducing the need for time-consuming end-of-year summative assessments.

Conclusion: Beyond Hype

While the education sector has seen its share of hype around new technologies, the potential of machine learning and artificial intelligence to transform education is undeniable. From optimizing instructional methods to predicting student outcomes and transforming educational assessments, these technologies are making significant inroads in the field.

However, it is important to approach these developments with a realistic perspective. While the potential is vast, it is also crucial to focus on tangible results and practical applications. As technology continues to evolve, the educational community must work together to ensure that these tools are used effectively and to the benefit of all students.