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This study introduces generative AI software for automated, customized grading of text answers, significantly reducing evaluation time and resources in education and research. The tool ensures high reliability, aiding educators and researchers in efficiently assessing student responses.

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Natural Language Processing

Background:

  • Automated evaluation of text-based answers in educational and behavioral research is labor-intensive.
  • Existing AI tools lack customization for specific case evaluations of large datasets.
  • There is a need for efficient, scalable solutions for text-based answer scoring.

Purpose of the Study:

  • To develop and validate a flexible software and web application for automated, case-specific grading of text-based answers.
  • To leverage cutting-edge artificial intelligence, specifically large language models (LLMs), for enhanced answer evaluation.
  • To provide researchers and educators with a user-friendly tool for processing large volumes of student responses.

Main Methods:

  • Development of a software and web application integrating LLMs with customizable parameters for question specifics, sample solutions, and evaluation criteria.
  • Implementation of an interface for specifying grading instructions tailored to individual research or educational contexts.
  • Validation of the automated scoring method through an empirical study comparing AI ratings with expert assessments.

Main Results:

  • The developed software demonstrated high reliability when compared against expert ratings in an empirical study.
  • The system enables customizable and case-specific automated grading of large text-based answer datasets.
  • The open-source nature of the software facilitates direct implementation and adaptation by users.

Conclusions:

  • The generative AI-enhanced software offers a valuable tool for facilitating and improving text-based answer evaluation.
  • The application addresses the limitations of current AI implementations by allowing automated, case-specific scoring of numerous student answers.
  • This open-source solution empowers educators and researchers with efficient and adaptable AI technology for assessment.