---
title: "Capturing the full potential of Maltese language learning through ChatGPT — Żammit 2024"
description: "The paper asks whether ChatGPT can help adult international students learn Maltese. 30 references."
authors: ["Jacqueline Żammit"]
year: 2024
venue: "Technology in Language Teaching & Learning 6"
source: "https://www.castledown.com/journals/tltl/article/view/1082"
doi: "10.29140/tltl.v6n1.1082"
licence: "CC BY-NC (journal licence per DOAJ; Technology in Language Teaching & Learning)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zammit-2024-capturing-full-potential-maltese-language/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zammit-2024-capturing-full-potential-maltese-language/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/) / Capturing the full potential of Maltese language learning through ChatGPT

# Capturing the full potential of Maltese language learning through ChatGPT

**Authors:** Jacqueline Żammit · **Year:** 2024 · **Venue:** Technology in Language Teaching & Learning 6 · **Licence:** [CC BY-NC (journal licence per DOAJ; Technology in Language Teaching & Learning)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://doi.org/10.29140/tltl.v6n1.1082) · [PDF](https://www.castledown.com/journals/tltl/article/download/1082/243) · [DOI 10.29140/tltl.v6n1.1082](https://doi.org/10.29140/tltl.v6n1.1082) · [OpenAlex](https://openalex.org/W4395089316)

**Citations:** 6 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks whether ChatGPT can help adult international students learn Maltese. Maltese is a minority language with about half a million speakers, its use is declining among younger people, and few digital learning materials exist for it. The author asked 41 learners to use ChatGPT as a study aid for two weeks. Their experience was then gathered through surveys and focus group discussions, so the evidence comes from learners' own reports rather than proficiency tests. Participants found the tool convenient and easy to approach: most rated it very easy or somewhat easy to use. However, they were frustrated by its weak handling of Maltese. Its answers were often inaccurate, and its help with grammar, vocabulary and conversation practice was limited. The study argues that easy access does not make up for poor coverage of a language. It calls for better Maltese training data, native speaker involvement and joint work between linguists and AI specialists, which is relevant to any less widely spoken language.

## Key points
- 41 adult international learners of Maltese used ChatGPT for two weeks. Evaluation relied on surveys and focus groups.
- On ease of use, 23 participants said "very easy," 16 said "somewhat easy," and 2 were neutral.
- Learners said ChatGPT offered only modest help when they worked on Maltese grammar, built vocabulary or practised conversation, and that this help was frequently wrong.
- The author lists the study's own weaknesses: a small sample, a short duration, no control group and no objective measure of learning.
- Recommended next steps:
  - longitudinal designs with control groups;
  - standardized Maltese proficiency tests or performance-based tasks;
  - iterative fine-tuning that adds native speaker input, Maltese-specific language features and cultural context;
  - multimodal (audio/video) features;
  - head-to-head comparisons with other learning tools.

## Interesting ideas
- Usability and correctness are reported separately, and the paper shows that high ease-of-use ratings can sit alongside poor accuracy, so user satisfaction should never stand in for quality.
- The author admits that self-reports cannot prove learning and calls for control groups plus objective tests, which is a reusable rule for judging any assistant.
- A general LLM is strong where it has seen a lot of text and weak on a thinly covered language, so its authority as a helper depends on how much data it saw in that area.
- The paper proposes iterative refinement that brings in native speaker judgments and explicit language-specific features instead of relying on raw scale alone.
- It stresses that learners also need cultural references and situational awareness, not just grammar, to use a language correctly.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Evaluation reports should keep "fluent/convenient output" metrics apart from accuracy against gold data, so a smooth-looking pipeline cannot hide wrong trees or frames.
- **I2** (learn): Every claimed improvement should be checked against a baseline run and a negative control on held-out tests, not against impressions from a few inspected examples.
- **I3** (learn): LLM teacher tiers should be trusted less on rare constructions, dialects and low-frequency domains, and their silver labels there should be cross-checked by more witness parsers or human review.
- **I4** (tokens): Morphology and lemma rules for tricky English phenomena (irregular forms, clitic-like contractions) could be improved in rounds, with targeted expert-checked examples added each round.
- **I5** (world): The compiled knowledge layer could carry situational and cultural defaults, such as typical settings and customary actions, so the world of a story is filled in plausibly and explainably.

