Reinforcement Learning for Personalized Candidate Assessment Paths
Suhani Kumari, Vaibhavi, Suryakant Sharma, Amaan Ahmed, Manisha Dawra · 2026
The traditional way of taking interviews is dependent on systems having a static nature, as the rules and questions are fixed. As a consequence of such systems, there come various limitations that ultimately hamper the choice of the most suitable candidate. The prominent lack was the non-adaptiveness with the latent traits or real-time performance of the candidate. They are based on the “one for all” pattern. Hence, they resulted in less personalized questions with respect to the candidates. This work introduces Adaptive Talent Screener , a reinforcement-learning system that serves the interview with the adaptability and personalization needed. It utilizes Q-learning to model state action from the résumé and job description as input from the candidate. It chooses each question by iterating the updated q-values. Candidate answers are taken in the form of voice input, and it operates the action-reward procedure. This allows the RL agent to make its questioning policy more precise during the whole interview process. This system provides an automated score and feedback after every answer and eventually generates an integrated detailed performance report. Cheating and behavioral changes are monitored using cameras to improve the integrity of remote interview simulations. Testing shows that the RL-based architecture has more efficient and accurate assessment than fixed rule-based evaluation strategies. The result specifies that reinforcement learning provides an adaptable and vigorous system for automation in candidate assessment.