Teaching neural networks at the secondary level is frequently impeded by the conceptual gap between everyday intuition and implementation-level code. This paper presents The Neural Bridge, a four-lesson instructional sequence for K-12 students that traverses three levels of abstraction: an embodied unplugged simulation using physical cords and carabiners (''7 Little Neurons''), a manual computation phase in Google Sheets, and a professional implementation in Google Colab. The sequence integrates Productive Failure [2] and the PRIMM framework [9] as its pedagogical scaffolding, and is grounded in Long and Magerko's [8] seventeen AI literacy competencies. Delivered to 27 students across Grades 10–12 in December 2025, a pre-/post-study showed that correct identification of AI as a data-processing system increased from 63% to 82%. Notably, students' self-assessed AI understanding decreased (M=2.70 to M=2.00), a positive outcome interpreted as metacognitive growth consistent with the Dunning-Kruger effect. Qualitative feedback identified the unplugged activity as the most impactful element.

This poster presents a practical model for transatlantic collaboration in computer science education through a blended mobility program between Karlsruhe University of Education (Germany) and Northeastern Illinois University (USA). The program funded by the European Union (Erasmus+) combines virtual and physical mobility phases, enabling students to participate in collaborative courses such as Physical Computing and Cybersecurity without interrupting their home semester. We share insights from the program's first implementation cycle, including student feedback and preliminary data on learning outcomes. This model demonstrates how EU-US cooperation can effectively support practical, hands-on computer science courses in a blended learning environment. Our work highlights challenges and best practices for designing sustainable, international blended mobility programs in computer science teacher education, offering valuable implications for institutions seeking to foster global collaboration in computer science education.

This research examines the promotion of algorithmic thinking - a future skill for K-12 students to understand the digital world - through AI education and block-based programming tools like Scratch. With the increasing emphasis on computational thinking, it is now included in many school curricula. AI tools, such as the Teachable Machine, open new avenues for improving computational and algorithmic problem-solving skills, although the most effective classroom methods for developing these skills remain under-researched. Through a mixed-methods case study, this research explores the development of algorithmic thinking, identifies failures in students' problem-solving processes, and examines the structure of problem-solving within a workshop designed around the productive failure approach. This workshop, conducted by pre-service teachers in a Computer Science teaching-learning lab, uses AI in Scratch to promote algorithmic thinking. Following a productive failure approach, students attempt to solve problems before receiving targeted instruction (PS-I). The goal is to provide students with a better understanding and learning environment that enhances their algorithmic thinking skills. The sample consists of N = 23 secondary school students. Their algorithmic skills were assessed before and after their participation. N = 7 groups (14 students) of them are recorded and the solving process within the steps of algorithmic thinking and the productive failures made are analyzed by means of qualitative content analysis. The results suggest that a short workshop is not sufficient to develop algorithmic thinking, with most failures occurring in the algorithm development phase. This study provides insights into the design of instructional settings that better support algorithmic thinking skills.

In today's technological age, algorithmic thinking and problem-solving skills are essential. To foster these skills, educators need accessible teaching methods for effective lesson planning. This study presents a teaching approach that employs the theory of the semantic wave to connect abstract concepts with practical applications, with the aim of improving students' algorithmic thinking and digital skills while promoting deeper learning. Tested in a CS teaching-learning lab, pre-service teachers practiced teaching K-12 students and refined their methods for teaching computer science. While previous studies have shown promise, this research advances the approach by integrating Necessity Learning Design (NLD), a method that builds on students' prior knowledge through structured tasks, improving problemsolving skills and significantly increasing algorithmic thinking. This “solve first, teach later” approach aligns with the principles of productive failure, where students initially attempt to solve problems on their own, encountering challenges that prepare them for deeper learning when guidance is later provided. The novelty of this work lies in the application of semantic wave theory with the Necessity Learning Design, which was not explored in CS education so far. Results provide valuable insights for pre-service computer science teachers and demonstrate the potential of this pedagogical approach to enrich both conceptual understanding and practical skills for the challenges of digital transformation.

The objective of this study is to develop and empirically evaluate an educational model that enhances algorithmic thinking - a key element of computational literacy - through the application with the concept of so called semantic waves for advancing K-12 students' digital proficiency. The concept of a semantic wave refers to the process of moving between abstract, theoretical knowledge and concrete, practical examples to create deeper understanding and learning. Considering this, our proposed model in the field of algorithmic thinking is intended to support pre-service computer science teachers and educators in designing instructional processes that are easy to implement and facilitate swift planning and reflection for K-12 computer science education. Initial results indicate promising outcomes but also suggested areas for enhancement. This research furthermore delves into refining the model through the incorporation of notional machines and the computational action approach for improving the training of future computer science teachers and students for the challenges of digital transformation.

Teaching algorithmic thinking and programming is an important competency for future informatics teachers to acquire. To encourage this, we are investigating the development of a teaching-learning structure that combines the concept of semantic waves and algorithmic thinking with integrating block-based languages. In our teaching-learning laboratory for informatics (TLL), pre-service informatics teachers develop workshops based our concept, evaluate them with school classes and reflect on their didactic learning. Our concept is evaluated by students’ algorithmic thinking and their perception of the semantic wave using mixed methods. This poster presents one workshop based on our teaching-learning structure and its research procedures.