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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65362
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science
Khalsa University, Amritsar, Punjab, India
Educational assessment is being reshaped by Artificial Intelligence (AI), which brings smarter, faster, and more individualized ways to evaluate learning. Many long-used assessment approaches are constrained by problems like slow responses to students, inconsistent judgment, difficulty handling large volumes, and heavy administrative effort. Smart assessment solutions powered by AI overcome these obstacles by combining Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), Learning Analytics, and Computer Vision. With these capabilities, they support automatic scoring, tests that adjust to the learner, tailored feedback, forecasting through analytics, and ongoing tracking of student progress. Yet, even with clear benefits, current AI-driven assessment tools still struggle with issues involving openness, biased outcomes from algorithms, ethical risks, the ability to justify decisions, and protecting data. This study surveys the present landscape of AI-enabled smart assessment and automated evaluation tools, highlights important gaps in the literature, and introduces a new model called the Explainable Ethical Multimodal Smart Assessment Framework (EEMSAF). In one cohesive structure, the model brings together Explainable AI (XAI), governance for ethical AI, security via blockchain, learning analytics, and assessment across multiple modalities to strengthen openness, equity, responsibility, and individualized support. A side-by-side comparison shows that EEMSAF performs better than standard AI-based assessment approaches. The article ends with consideration of emerging directions and research prospects for intelligent assessment in education.
KEYWORDS
AI-based smart assessment and automated evaluation systems are transforming educational assessment by improving efficiency, scalability, and personalization. Despite significant advancements, challenges related to explainability, fairness, transparency, ethics, and security remain unresolved. This paper proposed the Explainable Ethical Multimodal Smart Assessment Framework (EEMSAF), which integrates Explainable AI, Ethical Governance, Blockchain Security, Learning Analytics, and Multimodal Assessment into a unified architecture. The framework addresses critical limitations of existing systems and provides a pathway toward trustworthy, transparent, and learner-centered assessment. Future research should focus on validating the framework through real-world implementations and exploring the integration of Generative AI with ethical and explainable assessment mechanisms.
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Publication Date
6/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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