Individualization of foreign language training of university students using digital educational technologies

This study develops an integrative framework to advance individualized foreign language instruction through incorporation of digital technologies. As online platforms and adaptive learning systems proliferate, new opportunities emerge to tailor teaching to individual learner needs. However, prior research reveals gaps in existing assessment capabilities, and grounding in language acquisition theory. Many tools target only limited skills in isolation. An original conceptual model is proposed to address these limitations synthesizing adaptive sequencing, multidimensional evaluation, instructor guidance, and established pedagogical principles into a comprehensive system. A literature review analyzes prior empirical findings, frameworks, and best practices. The proposed model incorporates these insights around key elements: 1) adaptive learning platforms that adjust content based on ongoing diagnostics; 2) multifaceted assessment of linguistic and communicative competencies; 3) instructor access to analytics dashboards and customization tools; 4) overarching language acquisition theory and goals. This creates a cycle of personalized instruction, human-AI collaboration, and continuous improvement. Significance includes providing researchers an integrative framework to guide future innovation and empirical testing. For practitioners, it offers a roadmap to harness technologies for more effective language education accessible to all learners. Recommendations are presented for interdisciplinary research and purposeful implementation. This pioneering model establishes theoretical foundations for the next generation of equitable, empowering personalized language learning systems.

personalized learning, adaptive learning systems, intelligent tutoring systems, computer-assisted language learning, language assessment, educational technology