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February 22, 2026Open Access

Personalized Language Learning with AI and Large Language Models: Tailoring Instruction to Learner Prerequisites

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Authors

SZSaima ZamanNKNahid Awad KheriMAMehreen Andleeb

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Overview

Analysis demonstrates effective instructional alignment in AI-supported language learning, highlighting learner readiness.

Key Points

  • This research aims to redefine personalization in language education by emphasizing learner prerequisites and instructional readiness.
  • Introduced a prerequisite-centered framework for language learning personalization.
  • Analyzed various AI-supported approaches to language learning, focusing on instructional tailoring.
  • Evaluated the effectiveness of existing systems in aligning instruction with learner readiness.
  • Contemporary AI systems often lack principled mechanisms for instructional alignment.
  • The proposed framework reveals gaps in aligning instructional decisions with learners' developmental preparedness.
  • The analysis suggests improvements for AI tools to support tailored language learning.

Cite This Study

Zaman et al. (2026) studied this question.

synapsesocial.com/papers/699a9e20482488d673cd49a0https://doi.org/10.5281/zenodo.18708792
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Artificial intelligence-driven personalized language learning: Customizing content and feedback to learners’ needs and proficiency levels2025
  2. 2Personalized Language Education in the Age of AI: Opportunities and Challenges2024 · 8 citations
  3. 3Personalizing Second Language Learning: Integrating AI with Learner Preference, Proficiency, and Engagement2026
  4. 4A Review of AI-Powered Personalized Language Learning2026
  5. 5Natural language processing in language learning: Leveraging artificial intelligence for personalized and adaptive English teaching2025