---
title: "Evaluating Adaptive Personalization of Educational Readings"
description: "The authors build an offline framework that screens such policies with simulated learners grounded in cognitive theory. 21 references."
authors: ["Ryan T. Woo", "Anmol Rao", "Aryan Keluskar", "Yinong Chen"]
year: 2026
venue: "Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)"
source: "https://aclanthology.org/2026.bea-1.63/"
doi: "10.18653/v1/2026.bea-1.63"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/woo-2026-evaluating-adaptive-personalization-educational-readings/"
---
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# Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

**Authors:** Ryan T. Woo, Anmol Rao, Aryan Keluskar and Yinong Chen · **Year:** 2026 · **Venue:** [Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)](https://md.research.elamur.ai/free/en/venues/bea/) ([BEA 2026](https://md.research.elamur.ai/free/en/venues/bea/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bea-1.63/) · [PDF](https://aclanthology.org/2026.bea-1.63.pdf) · [DOI 10.18653/v1/2026.bea-1.63](https://doi.org/10.18653/v1/2026.bea-1.63) · [OpenAlex](https://openalex.org/W7166840725)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Tailoring course readings to what each student already knows or misunderstands is hard for instructors to do at scale. Testing adaptive reading policies directly in classrooms is also costly. The authors build an offline framework that screens such policies with simulated learners grounded in cognitive theory. From Wikibooks open textbooks, an LLM-assisted workflow proposes learning objectives (LOs) and knowledge components (KCs). Humans then refine these in a browser-based editor called Ontology Atlas. Textbook chunks are tagged with these entities by an LLM-as-judge pipeline, and aligned readings and multiple-choice items are generated. Simulated readers absorb passages through a Construction–Integration-style memory model. They differ along DIME reader factors, revise misconceptions following KReC, and are affected by New Dale–Chall readability. Answers are chosen by scoring options against each learner's explicit memory, and BKT estimates drive which readings come next. Adaptive cohorts clearly beat matched controls in computer science, gained modestly but inconclusively in inorganic chemistry, and showed no benefit in biology. The framework offers an inspectable way to vet reading policies before testing them with real students.

## Key points
- Three Wikibooks courses were used. Foundations of Computer Science has 16 chapters, 53 LOs and 131 KCs. General Biology has 20 chapters, 60 LOs and 172 KCs. Introduction to Inorganic Chemistry has 12 chapters, 57 LOs and 177 KCs.
- Reading is treated as the main intervention, and quizzes serve only as the observation channel for KC-level BKT tracking. The learner's hidden true knowledge is kept separate from the tutor's estimate.
- Answer choice is score-based rather than free-form LLM generation. This avoids the "competence paradox" of overly capable LLM students and keeps runs reproducible.
- Adaptive reading led control in every computer science and chemistry cycle. Biology showed a crossover: hidden mastery improved descriptively, but observed accuracy did not.
- The simulator is not yet calibrated against human data, and it uses fixed heuristic coefficients. Domain differences may partly reflect the quality of the ontology and labeling pipeline.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Arizona State University](https://md.research.elamur.ai/free/en/universities/arizona-state-university/) — University, Tempe, United States · 50 works · 2,032 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works · [🖥️ Computer Science Applications](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/) · 102 works

🧭 Topics (1): [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,220 works

## Cite

Cite as: Ryan T. Woo, Anmol Rao, Aryan Keluskar and Yinong Chen. 2026. Evaluating Adaptive Personalization of Educational Readings with Simulated Learners. Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{woo2026evaluating,
  title = {{Evaluating Adaptive Personalization of Educational Readings with Simulated Learners}},
  author = {Ryan T. Woo, Anmol Rao, Aryan Keluskar and Yinong Chen},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.bea-1.63},
  url = {https://doi.org/10.18653/v1/2026.bea-1.63}
}
```

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## References

21 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*