TEAM-BASED FIND&FIX METHOD INTEGRATING GAMIFICATION AND ARTIFICIAL INTELLIGENCE IN TEACHING PYTHON

Abstract

This article examines the development of students’ soft skills in Python lessons through the integration of gamification and artificial intelligence (AI). The study provides both theoretical and practical justification for the effectiveness of incorporating game-based elements and AI systems into the learning process. The lessons were organized in a team-based format, where each group received “Find and Fix the Error” tasks. These tasks were automatically generated by an AI model and classified into three levels: tasks with an explicit error (Level A), tasks that produce a seemingly correct output despite containing a logical flaw (Level B), and tasks with a subtle, hardly detectable error (Level C). This structure encourages students not only to write code but also to analyze it, identify inconsistencies, and justify their solutions.

The study was conducted over four weeks with a class of 23 ninth-grade students. The findings demonstrated that combining AI with a team-based game method significantly improved students’ code-reading speed, error-detection accuracy, and the quality of intra-group communication. Moreover, students showed increased self-confidence and a stronger sense of mutual responsibility. From a psychological perspective, their perception of errors also shifted-from viewing mistakes as failures to understanding them as essential steps in cognitive growth.

Overall, the proposed method integrates the synergy of gamification and artificial intelligence, presenting an innovative model for teaching Python. This approach effectively fosters not only subject-specific learning outcomes but also creativity, communication, and broader metacognitive competencies.