Reading in the digital age: Opportunities of artificial intelligence in teaching German as a foreign language

Abstract

Purpose. The article examines transformative approaches to teaching reading in German as a Foreign Language (Deutsch als Fremdsprache – DaF) in the context of the digitalization of education. It analyzes the use of AI and the LATILL platform (Level-Adequate Texts in Language Learning) for implementing differentiated reading instruction. The main focus is placed on the concepts of differentiation, individualization, and personalization of learning as key strategies for working in heterogeneous groups.

Methodology. This research utilizes theoretical analysis to define differentiation and personalization, alongside PRISMA thematic synthesis and Biblioshiny bibliometric mapping to visualize research trends. Methodological application includes pedagogical modeling for group profiling and task planning, complemented by the case method to analyze CEFR-adapted texts.

Results. Based on a bibliometric analysis of 129 publications in the Scopus database and a meta-analysis of seven empirical studies conducted in 2017–2024, the high effectiveness of differentiated instruction has been demonstrated, showing improvements in reading comprehension and grammar with a pooled mean difference of 2.92. The role of artificial intelligence and specialized digital platforms, particularly the LATILL platform, in automating the creation of level-appropriate texts corresponding to students’ knowledge according to the CEFR scale is analyzed. The methodology for creating a class profile to optimize the selection of learning resources is determined. The results of the study demonstrate that the use of AI allows the teacher to move from the role of a transmitter of knowledge to the role of a facilitator, providing each learner with relevant content and support. Cases of adapting materials “Berlin / Vienna” for different levels of language proficiency were demonstrated.

Conclusions. The digital age demands replacing unified instruction with differentiated, individualized, and personalized pathways. Empirical data confirms that differentiated instruction significantly outperforms traditional methods, yielding a 2.92 pooled mean difference in language proficiency. Tools like LATILL and AI automate CEFR-aligned text creation, enabling teachers to shift from transmitters to facilitators. While differentiation and individualization adjust methods and pace, personalization allows students to co-create their learning experience based on personal interests. Ultimately, AI is a supportive resource that strengthens pedagogy.