AI-Driven Exam Evaluation Systems: Challenges, Innovations, and Future Directions - Balkrishna Rasiklal Yadav (International Journal of Electronics Automation, 2024)
A proposed AI system is used to grade exams automatically. It addresses inefficiencies in human assessment. A GPT model trained on graded replies is used for evaluation, and TrOCR is used for precise handwritten text recognition. Efficiency and less bias are provided by this method, although there are still issues. More work is needed to assess open-ended questions and make sure they are understandable. To automate many aspects of exam evaluation, including grading, feedback, and plagiarism detection, it first examines the evolution of AI technologies, including machine learning, deep learning, and natural language processing. It also examines the potential for AI-driven assessment tools to enhance learning outcomes, reduce teacher workloads, and provide students with personalized feedback. Additionally, the study highlights several challenges, such as addressing. Our algorithm makes use of developments in two important fields of AI. To reduce bias, careful curation of training data is required. In its conclusion, the study emphasizes how important it is that the system be able to handle different question formats, deal with ambiguities, and incorporate human assessment. A promising first step toward an efficient, equitable, and AI-powered exam grading system is this research.
Keywords: Autograding; Debias; Explainability; GPT; TrOCR