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Ahmed Alsharif, Utah Valley University, United StatesAbstract
This paper presents the design, implementation, and assessment of a student-led Artificial Intelligence (AI) tutoring project at Utah Valley University (UVU). The system, integrated within the Canvas Learning Management System, was created to provide real-time, personalized academic support while upholding educational integrity. Its design intentionally modeled the scaffolding practices of human tutors, guiding students through problem-solving rather than supplying direct answers. Development was carried out by a small team of undergraduate students in collaboration with faculty mentors, supported by institutional grants and technology resources. The result was a mobile-friendly, scalable tool capable of supporting multiple courses. A preliminary assessment was carried out during the pilot deployment, drawing on surveys, analytics, and student reflections. Findings showed high engagement, with students reporting that the system enhanced understanding of course content and encouraged active learning. Technical evaluation confirmed reliable performance, and the project demonstrates that student-driven innovations can create meaningful educational technologies that promote accessibility, engagement, and equity in higher education. The findings also highlight both the pedagogical benefits and implementation challenges of introducing AI into authentic classroom contexts, offering a roadmap for institutions seeking to integrate AI responsibly into teaching and learning.
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Conference: IICE2026Stream: Design
This paper is part of the IICE2026 Conference Proceedings (View)
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To cite this article:
Alsharif A. (2026) Design, Implementation, and Early Engagement Outcomes of a Student-Led AI Tutor in Higher Education ISSN: 2189-1036 – The IAFOR International Conference on Education – Hawaii 2026 Official Conference Proceedings (pp. 155-163) https://doi.org/10.22492/issn.2189-1036.2026.15
To link to this article: https://doi.org/10.22492/issn.2189-1036.2026.15
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