AI-Based Assessment Models for Student Performance Evaluation

Shane Dawson

AI-Based Assessment Models for Student Performance Evaluation

Author(s)	Shane Dawson
Country	Australia
Abstract	Artificial Intelligence (AI) has increasingly influenced educational assessment by enabling automated evaluation, predictive analysis, personalized feedback, and continuous monitoring of student performance. Traditional assessment approaches often depend on periodic examinations and manually evaluated assignments, which may limit the ability to provide timely and individualized feedback. AI-based assessment models can analyze diverse forms of student data, including examination responses, assignments, learning activities, interaction patterns, and performance histories, to support more adaptive evaluation processes. This study examines AI-based assessment models for student performance evaluation through a conceptual and literature-based research design. The analysis focuses on automated assessment, learning analytics, predictive modeling, natural language processing, machine learning, personalized feedback, adaptive testing, and early identification of learning difficulties. AI systems can assist educators by processing large volumes of assessment information and identifying patterns that may be difficult to detect through conventional evaluation alone. However, effective implementation requires attention to assessment validity, algorithmic fairness, transparency, data privacy, cybersecurity, teacher oversight, and accessibility. Automated systems should complement rather than completely replace professional educational judgment because student performance involves contextual, behavioral, and developmental factors that may not be fully represented in digital data. The study proposes an integrated AI-based assessment framework connecting data collection, preprocessing, intelligent analysis, performance prediction, feedback generation, and teacher validation. The study concludes that AI can strengthen student performance evaluation when implemented through transparent, pedagogically aligned, human-supervised, and ethically responsible assessment practices.
Keywords	Artificial Intelligence, Student Assessment, Performance Evaluation, Machine Learning, Learning Analytics, Automated Assessment, Educational Technology, Predictive Analytics, Personalized Feedback, Adaptive Assessment
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-14

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American International Journal of Advanced Research and Development

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AI-Based Assessment Models for Student Performance Evaluation

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