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August 5, 20242 citationsOpen Access

Review Of Approaches Towards Building AI Based Career Recommender & Guidance Systems

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ASArya ShahRPRajashree PatiAPAishwarya Pimplikar

Key Points

  • AI-driven data mining and machine learning provide the optimal approach for career prediction, tailoring guidance to individual traits and personal environments.
  • Systematic assessment details algorithmic approaches designed to counter recommendation system bias while evaluating student aptitude and academic achievements.
  • Findings indicate that automated guidance tools help reduce data overload, offering scalable career advice to students seeking professional networking pathways.

Abstract

With the expansion of the Internet, users have access to a massive amount of data, which can lead toi nformation overload and has to be organized. Students struggle to choose a career. Personality, aptitude, academic achievements, and academic and personal environments influence students' job choices. In underdeveloped and even developed countries, students have struggled to acquire effective, free job advice. Due to this disparity, many students are unable to network with professionals in their dream careers. Based on the above facts and pain points, a systematic assessment of AI-based career recommendation and guidance system techniques has been presented in detail. Addressing career recommendation system bias followed in various ways. The study shows that AI-based Data Mining & ML is the optimal method for career prediction for students and early professionals.

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Cite This Study

Shah et al. (2024) studied this question.

synapsesocial.com/papers/68e5d692b6db64358756c8dfhttps://doi.org/10.14293/pr2199.000978.v1
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