AI-BASED ADAPTATION OF GRADED TEXTS
DOI:
https://doi.org/10.5281/zenodo.23065427Аннотация
Graded readers have been a staple in the foreign-language classroom for decades with good reason - texts at an appropriate reading level are demonstrably more effective at facilitating lexical acquisition and comprehension. With modern artificial intelligence, any digital text can be automatically simplified, and one is left wondering just how useful such a text could be. Do AI-simplified texts offer similar benefits to a properly graded reader? Based on the studies reviewed here, the evidence suggests otherwise – often, because text simplification tools aim at a generic user, rather than one’s personal level of competence in the language. In this article, I will review studies on the pedagogy of comprehensible input, examine the mechanics and effectiveness of automatic text simplification in theoretical contexts, perform a quantitative and qualitative comparison between AI- and human-simplified texts from previous studies, and identify what separates the two as far as language learning is concerned - individual calibration against a generic level. I will also describe a research design that could test this hypothesis.Ключевые слова
artificial intelligence, text simplification, graded readers, reading competence, CEFR calibration, comprehensible input, EFL instructionБиблиографические ссылки
1. Al-Thanyyan, S. S., & Azmi, A. M. (2021). Automated text simplification: A survey. ACM Computing Surveys, 54(2).
2. Krashen, S. D. (1982). Principles and Practice in Second Language Acquisition. Oxford: Pergamon Press.
3. Laufer, B. (1989). What percentage of text-lexis is essential for comprehension? In C. Lauren & M. Nordman (Eds.), Special Language: From Humans to Thinking Machines (pp. 316–323). Clevedon: Multilingual Matters.
4. Li, G., Arase, Y., & Crespi, N. (2025). Aligning sentence simplification with ESL learner’s proficiency for language acquisition. arXiv preprint, arXiv:2502.11457.
5. Odo, D. M. (2022). Automatic text simplification: Efficacy in the foreign language classroom. Applied Linguistics.
6. Odo, D. M. (2025). Comprehensibility of AI-generated and human simplified texts for L2 learners. Language Learning & Technology, 29(1), 1–18.
7. Uchida, S., Takada, S., & Arase, Y. (2018). CEFR-based lexical simplification dataset. Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018).
8. Marzona, E., Goikhman, M., Palmero Aprosio, A., & Zancanaro, M. (2025). Exploring paraphrasing strategies for CEFR A1-level constraints in LLMs. Findings of the Association for Computational Linguistics: EMNLP 2025.
9. Multi-round simplification of complex sentences for language learners using large language models. (2025). arXiv preprint, arXiv:2511.04495. AKTABGACHA VA AKTABGACHA VA AKTAB TA’LIMI AKTAB TA’LIMI 13.00.00 Pedagogika fanlari 13.00.01 Pedagogika nazariyasi. Pedagogik ta’limotlar tarixi 13.00.02 Ta’lim va tarbiya nazariyasi va metodikasi (sohalar bo‘yicha) 13.00.03 Maxsus pedagogika 13.00.04 Jismoniy tarbiya va sport mashg‘ulotlari nazariyasi va metodikasi 13.00.05 Kasb-hunar ta’limi nazariyasi va metodikasi 13.00.06 Elektron ta’lim nazariyasi va metodikasi (ta’lim sohalari va bosqichlari bo‘yicha) 13.00.07 Ta’limda menejment 13.00.08 Maktabgacha ta’lim va tarbiya nazariyasi va metodikasi 13.00.09 Ijtimoiy pedagogika 07.00.00 Tarix fanlari 19.00.00 Psixologiya fanlari 01.00.00 Fizika-matematika fanlari 02.00.00 Kimyo fanlari 03.00.00 Biologiya fanlari 09.00.00 Falsafa fanlari 10.00.00 Filologiya fanlari 11.00.00 Geografiya fanlari AKTABGACHA VA AKTABGACHA VA AKTAB TA’LIMI AKTAB TA’LIMI Mas’ul muharrir: Ramzidin Ashurov
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