---
title: "AI-based Arabic Language and Speech Tutor — Shao 2022"
description: "The authors develop AI-ALST, an intelligent tutor for students of Moroccan Arabic (Darija) at the University of Arizona."
authors: ["Sicong Shao", "Saleem Alharir", "Salim Hariri", "Pratik Satam", "Sonia Shiri", "Abdessamad Mbarki"]
year: 2022
venue: "arXiv:2210.12346"
source: "https://arxiv.org/abs/2210.12346"
doi: "10.48550/arxiv.2210.12346"
arxiv: "2210.12346"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/shao-2022-ai-based-arabic-language-speech/"
---
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# AI-based Arabic Language and Speech Tutor

**Authors:** Sicong Shao, Saleem Alharir, Salim Hariri, Pratik Satam, [Sonia Shiri](https://md.research.elamur.ai/free/en/authors/sonia-shiri/), Abdessamad Mbarki · **Year:** 2022 · **Venue:** arXiv:2210.12346 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2210.12346) · [PDF](https://arxiv.org/pdf/2210.12346) · [DOI 10.48550/arxiv.2210.12346](https://doi.org/10.48550/arxiv.2210.12346) · [arXiv 2210.12346](https://arxiv.org/abs/2210.12346) · [OpenAlex](https://openalex.org/W4317600352)

**Citations:** 22 citations (OpenAlex, 2026-10-14)

## Summary
The authors develop AI-ALST, an intelligent tutor for students of Moroccan Arabic (Darija) at the University of Arizona. Machine-learning pronunciation training had not previously been applied to this dialect. Dialectal Arabic is low-resource and has few qualified human tutors, so the system aims to give learners an always-available self-study space that goes past multiple-choice drills. Its core is a computer-assisted pronunciation training (CAPT) component built on four parts:
- MFCC acoustic features,
- a bidirectional LSTM,
- an attention layer,
- cost-sensitive learning to handle the scarcity of mispronounced examples.

The material comes from two multimedia Moroccan Arabic eTextbooks used in blended, flipped-classroom courses. The first evaluation is narrow: word pronunciation from lesson 1 only. Scoring uses accuracy, precision, recall and F1-score. The authors report that the model reliably identifies pronunciation errors. The work is a proof of concept for AI tutoring in an under-served Arabic dialect. Planned next steps are adaptive exercises, explainable error diagnoses and study of first-language accent effects.

## Key points
- The CAPT pipeline is MFCC features → BiLSTM → attention mechanism, with a cost-based strategy for class imbalance. It was evaluated on lesson 1 word pronunciation.
- The paper frames tutoring systems as three parts: domain knowledge, a student model that keeps learner history and drives adaptation, and a teaching module.
- The course material is organized as follows:
  - Level I has seven modules of three lessons each.
  - Each lesson splits into vocabulary/structures, listening comprehension and speaking assessment.
  - The modules come from a needs survey of Arabic Language Flagship Program stakeholders.
- The related-work section cites the Arabic Learner Corpus (ALC): 282,732 words and 1,585 materials from 942 students of 67 nationalities. It also lists Arabic dialect chatbots BOTTA (Egyptian) and Nabiha (Saudi).
- Chatbot knowledge bases are grouped into rule-based, retrieval-based and generative-based types.

## Interesting ideas
- Useful tutoring feedback should go beyond flagging an error: it should name the mistake, trace where it originates, and propose targeted follow-up practice. — For Mova (answer): When a student-reader fails a school test item, report which pipeline stage caused the miss (parse, frame, world state) and pick follow-up items that exercise that stage.
- Correct examples vastly outnumber errors, so the authors make misclassifying the rare class more expensive instead of rebalancing the data. — For Mova (tree): Cost-weighting could raise accuracy on rare UD relations (e.g. `vocative`, `dislocated`) in the perceptron parser or gparse without resampling treebanks.
- Imbalanced detection is scored with precision, recall and F1 alongside accuracy, since accuracy alone hides poor performance on the rare class. — For Mova (learn): Silver-data and repair-rule evaluations should always report per-class precision/recall for rare phenomena, so a high overall score cannot mask a failing minority case.
- A persistent student model records each learner's history and lets the system change what it presents next. — For Mova (answer): Mova's readers could keep a history of failed question types across test suites, so training and self-play focus on the weakest skills.
- The authors plan to give explainable diagnoses for each detected pronunciation error, rather than leave the classifier as an opaque judge. — For Mova (learn): This supports Mova's rule that every decision, including learned ones, must carry a human-readable justification that can be audited.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

