Design and development of a Web Application to Improve Tutor Search for Organizations and Individuals : a web-based system for enhancing tutor discovery and scheduling efficiency
Derakhshan Alavijeh, Mahsa (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202504106191
https://urn.fi/URN:NBN:fi:amk-202504106191
Tiivistelmä
The design and development of a tutor-finding platform which address the gaps of the online tutoring availa-ble in the market by integrating advanced features such as AI recommendations, reservation system, and se-cure authentication. By prioritizing a design thinking approach, the platform enhances accessibility and effi-ciency for both students and tutors.
A mixed-methods research methodology was used, combining quantitative surveys and qualitative interviews to gather user insights and inform an iterative design process. Implementation is based on the MERN stack, to have a scalable platform, while OpenAI-powered tools enable personalized learning experiences and real-time feedback. Compared to existing solutions, the system emphasizes usability, security, and modular architecture. Key features include advanced search filters, tutor reviews, and AI-driven learning adaptation, all designed to improve user’s satisfaction.
Building a strong foundation, future iterations will focus on refining the user interface, integrating payment systems, and expanding AI capabilities to address evolving educational needs. The findings contribute to the field by presenting a scalable, intelligent, and secure framework for developing online tutoring platforms that bridge existing gaps in digital education.
A mixed-methods research methodology was used, combining quantitative surveys and qualitative interviews to gather user insights and inform an iterative design process. Implementation is based on the MERN stack, to have a scalable platform, while OpenAI-powered tools enable personalized learning experiences and real-time feedback. Compared to existing solutions, the system emphasizes usability, security, and modular architecture. Key features include advanced search filters, tutor reviews, and AI-driven learning adaptation, all designed to improve user’s satisfaction.
Building a strong foundation, future iterations will focus on refining the user interface, integrating payment systems, and expanding AI capabilities to address evolving educational needs. The findings contribute to the field by presenting a scalable, intelligent, and secure framework for developing online tutoring platforms that bridge existing gaps in digital education.