*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

- [University of Malta](https://md.research.elamur.ai/free/en/universities/university-of-malta/) — University, Msida, Malta · 12 works · 573 citations

📚 Shelves (1): [🗣️ Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · 4,876 works

🧭 Topics (1): [🐪 Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 1,334 works

## Appears in

Countries: [🌍 Malta](https://md.research.elamur.ai/free/en/countries/malta/)

## Cite

Cite as: Jacqueline Żammit. 2024. Capturing the full potential of Maltese language learning through ChatGPT. Technology in Language Teaching & Learning 6.

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

```bibtex
@inproceedings{zammit2024capturing,
  title = {{Capturing the full potential of Maltese language learning through ChatGPT}},
  author = {Jacqueline Żammit},
  booktitle = {Technology in Language Teaching \& Learning 6},
  year = {2024},
  doi = {10.29140/tltl.v6n1.1082},
  url = {https://doi.org/10.29140/tltl.v6n1.1082}
}
```

- [← previous Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-arabic-stable-lm-adapting-stable/)
- [next → Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-detecting-anti-semitic-hate-speech/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/)

## References

30 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Allouche, E. (2023). Artificial intelligence and education: Tests and “interview” simulations with ChatGPT (Open AI). Hypotheses. [https://edunumrech.hypotheses.org/8034](https://edunumrech.hypotheses.org/8034) [↗](https://edunumrech.hypotheses.org/8034)
2. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN. http://dx.doi.org/10.2139/ssrn.4337484 [↗](https://doi.org/10.2139/ssrn.4337484)
3. Biswas, S. (2023). ChatGPT and the future of medical writing. Radiology, 307(2), 223312. https://doi.org/10.1148/radiol.223312 [↗](https://doi.org/10.1148/radiol.223312)
4. Burström, T., Parida, V., Lahti, T., & Wincent, J. (2021). AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. Journal of Business Research, 127, 85–95. https://doi.org/10.1016/j.jbusres.2021.01.016 [↗](https://doi.org/10.1016/j.jbusres.2021.01.016)
5. Cao, Y., Zhou, L., Lee, S., Cabello, L., Chen, M., Hershcovich, D. (2023). Assessing cross-cultural alignment between ChatGPT and human societies: An empirical study. Computer Science: Computation and Language. Preprint. https://doi.org/10.48550/arXiv.2303.17466 Chinonso, O. E., Adalikwu Mfon-Ette. [↗](https://doi.org/10.48550/arxiv.2303.17466) [our card](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/)
6. T., & Tolorunleke, C. A. (2023). ChatGPT for teaching, learning and research: prospects and challenges. Global Academic Journal of Humanities and Social Sciences, 5(2), 33–40. https://doi.org/10.36348/gajhss.2023.v05i02.001 [↗](https://doi.org/10.36348/gajhss.2023.v05i02.001)
7. Corneliu, B. (2022). AI for development: implications for theory and practice. Oxford Development Studies, 50(1), 78–90. https://doi.org/10.1080/13600818.2021.1960960 [↗](https://doi.org/10.1080/13600818.2021.1960960)
8. El Shazly, R. F. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3), 12667. https://doi.org/10.1111/exsy.12667 [↗](https://doi.org/10.1111/exsy.12667)
9. Farrokhnia, M., Kazem Banihashem, S., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, https://doi.org/10.1080/14703297.2023.2195846 [↗](https://doi.org/10.1080/14703297.2023.2195846)
10. Firat, M. (2023). How Chat GPT Can Transform Autodidactic Experiences and Open Education? https://doi.org/10.31219/osf.io/9ge8m [↗](https://doi.org/10.31219/osf.io/9ge8m)
11. Frances, C. & Zimotti, G. (2023). Robots vs. Humans: Does ChatGPT Pose a Challenge to Second Language Writing? [https://fltmag.com/chatgpt-second-language-writing/](https://fltmag.com/chatgpt-second-language-writing/) [↗](https://fltmag.com/chatgpt-second-language-writing/)
12. Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089 [↗](https://doi.org/10.1016/j.tbench.2023.100089)
13. Hwang, G. J. & Chang, C. Y. (2021). A review of opportunities and challenges of chatbots in education, Taylor & Francis Online: Interactive Learning Environments. https://doi.org/10.1080/10 494820.2021.1952615 [↗](https://doi.org/10.1080/10)
14. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Haleem Khan, I. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j. tbench.2023.100115 [↗](https://doi.org/10.1016/j)
15. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 [↗](https://doi.org/10.1016/j.lindif.2023.102274) [our card](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
16. Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82, 3713–3744. https://doi.org/10.1007/s11042-022-13428-4 [↗](https://doi.org/10.1007/s11042-022-13428-4)
17. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 52(2). https://doi.org/10.1177/00336882231162868 [↗](https://doi.org/10.1177/00336882231162868)
18. Lin, X. (2023). Exploring the role of ChatGPT as a facilitator for motivating self-directed learning among adult learners. Adult Learning. Advance online publication. https://doi.org/10.1177/10451595231184928 [↗](https://doi.org/10.1177/10451595231184928)
19. Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science & Technology,74(5), 570–581. http://dx.doi.org/10.1002/asi.24750 [↗](https://doi.org/10.1002/asi.24750)
20. Moore, P. V. (2019). Jerry Kaplan artificial intelligence: what everyone needs to know. SAGE Journals: Organization Studies, 40(3), 466–470. https://doi.org/10.1177/0170840618792173 [↗](https://doi.org/10.1177/0170840618792173)
21. Özçift, A., Akarsu, K., Yumuk, F., & Söylemez, C. (2021). Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish. Automatika, 62(2), 226–238. https://doi.org/10.1080/00051144.2021.1922150 [↗](https://doi.org/10.1080/00051144.2021.1922150)
22. Partha Pratim, R. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003 [↗](https://doi.org/10.1016/j.iotcps.2023.04.003)
23. Pruneski, J. A., Pareek, A., Nwachukwu, B. U., Martin, R. K., Kelly, B.T., Karlsson, J., Pearle, A. D., Kiapour, A. M., & Williams III, R. J. (2023). Natural language processing: using artificial intelligence to understand human language in orthopedics. Knee Surgery, Sports Traumatology, Arthroscopy, 31, 1203–1211. https://doi.org/10.1007/s00167-022-07272-0 [↗](https://doi.org/10.1007/s00167-022-07272-0)
24. Saunders, M., Lewis, P., & Thornhill, A. (2012). Research Methods for Business Students. Pearson Education Limited. [find](https://scholar.google.com/scholar?q=Saunders%2C+M%2E%2C+Lewis%2C+P%2E%2C+%26+Thornhill%2C+A%2E+%282012%29%2E+Research+Methods+for+Business+Students%2E+Pearson+Education+Limited%2E)
25. Sejnowski, T. J. (2023). Large language models and the reverse Turing test. Neural Comput, 35(3), 309–342. https://doi.org/10.1162/neco_a_01563 [↗](https://doi.org/10.1162/neco_a_01563)
26. Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner– instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(54), 1–23. https://doi.org/10.1186/s41239-021-00292-9 [↗](https://doi.org/10.1186/s41239-021-00292-9)
27. Shen, Y., Heacock, L., Elias, J., Hensel, K. D., Reig, B., Shih, G., & Moy, L. (2023). ChatGPT and other large language models are double-edged swords. Radiology, 230163. https://doi.org/10.1148/radiol.230163 [↗](https://doi.org/10.1148/radiol.230163)
28. Topsakal, O., & Topsakal, E. (2022). Framework for a foreign language teaching software for children utilizing AR, voicebots and ChatGPT (large language models). The Journal of Cognitive Systems, 7(2), 33–38. https://doi.org/10.52876/jcs.1227392 [↗](https://doi.org/10.52876/jcs.1227392)
29. Żammit, J. (2022a). Sociocultural issues experienced by adults learning Maltese as a second language. IAFOR Journal of Education: Language Learning in Education,10(1), 73–89. https://doi.org/10.22492/ije.10.1.04 [↗](https://doi.org/10.22492/ije.10.1.04)
30. Żammit, J. (2022b). Mixed methods to study the interlanguage of adult learners of Maltese as a second language. Asian Journal of Social Science and Management Technology, 4(3), 79–93. [find](https://scholar.google.com/scholar?q=%C5%BBammit%2C+J%2E+%282022b%29%2E+Mixed+methods+to+study+the+interlanguage+of+adult+learners+of+Maltese+as+a+second+language%2E+Asian+Journal+of+Social+Science+and+Management+Technology%2C+4%283%29%2C+79%E2%80%9393%2E)

*Updated 23 October 2026*