This paper explores the application of Bloom’s Taxonomy in the context of Competency-Based Computing Education and the integration of Artificial Intelligence (AI). The key levels of Bloom’s Taxonomy are explained, and its development within modern education systems is examined. The study also analyzes how information is processed within the framework of the Semiotic Ladder model and explores possible ways to connect this model with AI technologies. The application of AI in education is considered from both positive (personalized learning, adaptive learning, data-driven decision-making) and negative (decline in critical thinking, weakening teacher-learner relationships, ethical concerns) perspectives. The main findings of the paper suggest that integrating Bloom’s Taxonomy with AI-based learning models can significantly contribute to the personalization of education and competency-based assessment. At the same time, strategies such as enhancing critical thinking skills, addressing ethical issues, and strengthening teacher collaboration with AI are recommended to mitigate AI’s negative impacts. This research introduces new approaches in computing education, pedagogical technologies, and learning outcome optimization, offering practical recommendations for the more effective integration of AI and educational methodologies in the future.Computing Education and the integration of Artificial Intelligence (AI). The key levels of Bloom’s Taxonomy are explained, and its development within modern education systems is examined. The study also analyzes how information is processed within the framework of the Semiotic Ladder model and explores possible ways to connect this model with AI technologies. The application of AI in education is considered from both positive (personalized learning, adaptive learning, data-driven decision-making) and negative (decline in critical thinking, weakening teacher-learner relationships, ethical concerns) perspectives. The main findings of the paper suggest that integrating Bloom’s Taxonomy with AI-based learning models can significantly contribute to the personalization of education and competency-based assessment. At the same time, strategies such as enhancing critical thinking skills, addressing ethical issues, and strengthening teacher collaboration with AI are recommended to mitigate AI’s negative impacts. This research introduces new approaches in computing education, pedagogical technologies, and learning outcome optimization, offering practical recommendations for the more effective integration of AI and educational methodologies in the future.