Date
Publisher
arXiv
The growing ubiquity of artificial intelligence (AI), in particular large
language models (LLMs), has profoundly altered the way in which learners gain
knowledge and interact with learning material, with many claiming that AI
positively influences their learning achievements. Despite this advancement,
current AI tutoring systems face limitations associated with their reactive
nature, often providing direct answers without encouraging deep reflection or
incorporating structured pedagogical tools and strategies. This limitation is
most apparent in the field of mathematics, in which AI tutoring systems remain
underdeveloped. This research addresses the question: How can AI tutoring
systems move beyond providing reactive assistance to enable structured,
individualized, and tool-assisted learning experiences? We introduce a novel
multi-agent AI tutoring platform that combines adaptive and personalized
feedback, structured course generation, and textbook knowledge retrieval to
enable modular, tool-assisted learning processes. This system allows students
to learn new topics while identifying and targeting their weaknesses, revise
for exams effectively, and practice on an unlimited number of personalized
exercises. This article contributes to the field of artificial intelligence in
education by introducing a novel platform that brings together pedagogical
agents and AI-driven components, augmenting the field with modular and
effective systems for teaching mathematics.
What is the application?
Who is the user?
Who age?
Study design
