# Question Generation for Adaptive Education

arXiv: https://arxiv.org/abs/2106.04262 · HTML: https://mlsanity.com/paper/2106.04262/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [Generative Language Modeling for Automated Theorem Proving](https://mlsanity.com/paper/2009.03393/) (2020)
- [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)

## Built on by (most cited first, 22 of 22)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Opportunities and Challenges in Neural Dialog Tutoring](https://mlsanity.com/paper/2301.09919/) (2023)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Adaptive and Personalized Exercise Generation for Online Language Learning](https://mlsanity.com/paper/2306.02457/) (2023)
- [Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models](https://mlsanity.com/paper/2407.15645/) (2024)
- [Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency](https://mlsanity.com/paper/2310.06837/) (2023)
- [Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research](https://mlsanity.com/paper/2310.13915/) (2023)
- [Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?](https://mlsanity.com/paper/2410.15512/) (2024)
- [Toward In-Context Teaching: Adapting Examples to Students' Misconceptions](https://mlsanity.com/paper/2405.04495/) (2024)
- [World Models for Math Story Problems](https://mlsanity.com/paper/2306.04347/) (2023)
- [Assistive Teaching of Motor Control Tasks to Humans](https://mlsanity.com/paper/2211.14003/) (2022)
- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems](https://mlsanity.com/paper/2206.04187/) (2022)
- [GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering](https://mlsanity.com/paper/2204.04179/) (2022)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students](https://mlsanity.com/paper/2402.12291/) (2024)
- [Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information](https://mlsanity.com/paper/2409.20167/) (2024)
- [Automating Turkish Educational Quiz Generation Using Large Language Models](https://mlsanity.com/paper/2406.03397/) (2024)
- [Fair Knowledge Tracing in Second Language Acquisition](https://mlsanity.com/paper/2412.18048/) (2024)
- [KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing](https://mlsanity.com/paper/2605.23933/) (2026)
- [Ask-E: An Environment for Calibrated Question Generation](https://mlsanity.com/paper/2608.06933/) (2026)

## Abstract (arXiv)

Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.