International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 186 - Number 15 |
Year of Publication: 2024 |
Authors: Gaurav Rohatgi |
10.5120/ijca2024923530 |
Gaurav Rohatgi . AI for Skill Evaluation: Question Generation Framework for Skill Assessment. International Journal of Computer Applications. 186, 15 ( Apr 2024), 52-55. DOI=10.5120/ijca2024923530
In the realm of workforce evaluation, assessing employee skills accurately and efficiently is paramount for organizational growth and development. Traditional methods often fall short of capturing the comprehensive skill set of an employee, leading to subjective evaluations and biased outcomes. To address this challenge, this paper proposes a novel approach leveraging Artificial Intelligence (AI) for skill evaluation through question generation. The proposed framework utilizes AI algorithms to automatically generate tailored questions based on the specific skill set provided for evaluation. By employing natural language processing (NLP) techniques, the system analyzes the skill domain, identifies key concepts and competencies, and generates diverse and contextually relevant questions. These questions encompass various levels of difficulty and complexity, ensuring a comprehensive assessment of the employee's proficiency. Furthermore, the AI-powered question generation framework enables adaptive assessment, wherein the difficulty level and focus areas of questions dynamically adjust based on the employee's responses. Through continuous learning and refinement, the system enhances its question-generation capabilities, ensuring adaptive and personalized evaluations tailored to individual employee profiles. Sharma et al. (2019) proposed an automated assessment system for problem-solving skills using AI. They developed a methodology that leverages artificial intelligence algorithms to evaluate problem-solving capabilities efficiently (Sharma, Chhikara, & Sharma, 2019). The integration of AI-driven question generation not only streamlines the evaluation process but also enhances objectivity and fairness by standardizing assessment criteria. Additionally, the system provides valuable insights into employee strengths, weaknesses, and areas for improvement, enabling targeted training and development initiatives. Overall, this paper presents a pioneering approach to skill evaluation in the workplace, leveraging AI-driven question generation to deliver accurate, objective, and personalized assessments. By harnessing the power of AI, organizations can optimize their talent management strategies, foster employee growth, and drive organizational success in an increasingly competitive landscape.