Arseam

International Journal of Advances in Engineering & Scientific Research

GEN AI – ENABLED MENTORSHIP AND INTERNSHIP SUPPORT PLATFORM

E. Aksha P.R, Bavin B, Hariharan M & Sivasundharraj K

Mrs. E. Aksha Pricilda Reena, Assistant Professor, Department of Information Technology, Kongunadu College of Engineering and Technology, Trichy

Bavin B, B.Tech IT, Kongunadu College of Engineering and Technology, Trichy

Hariharan M, B.Tech IT, Kongunadu College of Engineering and Technology, Trichy.

Sivasundharraj K, B.Tech IT, Kongunadu College of Engineering and Technology, Trichy

DOI : https://doi.org/ 10.5281/zenodo.19429479 Page No : 88-97

Published Online : 2026-04-05

Download Full Article : PDF Check for Updates

ABSTRACT

The growing need to have individualized career advice and internship programs has revealed the shortcomings of the traditional mentorship and placement assistance frameworks that tend to be manual, piecemeal, and hard to scale. The paper will recommend a Generative AI-Powered Mentorship and Internship Support Platform to overcome these obstacles by combining Artificial Intelligence, Natural Language Processing, and full-stack application development to create career mentoring and internship support platforms that are all smart enough to be realistic and relevant nowadays. The system uses a chatbot powered by GenAI to deliver automated advice, resume and skills analysis, mentor placement, and individualized internship and job recommendations. The modern AI models carry out profile analysis, skill verification, mentor-mentee matching, and opportunity ranking according to relevance and user requirements. Data privacy and system integrity are guaranteed by means of secure authentication, role-based access control, and token management. Cloud-based deployment is scalable, highly available, and available 24/7 to a multiuser, multi-server environment. The experimental assessment shows better response efficiency, user engagement, and minimal manual intervention as compared to traditional mentorship methods. The suggested platform will facilitate inclusive, scaled, and intelligent career development in accordance with the current digital education and workforce preparedness goals.

Keywords: Generative Artificial Intelligence, Mentorship Platform, Internship Recommendation System, Natural Language Processing, AI Chatbot, Skill Analysis, Career Guidance, Role-Based Access Control, Cloud Computing, Automated Decision Support.

References:

[1] P. Chomphooyod, L. Jeerapradit, A. Suchato, and P. Punyabukkana, “Multi-Agentic AI for Automatic Course Syllabus Generation using LangGraph,” Proceedings of the 10th International STEM Education Conference (iSTEM-Ed), IEEE, 2025.

[2] A. Khamis, “Agentic AI Systems: Architecture and Evaluation Using a Frictionless Parking Scenario,” IEEE Access, vol. 13, 2025.

[3] A. Rani K. P. and G. S. Gowrishankar, “Agentic AI for Pathogen-Based Plant Disease Detection,” Proceedings of the 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC), IEEE, 2025.

[4] W. Zheng, S. Lu, Z. Cai, R. Wang, L. Wang, and L. Yin, “PAL-BERT: An Improved Question Answering Model,” Computer Modeling in Engineering & Sciences, vol. 139, no. 3, pp. 2729–2745, 2024.

[5] S. Saravanan, K. Muthumanickam, N. Subha and P. C. S. Mahesh, “Diagnosing Alzheimer Disease Using MRI Scan: A Deep Learning Approach,” 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2024, pp. 1047–1052, doi: 10.1109/ICSCSS60660.2024.10624909.

[6] Z. Epstein, A. Hertzmann, M. Akten, H. Farid, J. Fjeld, M. R. Frank, M. Groh, L. Herman, and N. Leach, “Art and the Science of Generative AI,” Science, vol. 380, no. 6650, pp. 1110–1111, 2023.

[7] M. R. Patel, S. V. Rao, and J. Chen, “Multi-Agent Reinforcement Framework for Adaptive Learning Management Systems,” IEEE Access, vol. 11, pp. 105980–105992, 2023.

[8] F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion Models in Vision: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, no. 1, pp. 1–20, Nov. 2023.

[9] “Generative AI: Education in the Age of Innovation,” Forbes Technology Council, Jan. 6, 2023. [Online]. Available: Forbes website.

[10] K. Baskar, P. Vijayalakshmi, K. Muthumanickam and A. Arthi, “A Novel Authentication and Access Scheduling Scheme to Improve the Performance of WSN,” Neural Network World, vol. 33, no. 4, pp. 205–224, 2023, doi: 10.14311/nnw.2023.33.013.

