Date
Publisher
arXiv
Integrating Artificial Intelligence in Education (AIED) aims to enhance
learning experiences through technologies like Intelligent Tutoring Systems
(ITS), offering personalized learning, increased engagement, and improved
retention rates. However, AIED faces three main challenges: the critical role
of teachers in the design process, the limitations and reliability of AI tools,
and the accessibility of technological resources. Augmented Intelligence (AuI)
addresses these challenges by enhancing human capabilities rather than
replacing them, allowing systems to suggest solutions. In contrast, humans
provide final assessments, thus improving AI over time. In this sense, this
study focuses on designing, developing, and evaluating MathAIde, an ITS that
corrects mathematics exercises using computer vision and AI and provides
feedback based on photos of student work. The methodology included
brainstorming sessions with potential users, high-fidelity prototyping, A/B
testing, and a case study involving real-world classroom environments for
teachers and students. Our research identified several design possibilities for
implementing AuI in ITSs, emphasizing a balance between user needs and
technological feasibility. Prioritization and validation through prototyping
and testing highlighted the importance of efficiency metrics, ultimately
leading to a solution that offers pre-defined remediation alternatives for
teachers. Real-world deployment demonstrated the usefulness of the proposed
solution. Our research contributes to the literature by providing a usable,
teacher-centered design approach that involves teachers in all design phases.
As a practical implication, we highlight that the user-centered design approach
increases the usefulness and adoption potential of AIED systems, especially in
resource-limited environments.
What is the application?
Who is the user?
Who age?
Why use AI?
