Effects of Artificial Intelligence on Educational Functioning: A Review and Meta-Analysis

Burgeoning integration of AI into educational settings could have profound implications for students’ performance. This systematic review and meta-analysis examined the effects of different types of AI and four levels of learning—cognition, knowledge utilization, meta-cognition, and psychological functioning—yielding 228 studies with 464 effect sizes that met criteria for inclusion. AI had large positive effects on cognition, r = 0.530, p < 0.001 [95%CI:0.447 to 0.613] and psychological functioning, r = 0.514, p < 0.001 [95%CI:0.246 to 0.720], moderate effects on knowledge utilization, r = 0.417, p < 0.001 [95%CI:0.305 to 0.747], and small effects on meta-cognition, r = 0.268, p = 0.21 [95%CI:-0.225 to 0.772]. Different types of AI had different effects on cognition, with generative AI demonstrating the largest effects, which were larger than other types of AI (e.g., intelligent tutoring, adaptive/personalized learning). However, different types of AI had comparable moderate effects in bolstering knowledge utilization and psychological functioning. AI had the largest effects on improving learning in arts and humanities. Analyses provided evidence for differential impact of AI on learning across countries with different economic advancement. The findings suggest that AI can be effective at improving learning under certain conditions and that the effectiveness varies with the type of AI.

Duke Scholars

Author Jennifer Lansford Sanford School of Public Policy