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Related Experiment Video

Updated: Jun 29, 2026

One Dimensional Turing-Like Handshake Test for Motor Intelligence
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One Dimensional Turing-Like Handshake Test for Motor Intelligence

Published on: December 15, 2010

Classifying human vs. AI text with machine learning and explainable transformer models.

Adven Masih1, Bushra Afzal2, Shamyla Firdoos2

  • 1Faculty of Computing and Information Technology, University of Sialkot, Daska Road, Sialkot, 51040, Punjab, Pakistan. adven.masih@uskt.edu.pk.

Scientific Reports
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a robust framework for detecting AI-generated text, finding RoBERTa to be the most accurate model. This AI content detection method offers high performance for verifying text authenticity.

Keywords:
AI generated text detectionGPT-4Human-Generated textLarge language models (LLMs)Natural language processing (NLP)Recurrent deep learningText classificationTransformer models

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Natural Language Processing
  • Artificial Intelligence Ethics

Background:

  • The rise of advanced AI models like ChatGPT necessitates methods to differentiate AI-generated text from human writing.
  • Ensuring content authenticity and ethical AI use are critical challenges in current digital communication.

Purpose of the Study:

  • To develop and evaluate a comprehensive framework for distinguishing between human-written and AI-generated text.
  • To compare the effectiveness of various machine learning and deep learning models in AI text detection.

Main Methods:

  • A dataset of 20,000 diverse text samples was created for training and testing.
  • Machine learning, sequential deep learning (LSTM, GRU), and transformer models (BERT, RoBERTa) were employed.
  • Performance was evaluated using accuracy, statistical significance tests (McNemar's), and explainability techniques (LIME, SHAP).

Main Results:

  • The RoBERTa model achieved the highest accuracy (96.1%), significantly outperforming other models.
  • Post-hoc analysis improved model calibration and precision for critical applications.
  • Explainability methods revealed distinct linguistic features differentiating AI and human text.

Conclusions:

  • RoBERTa presents a reliable, interpretable, and efficient solution for detecting AI-generated content.
  • The developed framework offers a robust approach to verifying text authenticity in the age of AI.