Natural Language Processing Applications in Teaching Persian as a Foreign Language
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Beschreibung
This book explores the application of Natural Language Processing (NLP) technologies to teaching Persian as a foreign language, with particular focus on Arabic-speaking learners. Drawing on empirical data from the AZFA Persian Learner Corpus at Ilam University, it demonstrates how computational methods can address longstanding pedagogical challenges in Persian language instruction. The work examines learner corpus development and annotation frameworks, presenting systematic approaches to categorizing morphological, syntactic, lexical, and orthographic errors specific to Persian interlanguage. It investigates both rule-based and machine learning approaches to automated error detection and correction, achieving F1 scores of approximately 0.82 with transformer-based models. The book discusses intelligent tutoring systems and adaptive learning pathways that personalize instruction based on individual learner profiles, showing significant improvements in grammatical accuracy (23% vs. 15% for traditional instruction) and learner satisfaction. It also addresses automated assessment of writing and speaking, ethical considerations including data privacy and algorithmic bias, and practical integration strategies for Persian language classrooms. Looking forward, the work examines emerging technologies such as transformer architectures and large language models while providing detailed recommendations for educators, researchers, and developers. Throughout, it emphasizes that NLP tools should complement rather than replace human instruction, supporting the development of comprehensive communicative competence while respecting learner autonomy and cultural diversity in Persian language education.
Autor*in
Amir Karimipour
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Spracherwerb Persich Linguistik