# Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

arXiv: https://arxiv.org/abs/2206.04187 · HTML: https://mlsanity.com/paper/2206.04187/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Adversarial NLI: A New Benchmark for Natural Language Understanding](https://mlsanity.com/paper/1910.14599/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Beam Search Strategies for Neural Machine Translation](https://mlsanity.com/paper/1702.01806/) (2017)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [QED: A Framework and Dataset for Explanations in Question Answering](https://mlsanity.com/paper/2009.06354/) (2020)
- [Identifying Well-formed Natural Language Questions](https://mlsanity.com/paper/1808.09419/) (2018)
- [Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization](https://mlsanity.com/paper/1909.00141/) (2019)
- [Question Generation for Adaptive Education](https://mlsanity.com/paper/2106.04262/) (2021)
- [Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds](https://mlsanity.com/paper/1911.02365/) (2019)
- [BERTTune: Fine-Tuning Neural Machine Translation with BERTScore](https://mlsanity.com/paper/2106.02208/) (2021)
- [A Survey of Automated Programming Hint Generation -- The HINTS Framework](https://mlsanity.com/paper/1908.11566/) (2019)
- [Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems](https://mlsanity.com/paper/2103.07785/) (2021)
- [Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval](https://mlsanity.com/paper/2104.08801/) (2021)
- [Let Me Know What to Ask: Interrogative-Word-Aware Question Generation](https://mlsanity.com/paper/1910.13794/) (2019)
- [Guiding Next-Step Hint Generation Using Automated Tests](https://mlsanity.com/paper/2105.05519/) (2021)
- [A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions](https://mlsanity.com/paper/2203.03724/) (2022)
- [Comparative Study of Learning Outcomes for Online Learning Platforms](https://mlsanity.com/paper/2104.07763/) (2021)

## Built on by (most cited first, 4 of 4)

- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Synthetic Context Generation for Question Generation](https://mlsanity.com/paper/2406.13188/) (2024)
- [WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation](https://mlsanity.com/paper/2412.01626/) (2024)
- [Beyond the Textual: Generating Coherent Visual Options for MCQs](https://mlsanity.com/paper/2508.18772/) (2025)

## Abstract (arXiv)

Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated questions as personalized feedback in an ITS. Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language. Our approach combines cause-effect analysis to break down student answers using text similarity-based NLP Transformer models to identify correct and incorrect or missing parts. We train a few-shot Neural Question Generation and Question Re-ranking models to show questions addressing components missing in the student answers which steers students towards the correct answer. Our model vastly outperforms both simple and strong baselines in terms of student learning gains by 45% and 23% respectively when tested in a real dialogue-based ITS. Finally, we show that our personalized corrective feedback system has the potential to improve Generative Question Answering systems.