STM Journals Peer-reviewed research from Consortium e-Learning Network

E-Commerce for Future & Trends Original Research

HirePrep: A Microservice-Based Integrated Placement Preparation Platform with AI Assistance

  1. Sumit Madaan Department of Computer Science and Engineering, JECRC University, Jaipur
  2. Rakshita Bhansali Department of Computer Science and Engineering, JECRC University, Jaipur
  3. Nagendra Singh Sisodiya Department of Computer Science and Engineering, JECRC University, Jaipur
  4. Lakshit Meena Department of Computer Science and Engineering, JECRC University, Jaipur
  5. Sonam Lowry Department of Computer Science and Engineering, JECRC University, Jaipur

Abstract

Preparing for campus placements can be a confusing and time-consuming process. Students have to use different platforms for things like practice tests, study materials, talking to people, and getting updates from the administration. This is not a waste of time, but it also makes it harder for students to be productive and clear about what they need to do when they are getting ready for their careers. To make things better, we made something called HirePrep. It is a platform that brings together all the things students need to do to get ready for campus placements. HirePrep has important parts, including tests, managing resources, tracking progress, sending notifications, and controls for the administration. We used React to make the front end of the platform look nice and work well. Django and Spring Boot are used for the end. We use PostgreSQL to keep all the data safe and sound. The way HirePrep is built makes it easy to add things and scale up. Each part of the platform works independently. The other parts are communicating with each other using special codes. This makes the platform more stable and able to handle a lot of users. We also added a chatbot that uses intelligence to give students help when they need it. The chatbot is really good at understanding what students are asking. Gives the right answers most of the time. HirePrep shows that using small services can work well in schools and universities. It also gives an example of how to build a platform that can be used by a lot of people.

Keywords

References (45)

