Lindner A (2025)
Publication Language: English
Publication Type: Thesis
Publication year: 2025
URI: https://open.fau.de/items/7dcfb2a7-b3f4-44ec-a562-93651baf6b19
Currently, everyone is talking about artificial intelligence (AI) and constant new technological innovations in this area demonstrate that AI technologies have the potential to significantly change society in the near future. This dissertation systematically addresses how transformative, i. e. society-changing, computer science (CS) topics such as artificial intelligence can be successfully integrated into K–12 computer science education (CSE). In this context, it pursues the goal of developing a general model of such an integration process and of identifying challenges in this process. To this end, it analyses and helps shape the introduction of the topic of AI into the Bavarian computer science curriculum.
First, in the course of defining the theoretical background, fundamental areas, approaches, concepts and functional principles of artificial intelligence are outlined to show which subject-related concepts need to be taught as part of AI education and which aspects the subject area encompasses. Furthermore, the current state of research regarding general models and frameworks for AI education is analysed. It becomes apparent that the existing educational approaches to AI focus on individual components, such as the development of AI competencies, but that a holistic view of the topic, which includes all actors and aspects of the educational context, as well as critical reflections on the educational challenges of the topic of AI are still missing. To address this research gap, the characteristics of transformative topics that contribute to the emergence of challenges when considering such topics in education are identified and, based on the Model of Educational Reconstruction for Computer Science Education (MER-CS) by Diethelm et al. (2011), a model is designed that identifies and describes the actors and thematic layers of AI relevant in educational settings. In its context, the central role of qualifying teachers for the successful integration of this topic into the classroom becomes clear. However, to date, this aspect has only been addressed to a limited extent in research.
Subsequently, various model dimensions are analysed to both gain AI-related educational insights into these aspects and to identify challenges that arise in relation to them as part of the integration process. Two studies analyse the preconceptions of students on the topic of AI. In addition, methods for teaching AI concepts to students are investigated by conceptualising and exploring two Unplugged learning materials. In a further step, the ideas and goals of teachers regarding the topic of AI are analysed and teachers’ expectations of professional development (PD) courses (on the topic of AI) are surveyed.
Based on the findings gathered in these analyses, a PD programme for teachers on the topic of AI that follows a flipped classroom (FC) approach is developed in three iterative cycles using design-based research (DBR). As part of the development process, design principles for conceptualising successful PD programmes are excerpted from the literature, a content framework for the programme and learning objectives are defined, and these aspects are further developed and supplemented over the design iterations. The main objectives of the programme are to provide computer science teachers with a solid knowledge of the subject and educational knowledge of artificial intelligence, to present teaching materials, to encourage their exploration and critical reflection and to initiate a thorough engagement with the curriculum on the subject. As part of the development, the requirements and needs of the teachers regarding the design of such a PD programme, as well as their knowledge development, are examined. The knowledge gained is finally generalised so that it can be applied to other transformative topics.
As a last step, a process model is conceptualised based on the knowledge gained from the investigations and the Model of Educational Actors and Layers in the field of AI developed at the beginning. This model describes and systematises the necessary steps for a successful integration process of transformative topics in K–12 CS education. In the context of the model, the identified challenges of integrating transformative topics into computer science education and potential ways of addressing them are summarised.
APA:
Lindner, A. (2025). Transformative Topics in K–12 CS Education: Characteristics, Facets, Challenges and Implications – An Exemplary Analysis of the Topic of Artificial Intelligence (Dissertation).
MLA:
Lindner, Annabel. Transformative Topics in K–12 CS Education: Characteristics, Facets, Challenges and Implications – An Exemplary Analysis of the Topic of Artificial Intelligence. Dissertation, 2025.
BibTeX: Download