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Educational Technology and Responsible Automated Essay Scoring in the Generative AI Era
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Author(s): Hieu Thai (RMIT University, Vietnam), Minh Nguyen (RMIT University, Vietnam), Hung Truong Thanh Nguyen (Analytics Everywhere Lab, University of New Brunswick, Canada), Diem Thi Hong Vo (RMIT University, Vietnam), Binh Nguyen Thanh (RMIT University, Vietnam), Khang Nguyen (QAI, FPT Software, Vietnam), Son Ha (RMIT University, Vietnam)and Tam Vi An Le (RMIT University, Vietnam)
Copyright: 2024
Pages: 32
Source title:
Navigating the Circular Age of a Sustainable Digital Revolution
Source Author(s)/Editor(s): Umair Tanveer (University of Exeter, UK & Ho Chi Minh City University of Economics and Finance, Ho Chi Minh City, Vietnam), Shamaila Ishaq (University of Derby, UK), Truong Quang Huy (RMIT University, Vietnam)and Thinh Gia Hoang (RMIT University, Vietnam)
DOI: 10.4018/979-8-3693-2827-9.ch011
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Abstract
Generative AI-driven automated essay scoring (AES) is expected to revolutionize personalized education by offering customized feedback to students. However, the reliability of these systems is currently undermined by inherent limitations, such as the tendency for “hallucination,” where the AI generates factually incorrect or irrelevant information. To mitigate these issues and bolster the trustworthiness of AES, this chapter argues that the implementation of explainable AI (XAI) is crucial. Suitable XAI algorithms could make the GenAI's decision-making process transparent, allowing educators and students to understand and trust the feedback provided, thus ensuring the effective integration of AI in education. Furthermore, the chapter outlines several recommendations for achieving a responsible GenAI-driven AES system.
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