@inproceedings{jose-etal-2026-edge,
title = "An Edge {AI}-Based Offline System for Educational Technology in Resource-Constrained Regions",
author = "Jose, Irin and
Kenyeres, Nickolas and
Cozart-Quesada, Kailey",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.6/",
pages = "57--66",
ISBN = "979-8-89176-495-8",
abstract = "Many foundational studies have highlighted how AI can improve education, from automated grading to adaptive, real-time instruction. However, these benefits remain inaccessible to millions of students due to digital inequality. Traditional AI grading systems depend on constant cloud connectivity and high-power hardware, creating barriers for schools with limited electricity or minimal technological infrastructure. We present a proof of concept for an offline, browser-based educational assessment system that leverages on-device AI to provide automated grading without requiring internet connectivity or cloud infrastructure. The system combines WebAssembly-compiled optical character recognition with Google{'}s Gemini Nano language model, embedded directly in the Chrome browser, to process handwritten student submissions locally on a user{'}s device. This work grounds the discussion of offline, privacy-preserving educational AI in a proof-of-concept implementation. We also reflect on the ethical and societal implications of automated grading, including the risk of automation complacency and the limits of offline access in addressing educational inequity."
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<abstract>Many foundational studies have highlighted how AI can improve education, from automated grading to adaptive, real-time instruction. However, these benefits remain inaccessible to millions of students due to digital inequality. Traditional AI grading systems depend on constant cloud connectivity and high-power hardware, creating barriers for schools with limited electricity or minimal technological infrastructure. We present a proof of concept for an offline, browser-based educational assessment system that leverages on-device AI to provide automated grading without requiring internet connectivity or cloud infrastructure. The system combines WebAssembly-compiled optical character recognition with Google’s Gemini Nano language model, embedded directly in the Chrome browser, to process handwritten student submissions locally on a user’s device. This work grounds the discussion of offline, privacy-preserving educational AI in a proof-of-concept implementation. We also reflect on the ethical and societal implications of automated grading, including the risk of automation complacency and the limits of offline access in addressing educational inequity.</abstract>
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%0 Conference Proceedings
%T An Edge AI-Based Offline System for Educational Technology in Resource-Constrained Regions
%A Jose, Irin
%A Kenyeres, Nickolas
%A Cozart-Quesada, Kailey
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F jose-etal-2026-edge
%X Many foundational studies have highlighted how AI can improve education, from automated grading to adaptive, real-time instruction. However, these benefits remain inaccessible to millions of students due to digital inequality. Traditional AI grading systems depend on constant cloud connectivity and high-power hardware, creating barriers for schools with limited electricity or minimal technological infrastructure. We present a proof of concept for an offline, browser-based educational assessment system that leverages on-device AI to provide automated grading without requiring internet connectivity or cloud infrastructure. The system combines WebAssembly-compiled optical character recognition with Google’s Gemini Nano language model, embedded directly in the Chrome browser, to process handwritten student submissions locally on a user’s device. This work grounds the discussion of offline, privacy-preserving educational AI in a proof-of-concept implementation. We also reflect on the ethical and societal implications of automated grading, including the risk of automation complacency and the limits of offline access in addressing educational inequity.
%U https://aclanthology.org/2026.nlp4pi-1.6/
%P 57-66
Markdown (Informal)
[An Edge AI-Based Offline System for Educational Technology in Resource-Constrained Regions](https://aclanthology.org/2026.nlp4pi-1.6/) (Jose et al., NLP4PI 2026)
ACL