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An Applied Statistics dataset for human vs AI-generated answer classification.

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A new dataset of 4231 question-answer pairs was created to train artificial intelligence (AI) detectors for Applied Statistics assignments. This helps instructors identify AI-generated text and ensure academic integrity.

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Area of Science:

  • Statistics
  • Computer Science
  • Education

Background:

  • Large Language Models (LLMs) are increasingly used by students for assignments.
  • Detecting AI-generated text is challenging for instructors, especially in specialized fields.
  • Existing AI detection tools lack domain-specific accuracy.

Purpose of the Study:

  • To develop a dataset for training AI detection tools in Applied Statistics.
  • To address the need for domain-specific AI text authenticity verification.
  • To create a framework for collecting similar datasets in other fields.

Main Methods:

  • Collected 116 Applied Statistics questions from domain experts.
  • Designed a framework for student answer collection.
  • Gathered 100 student responses and an equal number of ChatGPT-generated answers.
  • Created a dataset of 4231 question-answer pairs.

Main Results:

  • A novel dataset for Applied Statistics AI detection was successfully created.
  • The dataset contains both human-generated and AI-generated text.
  • The data preparation framework is adaptable for other domains.

Conclusions:

  • The dataset will aid in developing and benchmarking AI detection tools for Applied Statistics.
  • The study provides a methodology for creating domain-specific AI detection datasets.
  • This work supports academic integrity in the age of AI text generation.