Citation :

S. Parthasarathy, S. Suresh Raja, M. Yashica, M.S. Nehasri, S.R. Shreesha, M. Divyasri, R. Thangasankaran, "AI NEWUS: An AI-Powered Career Guidance and Placement Preparation Platform," International Journal of Computer & Organization Trends (IJCOT), vol. 16, no. 2, pp. 1-7, 2026. Crossref, https://doi.org/10.14445/22492593/IJCOT-V16I2P301

Abstract

This paper proposes an artificial intelligence-based career guidance and placement readiness tool that provides individuals looking to improve their skills and increase their employability potential with individualized and adaptive learning experiences. Current e-learning tools are based on static or fixed learning and assessment processes and often lack dynamic evaluation tools to assess individual performance. This paper proposes a dynamic evaluation framework that uses unique and randomized sets of questions to offer individualized learning experiences to users. Technical skills are developed through a series of structured learning stages with intelligent progress based on performance eligibility criteria. An AI-based analytics tool offers real-time evaluation of user interactions to identify gaps in technical skills, logical reasoning ability, verbal and quantitative aptitudes, and offers suggestions to improve these areas. Placement readiness is achieved through time-bound aptitude tests, company-specific technical problem sets, and AI-based mock interviews that mimic real-life processes. Automated feedback systems and visual performance analytics aid the learner in monitoring their progress while improving their weak areas. The novelty of this work lies in the unified integration of the concepts of adaptive learning, assessment generation, skill evaluation in multiple domains, and placement simulation under a single umbrella of artificial intelligence. This approach will improve learner engagement, placement preparedness, and the potential of artificial intelligence to match academic training with the ever-changing needs of the industry.

Keywords

Adaptive Learning, Artificial Intelligence, Career Guidance, Mock Interview, Placement Training.

References

[1] Ryan Shaun Baker, and Paul Salvador Inventado, “Educational Data Mining and Learning Analytics,” Learning Analytics, Springer, New York, NY, USA, vol. 1, no. 2, pp. 61-75, 2014.
[CrossRef] [Google Scholar] [Publisher Link]

[2] P. Brusilovsky, and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” The Adaptive Web, Springer, Berlin, Germany, vol. 3, no. 1, pp. 3-53, 2007.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Sanchi Dhende et al., “An AI-Based Personalized Learning Path Generator for Adaptive Education Systems,” International Journal of Engineering Research & Technology, vol. 15, no. 4, pp. 1-7, 2026.
[CrossRef] [Publisher Link]

[4] Judy Kay et al., “MOOCs: So Many Learners, So Much Potential,” IEEE Intelligent Systems, vol. 28, no. 3, pp. 70-77, 2013.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Venugopal Uppunuthala et al., “AI-Powered Personal Learning Assistant for Adaptive Education,” International Journal of Engineering Research & Technology, vol. 15, no. 2, pp. 1-8, 2026.
[CrossRef] [Publisher Link]

[6] C. Pramod, and S. Poojashree, “AI-Powered Career Guidance and Recommendation System,” International Journal of Advanced Research in Science, Engineering and Technology, vol. 13, no. 1, pp. 377-387, 2026.
[CrossRef] [Publisher Link]

[7] Shreya Nayak, and C.H. Vanipriya, “CareerCue: A Survey on Intelligent Job Recommendation Systems Using Skill Extraction and Conversational AI,” International Journal of Scientific Development and Research, vol. 10, no. 08, pp. b689-b695, 2025.
[Publisher Link]

[8] Pankaj Singh Sisodiya et al., “AI-Powered Personalized Career Guidance and Skill Recommendation System,” Advanced International Journal of Multidisciplinary Research, vol. 3, no. 6, pp. 1-4, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[9] B. Sahana Kumari et al., “AI-Powered Career Guidance System Using Machine Learning,” International Journal of Innovative Research in Engineering and Management, vol. 12, no. 5, pp. 65-67, 2025.
[CrossRef] [Publisher Link]

[10] E. Sundara Vignesh, V. Sanjay, and A. Akileshwari, “AI-Based Career Path Recommendation System,” International Journal for Science and Advance Research in Technology, vol. 11, no. 10, pp. 235- 242, 2025.
[Google Scholar] [Publisher Link]

[11] Aakash Maurya et al., “AI Based Career Recommendation System," International Journal for Multidisciplinary Research, vol. 8, no. 2, pp. 1-7, 2026.
[Publisher Link]

[12] Sachin Soni, “A Survey of Job Recommendation Systems: Techniques, Challenges, and Future Directions,” REST Journal on Emerging Trends in Modelling and Manufacturing, vol. 11, no. 2, pp. 13-15, 2025.
[CrossRef] [Publisher Link]

[13] J. Manyika et al., “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation,” McKinsey Global Institute, Technical Report, pp. 1-40, 2017.
[Google Scholar]

[14] Da Li, Yuehan Wang, and Mansha Liao, “Multi-Modal Deep Learning Models in Career Guidance for College Students: Fusing Text, Image, and Behavioral Data,” Proceedings of the 2025 8^th International Conference on Software Engineering and Information Management, pp. 226-232, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Cristóbal Romero, and Sebastián Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Transactions on Systems, Man and Cybernetics, vol. 40, no. 6, pp. 601-618, 2010.
[CrossRef] [Google Scholar] [Publisher Link]

[16] M. Woolf, AI in Education, Encyclopaedia of Artificial Intelligence, Wiley, pp. 320-350, 2018.
[Google Scholar] [Publisher Link]

[17] Zhen Yang, Songlin Liu, and Yi Yu, “Predictive Career Planning Using AI and Data-Driven Models for Student Development Guidance,” Proceedings of SPIE, vol. 13731, pp. 198-204, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Zhen Yang, Songlin Liu, and Yi Yu, “Research on Personalized Employment Recommendation System for Higher Vocational Students Based on Deep Learning Algorithms,” Proceedings of the 2024 4^th International Conference on Big Data, Artificial Intelligence and-Risk Management, pp. 479-483, 2025.
[CrossRef] [Google Scholar] [Publisher Link]