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PAIGE: A generative AI-based framework for promoting assignment integrity in higher education


  • Received: 26 October 2023 Revised: 23 November 2023 Accepted: 17 December 2023 Published: 26 December 2023
  • The integration of Generative Artificial Intelligence (GAI) tools like ChatGPT, Google Bard, and Bing Chat in higher education shows excellent potential for transformation. However, this integration also raises issues in maintaining academic integrity and preventing plagiarism. In this study, we investigate and analyze practical approaches for efficiently harnessing the potential of GAI while simultaneously ensuring the preservation of assignment integrity. Despite the potential to expedite the learning process and improve accessibility, concerns regarding academic misconduct highlight the necessity for the implementation of novel GAI frameworks for higher education. To effectively tackle these challenges, we propose a conceptual framework, PAIGE (Promoting Assignment Integrity using Generative AI in Education). This framework emphasizes the ethical integration of GAI, promotes active student interaction, and cultivates opportunities for peer learning experiences. Higher education institutions can effectively utilize the PAIGE framework to leverage the promise of GAI while ensuring the preservation of assignment integrity. This approach paves the way for a responsible and thriving future in Generative AI-driven education.

    Citation: Shakib Sadat Shanto, Zishan Ahmed, Akinul Islam Jony. PAIGE: A generative AI-based framework for promoting assignment integrity in higher education[J]. STEM Education, 2023, 3(4): 288-305. doi: 10.3934/steme.2023018

    Related Papers:

  • The integration of Generative Artificial Intelligence (GAI) tools like ChatGPT, Google Bard, and Bing Chat in higher education shows excellent potential for transformation. However, this integration also raises issues in maintaining academic integrity and preventing plagiarism. In this study, we investigate and analyze practical approaches for efficiently harnessing the potential of GAI while simultaneously ensuring the preservation of assignment integrity. Despite the potential to expedite the learning process and improve accessibility, concerns regarding academic misconduct highlight the necessity for the implementation of novel GAI frameworks for higher education. To effectively tackle these challenges, we propose a conceptual framework, PAIGE (Promoting Assignment Integrity using Generative AI in Education). This framework emphasizes the ethical integration of GAI, promotes active student interaction, and cultivates opportunities for peer learning experiences. Higher education institutions can effectively utilize the PAIGE framework to leverage the promise of GAI while ensuring the preservation of assignment integrity. This approach paves the way for a responsible and thriving future in Generative AI-driven education.



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  • Author's biography Shakib Sadat Shanto is an undergraduate currently studying Bachelor of Science in Computer Science and Engineering at American International University-Bangladesh. He is extremely passionate about AI and Data Science domain. His current research interests include AI, Educational Technology, Natural Language Processing and Cybersecurity; Zishan Ahmed is an enthusiastic undergraduate pursuing a Bachelor of Science in Computer Science and Engineering. He is captivated by the potential of data to alter the world we live in. In data science, natural language processing, and machine learning, he sees the greatest potential for innovation and influence. His knack for mathematics and programming has been refined throughout his academic career. He is well-versed in programming languages and is always keen to acquire new tools and technologies. His current research interests include AI, Educational Technology, Natural Language Processing, and computer vision; Dr. Akinul Islam Jony is currently working as an Associate Professor of Computer Science at American International University-Bangladesh. His current research interests include AI, machine learning, e-Learning, educational technology, cybersecurity, and issues in software engineering
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