The growing integration of artificial intelligence (AI) into education has created new opportunities for developing personalized learning environments that address learners’ cognitive and motivational needs, particularly those of gifted students. Traditional approaches to teaching Python programming may not adequately respond to the individual differences, need for cognitive challenge, and motivational requirements of gifted learners, despite the increasing importance of Python in digital literacy, artificial intelligence, and data science. The present study aims to design and validate an artificial intelligence–based learning program and examine its effectiveness in enhancing the academic motivation of gifted lower secondary school students in Python programming. The study will employ a mixed-methods approach with an exploratory sequential design. In the qualitative phase, a systematic review of the literature and thematic analysis will be conducted to identify the key components, strategies, and tools required for developing the learning program. The preliminary program will then be validated by experts in educational technology, artificial intelligence, and programming. In the quantitative phase, the effectiveness of the developed program will be examined using a quasi-experimental pretest–posttest control-group design. The intervention will be implemented through a six-session blended learning program incorporating AI-based tools such as ChatGPT, Gemini, Google Colab, and intelligent programming assistants. Academic motivation will be assessed using the Academic Motivation Scale developed by Vallerand and colleagues, and the collected data will be analyzed using descriptive statistics and analysis of covariance.