Scaling Up Counseling with AI: Evidence from a Nationwide Experiment

Nicolás Ajzenman, Ana Teresa Del Toro Mijares, Gregory Elacqua, Catalina Hermosilla and Santiago Nicolas Veleda

No 14771, IDB Publications (Working Papers) from Inter-American Development Bank

Abstract: A central obstacle to scaling many interventions is implementer quality: as a program expands and the pool of implementers broadens, average quality falls. Conversation-based interventions (counseling, mentoring) are particularly sensitive to this constraint. LLMs offer a natural way out: personalized exchanges at zero marginal cost and uniform quality. We run a nationwide experiment in Chile (N42,000) designed to reduce teacher shortages by increasing applications to education majors. We compare an AI chatbot, Kai, with trained human counselors, both reaching high school seniors via WhatsApp. Among students with baseline interest in education (the interventions target), Kai significantly increases first-ranked education majors and the share of education majors in students choice sets; estimates in the human arm are smaller and not significant. Although the best human counselors are highly effective, Kai matches or outperforms roughly two- thirds of them. Its advantage lies in what we call “controlled variance”: it compresses the dispersion in quality observed across human implementers. Finally, text analysis reveals different interaction patterns across arms. Kai concentrates on factual content and scores higher on semantic coherence, while counselors score substantially higher on motivational and empathetic language.

Keywords: Teachers; teacher shortages; Sclae-up; behavioral interventions; AI chatbots; Generative AI (search for similar items in EconPapers)
JEL-codes: D91 I23 I25 (search for similar items in EconPapers)
Date: 2026-10
New Economics Papers: this item is included in nep-ain, nep-edu, nep-exp and nep-lam
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Persistent link: https://EconPapers.repec.org/RePEc:idb:brikps:14771

DOI: 10.18235/0014549

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