Building on our previous research on bottom-up (student-led) and top-down (instructor-led) approaches in aerospace engineering education, this paper presents an enhanced ontology-based reasoner that evaluates two distinct methodologies: logical consequences and word embeddings. The framework examines logical consequences' structured and rule-based query capabilities alongside word embeddings' natural language processing abilities as paths toward creating comprehensive educational tools. Our implementation demonstrates how these complementary approaches enhance educational outcomes: students benefit from personalized learning pathways and clear prerequisite relationships, while instructors gain tools for curriculum optimization and adaptation to emerging technologies. Through representative use cases, we show how these distinct approaches provide robust frameworks that balance precise, logical reasoning with flexible natural language understanding, ultimately advancing aerospace engineering education by serving both student and instructor needs.