Are we doing our best for the machines?

The artificial intelligence (AI) and machine learning (ML) revolution is long overdue. Since the mid-1980s, scholarly journals have predicted the widespread adoption of AI in education. However, the momentum is accelerating.

Just four years ago, a study predicted that AI in education and learning would increase by 47.5% by 2021; the prediction turned out to be conservative.

The current landscape

AI and ML are used at every stage of the student and teacher journey to:

• Build statistical models of student knowledge, assess student achievement and instructor skills
• Streamline recruitment and reduce unconscious bias
• Create a digital “paper trail” for auditing purposes
• Organize and optimize learning materials and continuously update them based on student and instructor feedback
• Create optical systems that can automatically grade student work with a cellphone photo
• Evolve to AI-powered speech recognition systems that can help detect reading problems
• Create scheduling algorithms that can help determine optimal learning times for students and subjects
• Build grading systems that quickly aggregate assessment data and reduce response time to student needs
• Create rule-based tutoring systems that “learn” from student mistakes and teacher corrections

This is all in addition to larger district-wide evaluation and application.

Are the machines taking over?

To many, it sounds like a technology that succeeds in educating and preparing children; for others, it may seem like the machines are taking over.