INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Abstract:
Artificial intelligence (AI) is rapidly transforming the field of education, with the potential to provide personalized learning experiences, automate administrative tasks, and improve assessment outcomes. However, the implementation of AI in education also raises several ethical concerns, such as data privacy, bias, and the potential for AI to replace human teachers.
Explainable AI (XAI) is a promising solution to the problem of transparency and interpretability in AI models. XAI provides a way to explain the decision-making process of AI models, making it easier for stakeholders to understand and trust the decisions made by AI models.
This paper presents a systematic review of the opportunities and challenges of XAI for educational assessment. The review identifies the following key areas:
Opportunities: XAI can be used to improve the transparency and fairness of automated grading systems, provide feedback to students and teachers on their performance, and develop new assessment methods that are more aligned with the needs of individual students.
Challenges: Developing effective XAI methods for educational assessment is a challenging task, as educational assessment models are often complex and opaque. Additionally, it is important to consider the human factors involved in XAI, such as how to design XAI explanations that are understandable and useful for different stakeholders.
The paper concludes with a discussion of future research directions in XAI for educational assessment.
Keywords:
XAI, AI models, Artificial Intelligence
Cite Article:
"AI Transparency and Explain-ability", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.a519-a521, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311057.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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