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1724702 Vol 10 · Issue 5 Download Paper

AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review

Juvaireeya Jamadar Vaishnavi Patil Safiya Attar Sakshi Patil Komal Jadhav

Subject area: Science,Engineering and Technology  ·  Area of research: AI in Education and Student Assessment

DOI: 10.64388/ire1724702

Abstract

Students today use digital platforms to access study materials, practise questions and check their learning progress. However, these activities are often managed through different platforms, which can make learning and classroom management less convenient. This paper reviews research on the use of Artificial Intelligence in education, focusing on student assessment, automatic question generation, personalized learning and learning analytics. The reviewed studies discuss techniques such as machine learning, deep learning, natural language processing and large language models. They explain how these techniques can support learning analysis and the preparation of assessment questions. The studies also highlight challenges related to question quality, data availability and the need for human review of AI-generated content. Based on this review, the paper presents AIEdutrack, a proposed application that brings PDF-based study materials, flashcards, quick tests, live quizzes and result tracking together with classroom features. These features include class creation, notes, assignments and announcements. The application also aims to support AI-assisted quiz generation. The proposed system requires further testing in an actual educational environment to evaluate its usefulness for students and teachers.

Keywords

artificial intelligence; student assessment; learning analytics; automatic question generation; e-learning; classroom management

References

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How to cite this paper

Juvaireeya Jamadar, Vaishnavi Patil, Safiya Attar, Sakshi Patil, Komal Jadhav "AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review" Iconic Research And Engineering Journals Volume 10 Issue 5 2026 Page 735-741 https://doi.org/10.64388/ire1724702
Juvaireeya Jamadar, Vaishnavi Patil, Safiya Attar, Sakshi Patil, Komal Jadhav "AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review" Iconic Research And Engineering Journals, vol. 10, no. 5, Nov. 2026, doi: https://doi.org/10.64388/ire1724702
Juvaireeya Jamadar, Vaishnavi Patil, Safiya Attar, Sakshi Patil, Komal Jadhav (2026). AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review. Iconic Research And Engineering Journals, 10(5). doi: https://doi.org/10.64388/ire1724702
Juvaireeya Jamadar, Vaishnavi Patil, Safiya Attar, Sakshi Patil, Komal Jadhav "AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review" Iconic Research And Engineering Journals, vol. 10, no. 5, Nov. 2026. Crossref, https://doi.org/10.64388/ire1724702
@article{1724702,
      author = {Juvaireeya Jamadar, Vaishnavi Patil, Safiya Attar, Sakshi Patil, Komal Jadhav},
      title = {AIEdutrack: AI-Based Student Assessment and Progress Tracking Application – A Literature Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {735-741},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724702.pdf},
      abstract = {Students today use digital platforms to access study materials, practise questions and check their learning progress. However, these activities are often managed through different platforms, which can make learning and classroom management less convenient. This paper reviews research on the use of Artificial Intelligence in education, focusing on student assessment, automatic question generation, personalized learning and learning analytics. The reviewed studies discuss techniques such as machine learning, deep learning, natural language processing and large language models. They explain how these techniques can support learning analysis and the preparation of assessment questions. The studies also highlight challenges related to question quality, data availability and the need for human review of AI-generated content. Based on this review, the paper presents AIEdutrack, a proposed application that brings PDF-based study materials, flashcards, quick tests, live quizzes and result tracking together with classroom features. These features include class creation, notes, assignments and announcements. The application also aims to support AI-assisted quiz generation. The proposed system requires further testing in an actual educational environment to evaluate its usefulness for students and teachers.},
      keywords = {artificial intelligence; student assessment; learning analytics; automatic question generation; e-learning; classroom management},
      month = {November},
      doi = {https://doi.org/10.64388/ire1724702}
  }