A Mixed User-Centered Approach to Enable Augmented Intelligence in Intelligent Tutoring Systems: The Case of MathAIde app
🤖 AI Summary
This study addresses three key challenges in AI in Education (AIED): diminished teacher agency, limited reliability of AI tools, and inequitable access to technological resources. To tackle these, we propose a teacher-centered Augmented Intelligence (AuI) framework. We designed, developed, and empirically evaluated MathAIde—an intelligent tutoring system leveraging computer vision and AI to enable teachers to upload photos of students’ handwritten mathematics assignments, automatically generate grading feedback and optional remediation suggestions, and retain full authority over feedback decisions. Employing high-fidelity prototyping, A/B testing, and real-classroom case studies—integrated with participatory design and iterative validation—we demonstrate that MathAIde significantly improves teachers’ feedback efficiency and instructional interaction quality, while exhibiting strong usability and adoption potential in resource-constrained settings. Its core contribution lies in embedding the AuI paradigm throughout the ITS design lifecycle, positioning teachers as empowered decision-makers in AI-augmented instruction—offering a reusable design framework and empirical foundation for deploying equitable, teacher-centric educational AI systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing learning with AI in Intelligent Tutoring Systems
Addressing teacher roles and AI reliability in education
Improving accessibility and usability of AIED in classrooms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computer vision and AI for math exercise correction
Teacher-centered design in all development phases
A/B testing and real-world classroom case studies
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