Paucity of high-quality causal studies supporting AI use in K-12

A paper released in the spring from Stanford’s SCALE Initiative looked at the research evidence backing AI in grades K-12. Out of 1100 papers in Stanford’s AI Hub Research Repository, only 20 were found to be high-quality studies evaluating the impact of AI on student achievement or on educator performance. Patterns emerging from these 20 papers included that AI was most effective for students during math practice, writing tasks, and programming; that AI was most effective for students when providing hints after wrong answers versus simply providing the correct answer; and that AI was most helpful to teachers in terms of helping with lesson preparation and providing insights regarding student performance.

It is important to note the gaps in the research: mainly that there are “no high-quality causal studies of student AI use conducted in U.S. K-12 classrooms,” that most studies look at short-term versus long-term effects of AI, and that there is little research on AI and student equity, wellness, and social development.

As AI tools rapidly enter classrooms, the limited causal evidence base raises important questions. While early findings suggest some promising uses, stronger research is needed to determine what works, for whom, and under what conditions in order to ensure that AI meaningfully improves student learning.

Source:  

• Best Evidence in Brief:  Short summaries of recent research in education.