# An overview of artificial intelligence in computer-assisted language learning

arXiv: https://arxiv.org/abs/2505.02032 · HTML: https://mlsanity.com/paper/2505.02032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 179 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs](https://mlsanity.com/paper/2407.04051/) (2024)
- [Automatic Text Scoring Using Neural Networks](https://mlsanity.com/paper/1606.04289/) (2016)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Automated essay scoring with string kernels and word embeddings](https://mlsanity.com/paper/1804.07954/) (2018)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [Generating Distractors for Reading Comprehension Questions from Real Examinations](https://mlsanity.com/paper/1809.02768/) (2018)
- [SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring](https://mlsanity.com/paper/1711.04981/) (2017)
- [Educational data mining and learning analytics: An updated survey](https://mlsanity.com/paper/2402.07956/) (2024)
- [Generative AI for Education (GAIED): Advances, Opportunities, and Challenges](https://mlsanity.com/paper/2402.01580/) (2024)
- [Automated assessment of non-native learner essays: Investigating the role of linguistic features](https://mlsanity.com/paper/1612.00729/) (2016)
- [Automated Essay Scoring based on Two-Stage Learning](https://mlsanity.com/paper/1901.07744/) (2019)
- [Automatic Short Answer Grading and Feedback Using Text Mining Methods](https://mlsanity.com/paper/1807.10543/) (2018)
- [Co-Attention Based Neural Network for Source-Dependent Essay Scoring](https://mlsanity.com/paper/1908.01993/) (2019)
- [Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments](https://mlsanity.com/paper/1812.09628/) (2018)
- [Learning to Reuse Distractors to support Multiple Choice Question Generation in Education](https://mlsanity.com/paper/2210.13964/) (2022)
- [Neural Multi-task Learning in Automated Assessment](https://mlsanity.com/paper/1801.06830/) (2018)
- [Rank-Then-Score: Enhancing Large Language Models for Automated Essay Scoring](https://mlsanity.com/paper/2504.05736/) (2025)
- [Constructing Open Cloze Tests Using Generation and Discrimination Capabilities of Transformers](https://mlsanity.com/paper/2204.07237/) (2022)
- [Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models](https://mlsanity.com/paper/2306.04980/) (2023)
- [Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models](https://mlsanity.com/paper/2404.07720/) (2024)
- [Learning to Automatically Generate Fill-In-The-Blank Quizzes](https://mlsanity.com/paper/1806.04524/) (2018)
- [Pronunciation Assessment with Multi-modal Large Language Models](https://mlsanity.com/paper/2407.09209/) (2024)
- [Mask and Cloze: Automatic Open Cloze Question Generation using a Masked Language Model](https://mlsanity.com/paper/2205.07202/) (2022)
- [Deep Learning Architecture for Automatic Essay Scoring](https://mlsanity.com/paper/2206.08232/) (2022)

## Built on by (most cited first, 2 of 2)

- [Construction and educational application of a linguistically grounded dependency treebank for Uyghur](https://mlsanity.com/paper/2507.21536/) (2025)
- [Question Type, Cognitive Load, and CEFR Alignment: Evaluating LLM-Generated EFL Grammar Drill Exercises](https://mlsanity.com/paper/2606.01592/) (2026)

## Abstract (arXiv)

Computer-assisted language learning -- CALL -- is an established research field. We review how artificial intelligence can be applied to support language learning and teaching. The need for intelligent agents that assist language learners and teachers is increasing: the human teacher's time is a scarce and costly resource, which does not scale with growing demand. Further factors contribute to the need for CALL: pandemics and increasing demand for distance learning, migration of large populations, the need for sustainable and affordable support for learning, etc. CALL systems are made up of many components that perform various functions, and AI is applied to many different aspects in CALL, corresponding to their own expansive research areas. Most of what we find in the research literature and in practical use are prototypes or partial implementations -- systems that perform some aspects of the overall desired functionality. Complete solutions -- most of them commercial -- are few, because they require massive resources. Recent advances in AI should result in improvements in CALL, yet there is a lack of surveys that focus on AI in the context of this research field. This paper aims to present a perspective on the AI methods that can be employed for language learning from a position of a developer of a CALL system. We also aim to connect work from different disciplines, to build bridges for interdisciplinary work.