IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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THE AI-BASED INTERNSHIP RECOMMENDATION ENGINE FOR PM INTERNSHIP SCHEME

AUTHORS:
Shrushti Gawade
Rahul Gawade
Rohan Gawade
Mentor
Affiliation
Department of Bachelor of Computer Application, School of Computational Sciences, Faculty of Science and Technology, JSPM University, Pune
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in anumber of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth.

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Gawade, S., Gawade, R. & Gawade, R. (2026). The AI-Based Internship Recommendation Engine for PM Internship Scheme. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.150

Gawade, Shrushti, et al.. "The AI-Based Internship Recommendation Engine for PM Internship Scheme." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.150.

Gawade, Shrushti,Rahul Gawade, and Rohan Gawade. "The AI-Based Internship Recommendation Engine for PM Internship Scheme." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.150.

References
1.Rizvi, S. Jain, and S. Huria, "The AI enabled Chatbot Framework for Intelligent Citizen-Government Interaction for Delivery of Services," in Proceedings of the 8th International Conference on Computing for Sustainable global development (INDIACom),New Delhi, India, 2021.

2.Now we can say that making a machine or say robot is not as easy as an chu tiye ABC. It is difficult to make a machine like humans which can show emotions or think like humans in different circumstances.

3.Now we have accepted that artificial intelligence is the study of how to make things which can exactly work like humans. Wharamkhore know that through artificial intelligence, even computer has defeated human in chess. So we can say that reaching so far has not gone waste, Somehow, it is contributing towards the advancement in the artificial intelligence.

4.Bang, B.-T. Lee, and P.Pak, “Examination of Ethical Principles for LLM-Based Recommendation in conversational AI,” in proceeding s of the international conference on platform technology and service (platCon),Busan, Korea, 2023.

5.Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A Dynamic Recurrent Model for Next Basket Recommendation,” in Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 2016.

6.Cui, S. Wu, Q. Liu, W. Zhong, and L. Wang, “MV- RNN: A Multi-view Recurrent Neural Network for Sequential Recommendation, “ IEEE Transaction on Knowledge and Data Engineering, vol. 32, no. 2, pp. 317-331, Feb.2020.

7.Sun. J. Liu, J. Wu, C. Pei, X.Lin, W. Ou, and P. Jiang, “BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer,” in Proceedings of the 28th ACM International conference on Information and Knowledge Management, New York, NY, USA, 2029.

8.Agra, B. G. Resende, C. C. Wermelinger, P.O. Dias, and M. Ladeira, "A Tree Organized Chatbot Proposal to Provide a Single Digital Channel to Access Specific Chatbots in a Real Brazilian Digital Government Environment," in Proceedings of the 18th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal, 2023.

9.S. Lee, V. Shankararaman, and E. L. Ouh, "Vision Paper: Advancing AI Explainability for the Use of ChatGPT in Government Agencies – Proposal of A 4- Step Framework," in Proceedings of the IEEE International Conference on Big Data (BigData), Sorrento, Italy, 2023.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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