Neural Networks in Education: Building Simple Models for Learning Tasks

Artificial intelligence and neural networks are transforming modern education, but understanding how they work behind the scenes is essential for applying them effectively. This course guides you from fundamental terminology to constructing and evaluating your own basic neural network models tailored for learning contexts. What you'll learn: - Understand essential AI terminology, neural network architectures, and training mechanisms. - Build simple neural networks to process educational data using clear code examples. - Apply machine learning logic to common learning tasks like automated grading assistance and student performance tracking. - Explore modern concepts such as basic prompt engineering and intelligent tutoring models. - Evaluate model accuracy and effectiveness using standard assessment techniques. You will begin with key definitions, learning how artificial neurons, weights, and activation functions function in theory. Through structured text lessons and practical code walkthroughs, you will progress toward building and testing functional models step by step. Designed specifically for educators, academic staff, and beginners with no prior machine learning background. Start reading today to build practical confidence with AI in education.