What question did this study set out to answer?

The aim is to create a framework that conceptualizes the evolution of machine learning in science assessment.

From substitution to redefinition: A framework of machine learning‐based science assessment

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Develops a framework to understand machine learning's impact on science assessment, suggesting future trends.

The aim is to create a framework that conceptualizes the evolution of machine learning in science assessment.
Systematic review of 47 studies applying machine learning in science assessment.
Classification into five categories: constructed response, essay, simulation, educational game, and inter-discipline.
Two-step cluster analysis to identify characteristics and clusters across three variables.
Identified 12 critical characteristics mapping to construct, functionality, and automaticity.
Established four evolutionary levels of machine learning's impact on science assessment practice.
Proposed five trends for future machine learning integration in educational assessments.

Zhaı et al. (2020) studied this question.

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