Putting AI to the Test: Evidence from a Large-Scale RCT in Rural China

Yue Ma, Tianli Feng, Robert W. Fairlie, Chengfang Liu, Prashant Loyalka, Scott Rozelle and Xinwu Zhang

No 12837, CESifo Working Paper Series from CESifo

Abstract: The emergence of artificial intelligence (AI) has heightened interest in personalizing computer assisted learning (CAL) programs to tailor their instruction to individual students. Despite the proliferation of AI-driven CAL programs, evidence for their effectiveness remains limited. We present findings from a large-scale field experiment in rural China examining whether an AI-driven personalized CAL program improves student achievement. We randomly assign 8,647 students from 315 primary school classes to one of three treatment arms: (i) AI-CAL, (ii) non-personalized CAL (active control), and (iii) non-CAL educational activities (pure control). Results indicate that AI-CAL does not significantly improve achievement, with estimates precise enough to rule out non-trivial positive effects. This finding holds across the difficulty of assessment items and across the baseline achievement of students. The finding that R-CAL modestly benefits students in the middle ability tercile while AI-CAL shows no impact on students in any ability tercile suggests caution when projecting the promise of scaling adaptive AI-driven educational technologies in under-resourced settings.

Keywords: AI; education technology; computer assisted learning; adaptive learning; ICT; RCT; rural China (search for similar items in EconPapers)
JEL-codes: I21 O15 (search for similar items in EconPapers)
Date: 2026
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