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

```bibtex
@misc{shao2022ai,
  title = {{AI-based Arabic Language and Speech Tutor}},
  author = {Shao, Sicong and Alharir, Saleem and Hariri, Salim and Satam, Pratik and Shiri, Sonia and Mbarki, Abdessamad},
  year = {2022},
  doi = {10.48550/arxiv.2210.12346},
  eprint = {2210.12346},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2210.12346}
}
```

- [← previous DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2022-discogem-crowdsourced-corpus-genre-mixed/)
- [next → ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-connprompt-connective-cloze-prompt-learning/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/)

## References

27 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Y.-F. Wang and S. Petrina, “Using learning analytics to understand the design of an intelligent language tutor–Chatbot lucy,” Editorial Preface 4, no. 11, 124-131, 2013. [find](https://scholar.google.com/scholar?q=Y%2E%2DF%2E+Wang+and+S%2E+Petrina%2C+%E2%80%9CUsing+learning+analytics+to+understand+the+design+of+an+intelligent+language+tutor%E2%80%93Chatbot+lucy%2C%E2%80%9D+Editorial+Preface+4%2C+no%2E+11%2C+124%2D131%2C+2013%2E)
2. G. Richards, “Book review: Technology-mediated Learning Environments for Young English Learners (L. Leanne Parker),” Educational Technology & Society, 12 (2), 334-336, 2009. [find](https://scholar.google.com/scholar?q=G%2E+Richards%2C+%E2%80%9CBook+review%3A+Technology%2Dmediated+Learning+Environments+for+Young+English+Learners+%28L%2E+Leanne+Parker%29%2C%E2%80%9D+Educational+Technology+%26+Society%2C+12+%282%29%2C+334%2D336%2C+2009%2E)
3. A. Kerlyl, P. Hall, and S. Bull, “Bringing chatbots into education: Towards natural language negotiation of open learner models,” in International conference on innovative techniques and applications of artificial intelligence, pp. 179-192. Springer, London, 2006. [find](https://scholar.google.com/scholar?q=A%2E+Kerlyl%2C+P%2E+Hall%2C+and+S%2E+Bull%2C+%E2%80%9CBringing+chatbots+into+education%3A+Towards+natural+language+negotiation+of+open+learner+models%2C%E2%80%9D+in+International+conference+on+innovative+techniques+and+applications+)
4. E. Wenger. Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann, 2014. [find](https://scholar.google.com/scholar?q=E%2E+Wenger%2E+Artificial+intelligence+and+tutoring+systems%3A+computational+and+cognitive+approaches+to+the+communication+of+knowledge%2E+Morgan+Kaufmann%2C+2014%2E)
5. J. E. Greer and Gordon I. McCalla, “Student modelling: the key to individualized knowledge-based instruction,” Vol. 125. Springer Science & Business Media, 2013. [find](https://scholar.google.com/scholar?q=J%2E+E%2E+Greer+and+Gordon+I%2E+McCalla%2C+%E2%80%9CStudent+modelling%3A+the+key+to+individualized+knowledge%2Dbased+instruction%2C%E2%80%9D+Vol%2E+125%2E+Springer+Science+%26+Business+Media%2C+2013%2E)
6. J. Fouz-González, “Trends and directions in computer-assisted pronunciation training,” Investigating English Pronunciation, 314-342, 2015. [find](https://scholar.google.com/scholar?q=J%2E+Fouz%2DGonz%C3%A1lez%2C+%E2%80%9CTrends+and+directions+in+computer%2Dassisted+pronunciation+training%2C%E2%80%9D+Investigating+English+Pronunciation%2C+314%2D342%2C+2015%2E)
