Ayşe Alkan
This exploratory and design-oriented study investigates the feasibility and descriptive patterns of different types of cognitive load experienced by gifted students in a gamified AI-based learning environment. Using a single-group pretest–posttest design, the research involved 49 gifted students in the 2025–2026 academic year. Students trained an AI model by classifying planetary images on Machine Learning for Kids and used it in the Scratch-based gamified environment “Planet Detective.” The process included gamification elements such as points and levels. Data were collected with the “Different Types of Cognitive Load Scale.” Results indicated that the module enhanced achievement, balanced gender differences, and produced moderate-to-high intrinsic, low extraneous, and high effective cognitive load, suggesting a conceptually rich yet clear and effective learning experience.