Artificial Intelligence in Teaching German as Foreign Language: Exploring the Opportunities and Challenges of Digital Educational Assistants

Introduction
In recent years, the integration of Artificial Intelligence (AI) into foreign language education has increasingly gained attention. Technological advances, particularly in the development of large language models (LLMs), have made it possible to design language learning environments that are more adaptive, efficient, and responsive to learners’ individual needs. In the context of teaching German as a Foreign Language (Deutsch als Fremdsprache – DaF), AI offers innovative tools that support the learning process by providing immediate feedback, generating customized learning tasks, and enhancing assessment procedures. However, the pedagogical use of AI also raises critical questions concerning reliability, ethical implications, cultural sensitivity, and the limits of automated teaching and assessment. This study aims to explore the potential and challenges of employing AI-based tools in the DaF classroom. The central research question is: Can AI-based systems be effective tools in teaching German as a foreign language (DaF)? And do their benefits outweigh the drawbacks enough to justify their use in language education?
Background of the Study
Although AI tools such as ChatGPT, DeepL, and conversational bots have been increasingly applied in English language learning, their use in the field of German language instruction is still in its early stages. Preliminary studies suggest that AI can support learners by delivering corrective feedback, enhancing vocabulary acquisition, improving pronunciation, and offering personalized grammar exercises. Nonetheless, scholars also emphasize the importance of a critical perspective. Limitations include the risk of biased outputs, lack of intercultural competence, privacy concerns, and reduced human interaction. AI-generated content may be linguistically accurate but pragmatically or culturally inappropriate. Furthermore, overreliance on AI tools could reduce learners’ motivation to develop independent language competencies. Therefore, it is essential to investigate how AI can be responsibly integrated into language teaching and assessment practices without replacing the irreplaceable aspects of human instruction, such as empathy, adaptability, and pedagogical judgment.
Methodology
The study follows a triangulated mixed-methods approach, combining theoretical, empirical, and analytical perspectives. In the first phase, a systematic review of the literature was conducted using scholarly databases such as ERIC, Scopus, and Google Scholar to construct a conceptual framework. The second phase consisted of an experimental design involving 10 DaF learners at the B2 level. Participants completed open-ended speaking and writing tasks modeled after the TELC and ÖSD test formats. Their responses were independently evaluated by five native German-speaking DaF experts and one Persian-speaking DaF instructor using pre-defined assessment rubrics. These same responses were subsequently assessed by ChatGPT, following the same evaluation categories. The third phase involved a comparative analysis of the human and AI-generated assessments. Quantitative and qualitative data were analyzed using Microsoft Excel and visualized with Canva, enabling the identification of patterns, discrepancies, and strengths or weaknesses in both evaluation methods.
Conclusion
The findings of this study indicate that the integration of AI-based systems as instructional assistants in DaF is both recommendable and effective due to their facilitative, accelerative, and partially complementary functions. The use of such technologies significantly reduces the time required for the design, implementation, and evaluation of language assessments, while simultaneously enhancing learning quality and increasing the overall effectiveness of the teaching process. Nevertheless, the study underscores that the application of artificial intelligence in language education must remain within ethical, rational, and scientifically responsible frameworks. AI should not be regarded as a replacement for language teachers or human interaction, but rather as a valuable supplement within a constructive human-machine dynamic. In this model, AI can streamline the learning process, but the teacher remains the central figure in shaping, guiding, and supervising language acquisition. This interactive approach offers an optimal strategy for utilizing AI in digital-age language education.