Evaluating Human and Machine Assessment: Introducing a Hybrid Approach for Enhanced Educational Evaluation

Abstract

This essay examines the evolving landscape of educational assessment, focusing on the analysis and comparison of human expertise and machine capabilities. Traditional educational assessment has predominantly relied on human evaluators who utilize their judgment and experience to assess student performance. However, advancements in technology have introduced machine assessments that leverage artificial intelligence to analyze data and provide feedback efficiently. This paper explores various assessment methods, including formative, summative, and peer assessments, and the role technology plays in enhancing these processes. A critical analysis of the strengths and weaknesses of both human and machine assessments is presented, highlighting scenarios where machines excel in efficiency and objectivity, particularly in handling large datasets and standardizing evaluations. Conversely, the essay emphasizes the irreplaceable depth of human insight necessary for assessing complex cognitive skills like creativity and critical thinking. The research advocates for a hybrid approach that combines the rapid analytical capabilities of machines with the nuanced judgment of human assessors. This integrative strategy aims to enhance reliability, personalize learning feedback, and optimize strategic educational planning. The proposed hybrid model not only addresses the shortcomings of both systems but also underscores the potential for improved assessment practices in various professional fields beyond education. This study calls for further exploration into the implementation of such hybrid assessment methods across diverse educational settings to maximize the benefits of both human and machine contributions.