  1. Balakrishnan S, Bargavi N. An in-depth review on campus recruitment and the challenges faced. International Journal of Indian Culture and Business Management. 2025;34(4):429-442. doi:10.1504/ijicbm.2025.145681
  2. Nickerson JV. Teaching the integration of information systems technologies. IEEE Transactions on Education. 2006;49(2):271-277. doi:10.1109/te.2006.873966
  3. Tang A, Avgeriou P, Jansen A, Capilla R, Ali Babar M. A comparative study of architecture knowledge management tools. Journal of Systems and Software. 2010;83(3):352-370. doi:10.1016/j.jss.2009.08.032
  4. Nadareishvili I, Mitra R, McLarty M, Amundsen M. Microservice Architecture: Aligning Principles, Practices, and Culture. Sebastopol (CA): O’Reilly Media; 2016.
  5. Velepucha V, Flores P. A Survey on Microservices Architecture: Principles, Patterns and Migration Challenges. IEEE Access. 2023;11:88339-88358. doi:10.1109/access.2023.3305687
  6. Alfehaid A, Hammami MA. Artificial Intelligence in Education: Literature Review on The Role of Conversational Agents in Improving Learning Experience. International Journal of Membrane Science and Technology. 2023;10(3):3121-3129. doi:10.15379/ijmst.v10i3.3045
  7. Mew L. Information systems education: The case for the academic cloud. Inf Syst Educ J. 2016;14(5):71–9.
  8. Das S, Dayal M. Exploring determinants of cloud-based enterprise resource planning selection and adoption. J Inf Technol Case Appl Res. 2016;18(1):11–36. doi:10.1080/15228053.2016.1160733.
  9. Riad AM, El-Ghareeb HA. A service oriented architecture to integrate mobile assessment in learning management systems. Turk Online J Distance Educ. 2008;9(2):200–19.
  10. Richardson C. Microservices Patterns: With Examples in Java. New York (NY): Simon & Schuster; 2018.
  11. Newman S. Building Microservices: Designing Fine-Grained Systems. Sebastopol (CA): O’Reilly Media; 2021.
  12. Yin Z, Liu J, Chen B, Chen C. A Delivery Robot Cloud Platform Based on Microservice. Journal of Robotics. 2021;2021:1-10. doi:10.1155/2021/6656912
  13. Lyu Z, Wei H, Bai X, Lian C. Microservice-Based Architecture for an Energy Management System. IEEE Systems Journal. 2020;14(4):5061-5072. doi:10.1109/jsyst.2020.2981095
  14. Winkler R, Soellner M. Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Proceedings. 2018;2018(1):15903. doi:10.5465/ambpp.2018.15903abstract
  15. Pérez JQ, Daradoumis T, Puig JMM. Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education. 2020;28(6):1549-1565. doi:10.1002/cae.22326
  16. Okonkwo CW, Ade-Ibijola A. Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence. 2021;2:100033. doi:10.1016/j.caeai.2021.100033
  17. Aldiab A, Chowdhury H, Kootsookos A, Alam F, Allhibi H. Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia. Energy Procedia. 2019;160:731-737. doi:10.1016/j.egypro.2019.02.186
  18. Bhamangol P, Ningappa B, Nandavadekar DV, Khilari P, Hanmant S. Enterprise resource planning system in higher education: A literature review. Int J Manag Res Dev. 2011;1(1):1–7.
  19. Fowler M, Lewis J. (2014). Microservices: A definition of this new architectural term [Online]. Martin Fowler. Thoughtworks. Scientific Research Publishing. Available from: https://martinfowler.com/articles/microservices.html
  20. Evans E. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston (MA): Addison-Wesley; 2004.
  21. Present and Ulterior Software Engineering. 2017. doi:10.1007/978-3-319-67425-4
  22. Di Francesco P, Malavolta I, Lago P. Research on architecting microservices: Trends and focus. 2017 IEEE International Conference on Software Architecture (ICSA), Gothenburg, Sweden. 2017. p. 21–30. doi:10.1109/ICSA.2017.24.
  23. Nygard M. Release It!: Design and Deploy Production-Ready Software. 2nd ed. Raleigh (NC): Pragmatic Bookshelf; 2018.
  24. Django Project. (2026). Django Software Foundation. [Online]. Django (The web framework for perfectionists with deadlines). Available from: https://www.djangoproject.com/
  25. Spring Boot. 4.0.5. [Online]. Spring. Available from: https://spring.io/projects/spring-boot
  26. Facebook Open Source. (2021). React. React – a JavaScript library for building user interfaces [Online] Meta Platforms, Inc. Available from: https://legacy.reactjs.org/
  27. Spring Cloud Gateway. (2017). Spring Cloud Gateway. 4.0.9. [Online]. Spring.io. Available from: https://docs.spring.io/spring-cloud-gateway/docs/current/reference/html/
  28. The PostgreSQL Global Development Group. (2026). PostgreSQL. [online] PostgreSQL. Available from: https://www.postgresql.org/
  29. Deshpande Y, Hansen S. Web engineering: creating a discipline among disciplines. IEEE Multimedia. 2001;8(2):82-87. doi:10.1109/93.917974
  30. Fielding RT. Architectural styles and the design of network-based software architectures [PhD thesis]. Irvine (CA): University of California; 2000.
  31. Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al. Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020:38-45. doi:10.18653/v1/2020.emnlp-demos.6
  32. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2019 Jun; Minneapolis (MN). Stroudsburg (PA): Association for Computational Linguistics; 2019. p. 4171–86. doi:10.18653/v1/N19-1423.
  33. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4–9; Long Beach (CA). Red Hook (NY): Curran Associates Inc.; 2017. p. 6000–10.
  34. Wilde E, Pautasso C. REST: From Research to Practice. New York (NY): Springer; 2011.
  35. Crockford D. The application/json Media Type for JavaScript Object Notation (JSON). 2006. doi:10.17487/rfc4627
  36. Masse M. REST API Design Rulebook: Designing Consistent RESTful Web Service Interfaces. Sebastopol (CA): O’Reilly Media; 2011.
  37. The OAuth 2.0 Authorization Framework. 2012. doi:10.17487/rfc6749
  38. Hu VC, Ferraiolo D, Kuhn R, Schnitzer A, Sandlin K, Miller R, et al. Guide to Attribute Based Access Control (ABAC) Definition and Considerations. 2014. doi:10.6028/nist.sp.800-162
  39. Jones M, Bradley J, Sakimura N. JSON Web Token (JWT). 2015. doi:10.17487/rfc7519
  40. Sheffer Y, Holz R, Saint-Andre P. Recommendations for Secure Use of Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). 2015. doi:10.17487/rfc7525
  41. Schneier B. Applied Cryptography: Protocols, Algorithms, and Source Code in C. New York (NY): John Wiley & Sons; 2007.
  42. Simmons G. Secure Communications And Asymmetric Cryptosystems. 2019. doi:10.4324/9780429305634
  43. Pahl C, Jamshidi P. Microservices: A Systematic Mapping Study. Proceedings of the 6th International Conference on Cloud Computing and Services Science. 2016:137-146. doi:10.5220/0005785501370146
  44. Bharti SK, Babu KS, Jena SK. Parsing-based Sarcasm Sentiment Recognition in Twitter Data. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. 2015:1373-1380. doi:10.1145/2808797.2808910
  45. Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems. Computer. 2009;42(8):30-37. doi:10.1109/mc.2009.263
Support