Cited by 31Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

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

Mahamuni, Ashwini Jagannath, Parminder, Tonpe, Shalini Santosh·2024·2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/ickecs61492.2024.10616620This paper is marked retracted.

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 12

  1. Doe, 2020, Rethinking Assessment: The Impact of Standardized Testing on Education, Journal of Educational Research, 112, 480
  2. Johnson, 2018, Artificial Intelligence in Education: The Promise and the Peril, AI Magazine, 39, 10
  3. Patel, 2020, Ethical Implications of AI in Education: A Critical Review, Ethics and Information Technology, 22, 245
  4. Smith, 2021, Automating Standardized Test Grading An AI Approach, Journal of Educational Technology & Innovation, 34, 145
  5. Personalized Learning Through AI-Driven Adaptive Assessments, International Journal of Learning Analytics and Artificial Intelligence for Education, 5, 22
  6. Green, 2020, Evaluating Open-Ended Responses Using NLP Techniques: A New Frontier in Educational Assessment, Educational Research Review, 29, 204
  7. Johnson, 2019, Real-Time Feedback in Formative Assessments: An AI Approach, Journal of Adaptive Learning, 7
  8. Patel, 2018, Ethical Implications of AI in Educational Assessments: A Critical Review, Journal of Ethics in Information Technology, 20, 267
  9. Zhang, 2019, The Digital Divide in AI-Enhanced Education: Bridging the Gap, Education and Information Technologies, 24, 3031
  10. Johnson, 2020, Human-AI Collaboration in Educational Assessments: Ensuring Fairness and Reliability, AI & Society, 35, 545
  11. Kim, 2020, Machine Learning Models for Predicting Student Performance: A Comparative Study, Vocational and Technical Education Review, 45, 84
  12. Lee, 2018, The Impact of Item Response Theory in Educational Assessment, Assessment & Evaluation in Higher Education, 43, 987

Cited by

See all 31

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
Open access
closed
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review