[11] K. Selvi, K. Muthumanickam and P. Vijayalakshmi, “ECC-Reliant Secure Authentication Protocol for Cloud Server and Smart Devices in IoT,” The Journal of Supercomputing, vol. 79, pp. 12191–12218, 2023, doi: 10.1007/s11227-023-05135-0.

[12] P. C. Senthil Mahesh, K. Muthumanickam and P. Vijayalakshmi, “Implicit Spatio-Temporal Based Hybrid Recommendation Model to Discover Malicious Wireless Access Points,” Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7821–7831, 2023.

[13] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical Text-Conditional Image Generation with CLIP Latents,” arXiv:2204.06125, 2022.

[14] D. Saxena and J. Cao, “Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–42, Apr. 2022.

[15] Bharathi, V., Monikavishnuvarthini, A., Dhakne, A., & Preethi, P. (2023). AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT. Meas. Sens, 25, 100614.

[16] Preethi, P., Swathika, R., Kaliraj, S., Premkumar, R., &Yogapriya, J. (2024). Deep learning–based enhanced optimization for automated rice plant disease detection and classification. Food and Energy Security, 13(5), e70001.

[17] Ansari, S. A., & Zafar, A. (2023, March). A comprehensive study on video captioning techniques, benchmark datasets and QoS metrics. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1598-1603). IEEE.

[18] Avacharmal, R., Balakrishnan, A., Ranjan, P., Vandanapu, M. K., Mulukuntla, S., & Preethi, P. (2024, June). Leveraging reinforcement learning for advanced financial planning for effective personalization in economic forecasting and savings strategies. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-4). IEEE.

[19] Wajid, M. A., Ansari, S. A., Luqman, M., Siddiqui, M. K., & Wajid, M. S. (2025). GSR‐C2N: Graph Feature Extracted Spar‐Raven Optimized CNN Based Crypto Mining Framework. Concurrency and Computation: Practice and Experience, 37(15-17), e70146.

[20] Ansari, S. A., Ali, S., Luqman, M., & Ahmad, S. (2025, March). Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions. In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 94-99). IEEE.

[21] Pandey, D., Jain, V., & Ansari, S. A. (2024, September). Adaptability of Blockchain in Wireless Sensor Networks: Challenges and Future Scope. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 653-659). IEEE.

[22] Ranaweera, J. (2025) ‘Artificial Intelligence Meets Silicon: The Next Era of Chip Design and Manufacturing’, IEEE Electron Devices Magazine, vol. 3, no. 1, pp. 45–59.

Cite this Article as: E. Aksha P.R, Bavin B, Hariharan M & Sivasundharraj K (2026), “Gen AI – Enabled Mentorship and Internship Support Platform”, International Journal of Advances in Engineering & Scientific Research, Volume 13, Issue 1, 2026, pp 88-97. DOI : https://doi.org/ 10.5281/zenodo.19429479

Article View: 9
PDF Download: 3

References:

[2] A. Khamis, “Agentic AI Systems: Architecture and Evaluation Using a Frictionless Parking Scenario,” IEEE Access, vol. 13, 2025.

[4] W. Zheng, S. Lu, Z. Cai, R. Wang, L. Wang, and L. Yin, “PAL-BERT: An Improved Question Answering Model,” Computer Modeling in Engineering & Sciences, vol. 139, no. 3, pp. 2729–2745, 2024.

[7] M. R. Patel, S. V. Rao, and J. Chen, “Multi-Agent Reinforcement Framework for Adaptive Learning Management Systems,” IEEE Access, vol. 11, pp. 105980–105992, 2023.

[9] “Generative AI: Education in the Age of Innovation,” Forbes Technology Council, Jan. 6, 2023. [Online]. Available: Forbes website.

[13] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical Text-Conditional Image Generation with CLIP Latents,” arXiv:2204.06125, 2022.

[14] D. Saxena and J. Cao, “Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–42, Apr. 2022.

[22] Ranaweera, J. (2025) ‘Artificial Intelligence Meets Silicon: The Next Era of Chip Design and Manufacturing’, IEEE Electron Devices Magazine, vol. 3, no. 1, pp. 45–59.

International Peer Reviewed & Refereed Journal as per UGC New Rules

ISSN (P) : 2349 –4824; ISSN (O) : 2349 –3607, UGC Approved as per UGC New Rules

Submission Instruction

Call for Paper

Submit Paper Online

Join Editorial Board

Author Guidelines

Impact Factor: 6.013

Indexed in

GEN AI – ENABLED MENTORSHIP AND INTERNSHIP SUPPORT PLATFORM

Aims and Objectives

Ethics Policy

Peer Review Policy

Call for Paper

Conference

Instruction To Review

Guide To Authors