Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education

The rise of AI in education offers exciting new ways to support student learning, especially in complex fields like programming. But simply using AI to generate feedback isn’t enough. To truly help students, we need to ground AI-driven tools in solid teaching principles. In our recent research, we explored how metacognition – the ability to understand and manage one’s own learning – can be used to create more effective AI-assisted programming education. We developed an AI hint system designed around the metacognitive phases of planning, monitoring, and evaluation, and studied how students used it in a real-world course. Why Metacognition … Continue reading Plan More, Debug Less: Applying Metacognitive Theory to AI-Assisted Programming Education

Transforming everything to vectors with Deep Learning: from Word2Vec, Node2Vec, to Code2Vec and Data2Vec

Let us discuss the state-of-the-art methods for transforming every kind of input data into fixed-length vectors of continuous values, including Word2Vec, Doc2Vec, Image2Vec, Node2Vec, Edge2Vec, Code2Vec, and Data2Vec. Continue reading Transforming everything to vectors with Deep Learning: from Word2Vec, Node2Vec, to Code2Vec and Data2Vec