7. A. Diment, E. Fagerlund, A. Benfield, and T. Virtanen, “Detection of typical pronunciation errors in non-native English speech using convolutional recurrent neural networks,” in International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2019. [find](https://scholar.google.com/scholar?q=A%2E+Diment%2C+E%2E+Fagerlund%2C+A%2E+Benfield%2C+and+T%2E+Virtanen%2C+%E2%80%9CDetection+of+typical+pronunciation+errors+in+non%2Dnative+English+speech+using+convolutional+recurrent+neural+networks%2C%E2%80%9D+in+International+Joint+Co)
8. M. A. Mansour, Mohamed Abdelmageed, “The absence of Arabic corpus linguistics: a call for creating an Arabic national corpus,” International Journal of Humanities and Social Science 3, no. 12, 81-90, 2013. [find](https://scholar.google.com/scholar?q=M%2E+A%2E+Mansour%2C+Mohamed+Abdelmageed%2C+%E2%80%9CThe+absence+of+Arabic+corpus+linguistics%3A+a+call+for+creating+an+Arabic+national+corpus%2C%E2%80%9D+International+Journal+of+Humanities+and+Social+Science+3%2C+no%2E+12%2C+81%2D90%2C+)
9. M. Zaki, “Corpus based teaching in the Arabic classroom: Theoretical and practical perspectives,” International Journal of Applied Linguistics 27, no. 2, 514-541, 2017. [find](https://scholar.google.com/scholar?q=M%2E+Zaki%2C+%E2%80%9CCorpus+based+teaching+in+the+Arabic+classroom%3A+Theoretical+and+practical+perspectives%2C%E2%80%9D+International+Journal+of+Applied+Linguistics+27%2C+no%2E+2%2C+514%2D541%2C+2017%2E)
10. J. Weizenbaum, “ELIZA—a computer program for the study of natural language communication between man and machine,” Communications of the ACM 9, no. 1, 36-45, 1966. [find](https://scholar.google.com/scholar?q=J%2E+Weizenbaum%2C+%E2%80%9CELIZA%E2%80%94a+computer+program+for+the+study+of+natural+language+communication+between+man+and+machine%2C%E2%80%9D+Communications+of+the+ACM+9%2C+no%2E+1%2C+36%2D45%2C+1966%2E)
11. J. L. Hutchens and M. D. Alder, “Introducing megahal,” in New Methods in Language Processing and Computational Natural Language Learning, 1998. [find](https://scholar.google.com/scholar?q=J%2E+L%2E+Hutchens+and+M%2E+D%2E+Alder%2C+%E2%80%9CIntroducing+megahal%2C%E2%80%9D+in+New+Methods+in+Language+Processing+and+Computational+Natural+Language+Learning%2C+1998%2E)
12. L. Fryer and R. Carpenter, “Bots as language learning tools,” Language Learning & Technology 10, no. 3, 8-14, 2006. [find](https://scholar.google.com/scholar?q=L%2E+Fryer+and+R%2E+Carpenter%2C+%E2%80%9CBots+as+language+learning+tools%2C%E2%80%9D+Language+Learning+%26+Technology+10%2C+no%2E+3%2C+8%2D14%2C+2006%2E)
13. A. Ahmed, N. Ali, M. Alzubaidi, W. Zaghouani, A. Abd-alrazaq, and M. Househ, “Arabic Chatbot Technologies: A Scoping Review,” Computer Methods and Programs in Biomedicine Update, 2022. [find](https://scholar.google.com/scholar?q=A%2E+Ahmed%2C+N%2E+Ali%2C+M%2E+Alzubaidi%2C+W%2E+Zaghouani%2C+A%2E+Abd%2Dalrazaq%2C+and+M%2E+Househ%2C+%E2%80%9CArabic+Chatbot+Technologies%3A+A+Scoping+Review%2C%E2%80%9D+Computer+Methods+and+Programs+in+Biomedicine+Update%2C+2022%2E)
