AI-Powered Career Coach: A Review on Digital Career Assistance System

Authors

  • Saba Zaidi Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana, India Author
  • Gyan Prakash Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana, India Author
  • Rajat Siwach Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana , India Author
  • Manan Yadav Panipat Institute of Engineering & Technology, Samalkha, Panipat, Haryana , India Author
  • Rahul Bhandari Bellevue Parfums, USA LLC, New York,10118, United States Author

DOI:

https://doi.org/10.21467/proceedings.7.6.47

Keywords:

Artificial Intelligence, Career Guidance, Career Counseling

Abstract

Career counselling mentoring continues to be important in the modern labour market because it provides useful feedback on the workplace. Yet the current career advice practices are in many ways failing because they do not offer individualized solutions attuned to present-day needs. To meet this, AI-based career guidance systems have been developed with important features including personalized career suggestions, current industry information, and automated features such as resume builders, cover letter writers, and career estimation tools. This paper discusses the role of AI in career planning through its capacity to produce actionable insights, solve planning problems, and improve job readiness. It also investigates essential issues like accuracy of data, ethical impact of AI recommendation, and trust loss potential for users. Additionally, the study compares current AI-powered career websites and approaches with a focus on the need to incorporate AI with human guidance, have algorithmic transparency, and improve personalization while addressing ethical challenges in forthcoming advancements.

References

[1] S. Westman et al., “Artificial intelligence for career guidance - current requirements and prospects for the future,” IAFOR J. Educ., vol. 9, no. 4, pp. 43–62, 2021, doi: 10.22492/ije.9.4.03.

[2] S. H. Faruque, S. A. Khushbu, and S. Akter, “Unlocking Futures: A Natural Language Driven Career Prediction System for Computer Science and Software Engineering Students,” pp. 1–24, 2024, [Online]. Available: http://arxiv.org/abs/2405.18139

[3] S. N. Akter et al., “An In-depth Look at Gemini’s Language Abilities,” 2023, [Online]. Available: http://arxiv.org/abs/2312.11444

[4] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using siamese BERT-networks,” EMNLP-IJCNLP 2019 - 2019 Conf. Empir. Methods Nat. Lang. Process. 9th Int. Jt. Conf. Nat. Lang. Process. Proc. Conf., pp. 3982–3992, 2019, doi: 10.18653/v1/D19-1410.

[5] Z. Papamitsiou and A. A. Economides, “Learning analytics and educational data mining in practice: A systemic literature review of empirical evidence,” Educ. Technol. Soc., vol. 17, no. 4, pp. 49–64, 2014.

[6] R. Binns, “Fairness in Machine Learning: Lessons from Political Philosophy,” Proc. Mach. Learn. Res., vol. 81, no. 2016, pp. 149–159, 2018.

[7] A. Ferrario and M. Loi, “How Explainability Contributes to Trust in AI,” SSRN Electron. J., 2022, doi: 10.2139/ssrn.4020557.

[8] A. Izbassar, M. Muratbekova, D. Amangeldi, N. Oryngozha, A. Ogorodova, and P. Shamoi, “Intelligent System for Assessing University Student Personality Development and Career Readiness,” Procedia Comput. Sci., vol. 231, pp. 779–785, 2024, doi: 10.1016/j.procs.2023.12.138.

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Published

2025-11-21

How to Cite

[1]
S. Zaidi, G. Prakash, R. Siwach, M. Yadav, and R. Bhandari, “AI-Powered Career Coach: A Review on Digital Career Assistance System”, AIJR Proc., vol. 7, no. 6, pp. 412–417, Nov. 2025, doi: 10.21467/proceedings.7.6.47.