Implementing Learning Principles with a Personal AI Tutor: A Case Study
Ambroise Baillifard Maxime Gabella Pamela Banta Lavenex Corinna S. Martarelli
Effective learning strategies based on principles like personalization,
retrieval practice, and spaced repetition are often challenging to implement
due to practical constraints. Here we explore the integration of AI tutors to
complement learning programs in accordance with learning sciences. A
semester-long study was conducted at UniDistance Suisse, where an AI tutor app
was provided to psychology students taking a neuroscience course (N=51). After
automatically generating microlearning questions from existing course materials
using GPT-3, the AI tutor developed a dynamic neural-network model of each
student's grasp of key concepts. This enabled the implementation of distributed
retrieval practice, personalized to each student's individual level and
abilities. The results indicate that students who actively engaged with the AI
tutor achieved significantly higher grades. Moreover, active engagement led to
an average improvement of up to 15 percentile points compared to a parallel
course without AI tutor. Additionally, the grasp strongly correlated with the
exam grade, thus validating the relevance of neural-network predictions. This
research demonstrates the ability of personal AI tutors to model human learning
processes and effectively enhance academic performance. By integrating AI
tutors into their programs, educators can offer students personalized learning
experiences grounded in the principles of learning sciences, thereby addressing
the challenges associated with implementing effective learning strategies.
These findings contribute to the growing body of knowledge on the
transformative potential of AI in education.