Abstract:

The significance of artificial intelligence (AI) is continuously growing across diverse domains of society. Grasping these approaches is increasingly recognized as a crucial skill in the 21st century. Notably, the methodology of AI diverges substantially from traditional algorithmic problem-solving, such as iterative programming. Consequently, there is a growing demand for educational initiatives in the field, specifically within computer science instruction.
This paper presents the development of modular instructional materials aimed at imparting fundamental concepts of AI to secondary school students in computer science classes. The focus lies on two distinct scenarios: the first scenario introduces the concept of clustering through the utilization of the K-Mean algorithm. Employing a playful and problem-oriented approach, students collaboratively devise solutions using the assistance of the small robot named "Kiu." Building upon this foundation, a more comprehensive scenario explores the functionality of an artificial neural network by employing an interactive learning video to recognize digits from zero to nine using a 16x16 pixel input field. Throughout the learning process, students receive continuous support via appropriate hints and tasks. Additionally, a complementary e-learning course guides students through various content, specifically tailored for computer science classes.
The developed learning units facilitate a gradual understanding of AI procedures, starting from simple problem solutions and progressing towards more complex ones. The provided instructional material empowers students to independently explore the subject matter within a gamified learning environment, guided at their own pace, while adopting a problem-oriented approach. This approach enables an effective organization of the topic through the incorporation of diverse learning methodologies. Initially, the material emphasizes the technical implementation and practical application of algorithms designed for AI applications. Eventually, it encompasses a holistic perspective by addressing the social implications of these advancements.
This offers students a constructive initiation into the realm of AI. It capitalizes on their foundational understanding of algorithms, commencing with the instruction of a conceptual approach wherein an algorithm generates a computational model for problem-solving based on input data. This approach leverages the students existing knowledge to facilitate comprehension. The modular structure of the instructional material empowers educators to selectively incorporate elements into their lessons, fostering flexible and personalized learning experiences. This not only facilitates the freedom to design the learning process but also ensures effective support through well-prepared didactic learning elements.

Keywords:

Artificial intelligence, e-learning scenarios, clustering, artificial neural networks, blended learning, computer science education.