Real Time Adaptive Question Crafting with Accuracy Feedback enabled by Machine Learning and Artificial Intelligence
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Abstract
In the context of today’s increasing competitive employment landscape, precisely assessing a candidate’s technical capabilities throughout the interview procedure is both critical and challenging. Conventional methods, including multiple-choice assessments and standardized question sets, often be lacking in capturing the depth and gradation of an individual's proficiency. Additionally, dependence on third-party agencies for primary candidate screening may consequence in superficial evaluations that manage contextual skill orientation with the job role.
To report these boundaries, this research recommends a novel AI-driven framework that influences Natural Language Processing (NLP) and Machine Learning (ML) techniques to dynamically generate and evaluate technical interview questions in real time. The projected system occupies candidates in a natural language dialogue and uses their responses to create contextually suitable, open-ended technical questions on-the-fly. By continuously inspecting semantic and syntactic landscapes of candidate inputs, the system disseminates question difficulty and topic focus, thereby generating a highly personalized and adaptive interview experience.
This intelligent interview framework not only progresses the precision of skill assessment but also expands candidate engagement by simulating a more real time and receptive assessment environment. The system’s adaptive questioning mechanism assurances hard and role-specific assessment, eventually leading to more informed hiring results and improved talent acquisition workflows.