14. S. Nakagawa, L. Wang, and S. Ohtsuka, “Speaker identification and verification by combining MFCC and phase information,” IEEE transactions on audio, speech, and language processing 20, no. 4, 2011. [find](https://scholar.google.com/scholar?q=S%2E+Nakagawa%2C+L%2E+Wang%2C+and+S%2E+Ohtsuka%2C+%E2%80%9CSpeaker+identification+and+verification+by+combining+MFCC+and+phase+information%2C%E2%80%9D+IEEE+transactions+on+audio%2C+speech%2C+and+language+processing+20%2C+no%2E+4%2C+2011%2E)
15. A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Physica D: Nonlinear Phenomena, 2020. [find](https://scholar.google.com/scholar?q=A%2E+Sherstinsky%2C+%E2%80%9CFundamentals+of+recurrent+neural+network+%28RNN%29+and+long+short%2Dterm+memory+%28LSTM%29+network%2C%E2%80%9D+Physica+D%3A+Nonlinear+Phenomena%2C+2020%2E)
16. Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural Computation, Vol. 31, no. 7, 1235-1270, 2019. [find](https://scholar.google.com/scholar?q=Y%2E+Yu%2C+X%2E+Si%2C+C%2E+Hu%2C+and+J%2E+Zhang%2C+%E2%80%9CA+review+of+recurrent+neural+networks%3A+LSTM+cells+and+network+architectures%2C%E2%80%9D+Neural+Computation%2C+Vol%2E+31%2C+no%2E+7%2C+1235%2D1270%2C+2019%2E)
17. S. Siami-Namini, N. Tavakoli, and A. S. Namin, “The performance of LSTM and BiLSTM in forecasting time series,” in IEEE International Conference on Big Data (Big Data), pp. 3285-3292. IEEE, 2019. [find](https://scholar.google.com/scholar?q=S%2E+Siami%2DNamini%2C+N%2E+Tavakoli%2C+and+A%2E+S%2E+Namin%2C+%E2%80%9CThe+performance+of+LSTM+and+BiLSTM+in+forecasting+time+series%2C%E2%80%9D+in+IEEE+International+Conference+on+Big+Data+%28Big+Data%29%2C+pp%2E+3285%2D3292%2E+IEEE%2C+2019%2E)
18. D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in International Conference on Learning Representations, 2014. [find](https://scholar.google.com/scholar?q=D%2E+Bahdanau%2C+K%2E+Cho%2C+and+Y%2E+Bengio%2C+%E2%80%9CNeural+machine+translation+by+jointly+learning+to+align+and+translate%2C%E2%80%9D+in+International+Conference+on+Learning+Representations%2C+2014%2E) [our card](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
19. Z.-H. Zhou and X.-Y. Liu, “Training cost-sensitive neural networks with methods addressing the class imbalance problem,” IEEE Transactions on knowledge and data engineering 18, no. 1, 63-77, 2005. [find](https://scholar.google.com/scholar?q=Z%2E%2DH%2E+Zhou+and+X%2E%2DY%2E+Liu%2C+%E2%80%9CTraining+cost%2Dsensitive+neural+networks+with+methods+addressing+the+class+imbalance+problem%2C%E2%80%9D+IEEE+Transactions+on+knowledge+and+data+engineering+18%2C+no%2E+1%2C+63%2D77%2C+2005%2E)
20. J. Bang, J. Lee, G. G. Lee, and M. Chung, “Pronunciation variants prediction method to detect mispronunciations by Korean learners of English,” ACM Transactions on Asian Language Information Processing (TALIP) 13, no. 4, 1-21, 2014. [find](https://scholar.google.com/scholar?q=J%2E+Bang%2C+J%2E+Lee%2C+G%2E+G%2E+Lee%2C+and+M%2E+Chung%2C+%E2%80%9CPronunciation+variants+prediction+method+to+detect+mispronunciations+by+Korean+learners+of+English%2C%E2%80%9D+ACM+Transactions+on+Asian+Language+Information+Processin)
21. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations, 2015. [find](https://scholar.google.com/scholar?q=D%2E+P%2E+Kingma+and+J%2E+Ba%2C+%E2%80%9CAdam%3A+A+method+for+stochastic+optimization%2C%E2%80%9D+in+International+Conference+on+Learning+Representations%2C+2015%2E)
22. I. Hamed, D. Pavel, C. Li, M. Elmahdy, S. Abdennadher, and N. T. Vu, “Investigations on speech recognition systems for low-resource dialectal Arabic–English code-switching speech,” Computer Speech & Language, 2022. [find](https://scholar.google.com/scholar?q=I%2E+Hamed%2C+D%2E+Pavel%2C+C%2E+Li%2C+M%2E+Elmahdy%2C+S%2E+Abdennadher%2C+and+N%2E+T%2E+Vu%2C+%E2%80%9CInvestigations+on+speech+recognition+systems+for+low%2Dresource+dialectal+Arabic%E2%80%93English+code%2Dswitching+speech%2C%E2%80%9D+Computer+Speech+%26+L)
23. S. Shiri, “Intercultural communicative competence development during and after language study abroad: Insights from Arabic,” Foreign Language Annals 48, no. 4, 2015. [find](https://scholar.google.com/scholar?q=S%2E+Shiri%2C+%E2%80%9CIntercultural+communicative+competence+development+during+and+after+language+study+abroad%3A+Insights+from+Arabic%2C%E2%80%9D+Foreign+Language+Annals+48%2C+no%2E+4%2C+2015%2E)
24. S. Shao, C. Tunc, A. Al-Shawi, and S. Hariri, “An ensemble of ensembles approach to author attribution for internet relay chat forensics,” ACM Transactions on Management Information Systems (TMIS) 11, no. 4, 1-25, 2022. [find](https://scholar.google.com/scholar?q=S%2E+Shao%2C+C%2E+Tunc%2C+A%2E+Al%2DShawi%2C+and+S%2E+Hariri%2C+%E2%80%9CAn+ensemble+of+ensembles+approach+to+author+attribution+for+internet+relay+chat+forensics%2C%E2%80%9D+ACM+Transactions+on+Management+Information+Systems+%28TMIS%29+11%2C)
25. S. Shao, C. Tunc, A. Al-Shawi, and S. Hariri, “One-class classification with deep autoencoder neural networks for author verification in internet relay chat,” in IEEE/ACS International Conference on Computer Systems and Applications, 2019. [find](https://scholar.google.com/scholar?q=S%2E+Shao%2C+C%2E+Tunc%2C+A%2E+Al%2DShawi%2C+and+S%2E+Hariri%2C+%E2%80%9COne%2Dclass+classification+with+deep+autoencoder+neural+networks+for+author+verification+in+internet+relay+chat%2C%E2%80%9D+in+IEEE%2FACS+International+Conference+on+C)
26. D. Al-Ghadhban and Nora Al-Twairesh, “Nabiha: an Arabic dialect chatbot,” International Journal of Advanced Computer Science and Applications 11, no. 3, 2020. [find](https://scholar.google.com/scholar?q=D%2E+Al%2DGhadhban+and+Nora+Al%2DTwairesh%2C+%E2%80%9CNabiha%3A+an+Arabic+dialect+chatbot%2C%E2%80%9D+International+Journal+of+Advanced+Computer+Science+and+Applications+11%2C+no%2E+3%2C+2020%2E)
27. D. A. Ali and N. Habash, “Botta: An arabic dialect chatbot,” in International Conference on Computational Linguistics: System Demonstrations, 2016. [find](https://scholar.google.com/scholar?q=D%2E+A%2E+Ali+and+N%2E+Habash%2C+%E2%80%9CBotta%3A+An+arabic+dialect+chatbot%2C%E2%80%9D+in+International+Conference+on+Computational+Linguistics%3A+System+Demonstrations%2C+2016%2E)