The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Retracted: Enhancing Educational Assessment with Artificial Intelligence: Challenges and Opportunities
Abstract
In the evolving landscape of education, Artificial Intelligence (AI) presents transformative potentials for enhancing educational assessment. This paper explores the integration of AI technologies into the assessment process, aiming to improve accuracy, fairness, and efficiency. Traditional assessment methods, while effective to a certain extent, face limitations in adaptability, scalability, and the ability to provide personalized feedback. AI-driven approaches, leveraging machine learning, natural language processing, and advanced analytics, offer a pathway to overcome these challenges. We examine the current state of educational assessments, identifying key areas where AI can contribute significantly, including automated grading, adaptive testing, and real-time feedback mechanisms. Furthermore, we propose a novel framework that incorporates AI to refine assessment strategies, ensuring they are more aligned with individual learner needs and learning outcomes. The implementation of this framework is demonstrated through specific algorithms and mathematical models, designed to enhance decision-making processes within educational systems. Our results, supported by empirical data and simulated scenarios, showcase the efficacy of AI in providing more nuanced insights into student performance and learning gaps. However, the integration of AI into educational assessments is not without challenges. Issues related to ethics, data privacy, and the digital divide are discussed, emphasizing the need for a balanced approach that considers both the opportunities and the obstacles inherent in leveraging AI for educational purposes.
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In the citing paper
“AI can enhance student engage -ment [ 6 ] and efficiency in assessments [ 7 ]”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [7]
In the citing paper
“A key aspect challenging the education sector is con -ducting assessments in an AI-infused environment [ 7, 11].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [7]
Citations
Cites
Adaptive Learning Systems: Beyond Teaching Machines
Kara, N., & Sevim, N. (2013) · Contemporary Educational Technology · doi:10.30935/cedtech/6095 · Cited by 70
Research works printed but not linked: 12
- Doe, 2020, Rethinking Assessment: The Impact of Standardized Testing on Education, Journal of Educational Research, 112, 480
- Johnson, 2018, Artificial Intelligence in Education: The Promise and the Peril, AI Magazine, 39, 10
- Patel, 2020, Ethical Implications of AI in Education: A Critical Review, Ethics and Information Technology, 22, 245
- Smith, 2021, Automating Standardized Test Grading An AI Approach, Journal of Educational Technology & Innovation, 34, 145
- Personalized Learning Through AI-Driven Adaptive Assessments, International Journal of Learning Analytics and Artificial Intelligence for Education, 5, 22
- Green, 2020, Evaluating Open-Ended Responses Using NLP Techniques: A New Frontier in Educational Assessment, Educational Research Review, 29, 204
- Johnson, 2019, Real-Time Feedback in Formative Assessments: An AI Approach, Journal of Adaptive Learning, 7
- Patel, 2018, Ethical Implications of AI in Educational Assessments: A Critical Review, Journal of Ethics in Information Technology, 20, 267
- Zhang, 2019, The Digital Divide in AI-Enhanced Education: Bridging the Gap, Education and Information Technologies, 24, 3031
- Johnson, 2020, Human-AI Collaboration in Educational Assessments: Ensuring Fairness and Reliability, AI & Society, 35, 545
- Kim, 2020, Machine Learning Models for Predicting Student Performance: A Comparative Study, Vocational and Technical Education Review, 45, 84
- Lee, 2018, The Impact of Item Response Theory in Educational Assessment, Assessment & Evaluation in Higher Education, 43, 987
Cited by
Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31
Mentigo: An Intelligent Agent for Mentoring Students in the Creative Problem Solving Process
Zha, S., Liu, Y., Zheng, C., Xu, J., Yu, F., Gong, J., & Xu, Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713952 · Cited by 19
Metadata
- Authors
- Mahamuni, A. J., Parminder, & Tonpe, S. S.
- Affiliations
- Lovely Professional University (IN)
- MIT World Peace University (IN)
- Venue
- 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)
- Publisher
- IEEE
- Published
- 2024-04
- Pages
- 1-5
- OpenAlex
- W4401387761
- Cited by
- 31
- References
- 1
- Citations by year
- 2026: 8 · 2025: 23
- FWCI
- 11.79
- Type
- conference paper
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-10-23)
- Open access
- closed
- Licence
- Locations
- International Conference on Knowledge Engineering and Communication Systems (ICKECS) (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- artificial intelligence in education, educational assessment, automated grading, adaptive testing, real-time feedback, personalized feedback, assessment fairness, student performance analysis, learning gaps, AI ethics in education, data privacy, digital divide
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- The paper
- Mahamuni, A. J., Parminder, & Tonpe, S. S. (2024). Retracted: Enhancing Educational Assessment with Artificial Intelligence: Challenges and Opportunities. 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). https://doi.org/10.1109/ickecs61492.2024.10616620