AI can aid in identifying students who need help
North Carolina State University researchers have designed an artificial intelligence model that can predict how much students are learning in educational games.
In recent years, there has been growing interest in modeling student knowledge in adaptive learning environments. The task of predicting students’ future performance on a test based upon their past interactions with a learning environment.
By tailoring student experiences, helps students in adaptive learning environments, such educational games and intelligent tutoring systems.
The researchers’ improved AI model makes use of an Artificial Intelligence training concept called multi-task learning. It is an approach in which one model is asked to perform multiple tasks and could be used to improve both instruction and learning outcomes.
Jonathan Rowe, co-author and research scientist in CEI said that, we wanted the model to predict whether a student would answer each question on a test correctly, and the educational game called Crystal Island.
This is a game-based learning environment for middle-grade science and literacy that teaches students microbiology education.
This multi-task model was 10 percent more accurate than other models that relied on conventional AI training methods.
