Smart Collaborative Platform for Connecting Ideas with Skills (IDEAMATCH)

Abstract

In the present academic environment, the formation of efficient teams for technical projects is a major problem. Students with innovative ideas may not find suitable collaborators for the projects, while students with the required technical skills may not find appropriate projects where they can apply their skills. This research aims to develop an intelligent collaboration platform named IdeaMatch, which helps to bridge the gap by efficiently matching the project ideas with the required skills. To achieve this, the proposed platform utilizes web technologies along with Natural Language Processing techniques to analyze the project ideas and the skills of the users. Based on the analysis, the best collaborators for the projects can be determined. In this research, the proposed system design for the IdeaMatch platform is based on a three-tier architecture, where the front end is based on React, the back end is based on ASP.NET Core, and the database is based on MySQL. From the experimental results, it is clear that the time required for team formation can be reduced significantly using the proposed intelligent collaboration platform.

Introduction

IdeaMatch is a proposed web-based collaboration platform designed to improve how students form project teams in academic environments. It addresses the common problem where students with innovative ideas struggle to find suitable technical collaborators, while skilled students lack relevant projects to join.

The system uses AI-based skill matching to connect idea creators with students whose expertise, interests, and availability align with project requirements. Instead of relying on random selection or informal networks, it calculates compatibility scores to recommend suitable team members, improving efficiency and project success rates.

The platform is built using a three-tier architecture consisting of a user interface, application layer, and database. The application layer handles authentication and AI-driven matching, while the database stores user profiles and project data. Communication occurs through APIs, and the system is designed to be modular and scalable.

Development follows an iterative methodology combining Agile principles, including requirement analysis, system design, implementation, and testing. User feedback is used to refine performance and improve matching accuracy.

Conclusion

This research presents IdeaMatch, an intelligent collaboration platform designed to bridge the gap between innovative ideas and technical expertise among students. By combining modern web technologies with automated skill?matching mechanisms, the system offers an efficient approach to forming project teams within academic environments. The proposed architecture ensures scalability, security, and ease of use, making the platform suitable for deployment within universities and research communities. Future improvements may include advanced machine learning models for more accurate matching, mobile application support, and integration with institutional learning management systems. Overall, IdeaMatch demonstrates the potential of technology?driven collaboration systems in fostering innovation, improving project success rates, and strengthening student engagement in practical learning.

References

[1] G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529– 551, April 1955. [2] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68–73. [3] I. Sommerville, Software Engineering, 10th ed. Boston, MA, USA: Pearson Education, 2016. [4] R. S. Pressman and B. Maxim, Software Engineering: A Practitioner’s Approach, 8th ed. New York, NY, USA: McGraw-Hill Education, 2015. [5] T. H. Davenport and L. Prusak, Working Knowledge: How Organizations Manage What They Know. Boston, MA, USA: Harvard Business School Press, 2000. [6] J. D. Herbsleb and R. E. Grinter, “Architectures, coordination, and distance: Conway’s law and beyond,” IEEE Software, vol. 16, no. 5, pp. 63–70, Sept. 1999. [7] M. E. J. Newman, Networks: An Introduction. Oxford, U.K.: Oxford University Press, 2010. [8] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. Int. Conf. Learning Representations (ICLR), Scottsdale, AZ, USA, 2013. [9] J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, Jan. 2008. [10] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2020. [11] M. Fowler, Patterns of Enterprise Application Architecture. Boston, MA, USA: AddisonWesley, 2002. [12] E. Gamma, R. Helm, R. Johnson, and J. Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software. Boston, MA, USA: Addison-Wesley, 1994.

Copyright

Copyright © 2026 Mr. Nitin Thakre , Ms. Khushi Chaudhari, Mr. Faisal Hussain , Ms. Kiran Deshmukh , Ms. Pratiksha Asutkar , Ms. Diksha Bhalerao . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.