@inproceedings{chang-etal-2026-learning,
title = "Learning to Prompt: Improving Student Engagement with Adaptive {LLM}-based High-School Tutoring",
author = "Chang, Po-Chin and
Hogan, Nicholas and
Plaat, Aske and
van der Meer, Michiel T.",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.24/",
pages = "365--393",
ISBN = "979-8-89176-460-6",
abstract = "LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines (0.694 vs. 0.647 and 0.64, $p<0.001$). A/B testing ($N=656$ conversations from 359 students) shows sim-to-real transfer where the model switches from analytical to scaffolding learning strategies. Our adaptive prompt selection mechanism improves instructional efficiency, maintains pedagogical quality and reduces interactions by around 3 turns ($p=0.007$). While a greedy router achieves a comparable exercise conversion rate with the baseline (19.1{\%} vs. 19.6{\%}), a stochastic router that samples strategies leads to a higher conversion rate (28.1{\%})."
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<abstract>LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines (0.694 vs. 0.647 and 0.64, p<0.001). A/B testing (N=656 conversations from 359 students) shows sim-to-real transfer where the model switches from analytical to scaffolding learning strategies. Our adaptive prompt selection mechanism improves instructional efficiency, maintains pedagogical quality and reduces interactions by around 3 turns (p=0.007). While a greedy router achieves a comparable exercise conversion rate with the baseline (19.1% vs. 19.6%), a stochastic router that samples strategies leads to a higher conversion rate (28.1%).</abstract>
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%0 Conference Proceedings
%T Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring
%A Chang, Po-Chin
%A Hogan, Nicholas
%A Plaat, Aske
%A van der Meer, Michiel T.
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F chang-etal-2026-learning
%X LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop and test a system with subject-aware prompting, based on 14 pedagogical features (e.g., tutor scaffolding, student understanding) extracted from raw transcripts. We first train a prompt routing model in a simulation environment, and then deploy it for online adaptation with actual high-school students. The simulation benchmark shows the router outperforming two static baselines (0.694 vs. 0.647 and 0.64, p<0.001). A/B testing (N=656 conversations from 359 students) shows sim-to-real transfer where the model switches from analytical to scaffolding learning strategies. Our adaptive prompt selection mechanism improves instructional efficiency, maintains pedagogical quality and reduces interactions by around 3 turns (p=0.007). While a greedy router achieves a comparable exercise conversion rate with the baseline (19.1% vs. 19.6%), a stochastic router that samples strategies leads to a higher conversion rate (28.1%).
%U https://aclanthology.org/2026.emnlp-industry.24/
%P 365-393
Markdown (Informal)
[Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring](https://aclanthology.org/2026.emnlp-industry.24/) (Chang et al., EMNLP 2026)
ACL