Designing Meaningful AI Learning Activities in Pre-Service Teacher Education

Abstract

This article examines how artificial intelligence can be meaningfully integrated into pre-service teacher education through a case study approach. Rather than treating AI as a general technological innovation or a universal tool for increasing efficiency, the article focuses on three concrete teaching interventions in the subject didactics of Czech language, mathematics, and biology. Each case was designed and implemented in university courses for future teachers and illustrates a different way in which AI can support students in professionally demanding areas where they often experience uncertainty, anxiety, or lower self-efficacy. The study is grounded in the assumption that the educational value of AI does not lie in the mere use of a digital tool, but in its integration into subject-specific and pedagogically meaningful activities. In Czech language education, AI supports work with authentic language data, particularly when formulating corpus queries, interpreting corpus results, and didactically transforming language material for school use. In mathematics, AI functions as a consulting partner in the design of empirical investigations and the statistical processing of data in students’ final theses, helping students understand the relationship between research questions, data, methods of analysis, and interpretation. In biology, AI-based applications facilitate the identification of plants and animals using visual and audio inputs, thereby allowing students to focus not only on correct species naming but also on observation, comparison, verification, and argumentation. Across the three cases, the analysis shows that AI can serve as didactic scaffolding. It lowers the entry barrier to complex activities that student teachers might otherwise avoid, while still requiring them to check, refine, interpret, and critically evaluate AI outputs. The aim is therefore not to replace expert judgement, but to make it more visible and to support its development. The cases also show that AI-supported activities become educationally meaningful only when they are clearly connected to future classroom practice and when students are guided to reflect on the limits and responsible use of AI. The article formulates common principles for designing AI-supported activities in teacher education: subject-didactic anchoring, orientation toward professionally demanding areas, critical work with AI outputs, explicit links to future work with pupils, development of AI literacy, and support for students’ professional confidence. In this sense, AI can contribute not only to technological competence but also to subject reasoning, pedagogical judgement, and the didactic transformation of knowledge.