Paper now out in ACM
#CHI2026!
Spoken English proficiency drives economic mobility for low-income Indian youth, but opportunities to actually speak English in school are scarce. Large classes, teachers without adequate proficiency, and textbooks in English but instruction in local languages. And with generative AI making polished written English cheaper, the relative value of spoken fluency is only rising.
New work led by Sneha and
@viviennebhchi deployed a voice-based chatbot for conversational English practice across four low-fee Delhi schools. Through a six-day field study involving 23 students, 6 teachers, and 5 principals, they tracked what actually happens when this technology is implemented in resource-constrained multilingual classrooms.
Although this was a qualitative study, the observed gains in student-initiated questions were substantial: from 36% on Day 1 to 65% by Day 5. Students who started nervous and hesitant began speaking freely. But the gains were fragile; ASR errors with proper nouns and Hindi loanwords, unfamiliar accents, hold-to-talk buttons that 35% of learners couldn't operate correctly. Small technical frictions quickly unraveled nascent confidence.
The paper highlights a core design tension: students wanted open-ended, conversational practice, whereas administrators wanted curriculum-aligned assessment. Reconciling both is the path to sustainable adoption.
Includes a deployment checklist for educational chatbots in resource-constrained multilingual communities.
Full paper:
lnkd.in/eQSZ82x8
Grateful to
@PennGlobal funding and the collaboration with
@csbcashoka @AshokaUniv that made this study possible!
ALT Paper's title and abstract