Education Reform Based on DIKWP Artificial Consciousness Theory

Traditional education methods are increasingly challenged by the growing complexity of cognitive demands in artificial intelligence (AI). This paper introduces an innovative AI education framework based on the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) semantic framework, the “BUG” consciousness theory, and sovereign AI principles. By leveraging bidirectional semantic transformations and real-time feedback mechanism, the framework mitigates cognitive biases and limitations, optimizing personalized learning strategies. Using DIKWP Semantic Mathematics as an analytical tool, it identifies and corrects errors in students' learning paths, addressing the “three-no” problems-namely incompleteness, inconsistency, and imprecision-commonly encountered in traditional AI education methods. This framework not only provides a new theoretical foundation for AI education but also demonstrates practical applications in improving learning outcomes and student experiences through a semantic-driven feedback mechanism.