AI-Driven Exam Grading, Percentile Calculation, and Plagiarism Detection System for Educational Integrity

Year : 2026 | Volume : 16 | Issue : 01 | Page : 07 12
    By

    Tajjuddin Malnas,

  • Prasanna Pandhare,

  • Divyank Panwar,

  • Sphurti Deshmukh,

  1. Student, Department Information Technology, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India
  2. Student, Department Information Technology, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India
  3. Student, Department Information Technology, Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India
  4. Assistant Professor, Department Information Technology, Pimpri Chinchwad College of Engineering Pune, Maharashtra, India

Abstract

The influence of artificial intelligence (AI) on academic evaluation has become increasingly significant, particularly in areas such as automated grading, large-scale content analysis, and plagiarism detection. These technologies provide educators with rapid processing, consistent scoring, and timely feedback that can support both teaching and learning. However, alongside these advantages, serious concerns have emerged regarding the reliability and fairness of AI-driven assessments. The growing presence of AI-generated content has further complicated the evaluation process, often leading to false positives, misclassification, and questionable judgments about academic integrity. These issues highlight the ethical challenges educators face when relying solely on automated systems. Therefore, a more balanced and responsible approach is essential—one that blends human expertise with the analytical power of AI. Such a hybrid framework can help reduce errors, improve transparency, and maintain accountability within academic environments. By combining human judgment with technological efficiency, institutions can promote a more reliable, ethical, and high-quality assessment process, ensuring that AI-supported educational tools genuinely enhance learning rather than compromise it.

Keywords: AI-powered education, grading, academic integrity, plagiarism detection, hybrid framework, ethical education, AI content, human-AI collaboration, content analysis, human insights, misclassification

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Tajjuddin Malnas, Prasanna Pandhare, Divyank Panwar, Sphurti Deshmukh. AI-Driven Exam Grading, Percentile Calculation, and Plagiarism Detection System for Educational Integrity. Current Trends in Information Technology. 2025; 16(01):07-12.
How to cite this URL:
Tajjuddin Malnas, Prasanna Pandhare, Divyank Panwar, Sphurti Deshmukh. AI-Driven Exam Grading, Percentile Calculation, and Plagiarism Detection System for Educational Integrity. Current Trends in Information Technology. 2025; 16(01):07-12. Available from: https://journals.stmjournals.com/ctit/article=2025/view=236626


References

  1. Elkhatat AM, Elsaid K, Almeer S. Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. Int J Educ Integr. 2023;19:17. doi:10.1007/s40979-023-00140-5.
  2. Kumar R. Faculty members’ use of artificial intelligence to grade student papers: A case of implications. Int J Educ Integr. 2023;19(1):9. doi:10.1007/s40979-023-00130-7.
  3. Tobler S. Smart grading: A generative AI-based tool for knowledge-grounded answer evaluation in educational assessments. MethodsX. 2024;12:102531. doi:10.1016/j.mex.2023.102531.
  4. Malik AR, Pratiwi Y, Andajani K, Numertayasa IW, Suharti S, Darwis A, et al. Exploring artificial intelligence in academic essay: Higher education student’s perspective. Int J Educ Res Open. 2023;5:100296. doi:10.1016/j.ijedro.2023.100296.
  5. Lanke MM, Porwal MA, Sarolkar MS, Palaskar MG, Kakad PS. AI based student’s assignments plagiarism detector. Int J Res Appl Sci Eng Technol. 2022;10(12):991–999. doi:10.22214/ijraset.2022.48080.
  6. Perkins M, Gezgin UB, Roe J. Reducing plagiarism through academic misconduct education. Int J Educ Integr. 2020;16(1):3. doi:10.1007/s40979-020-00052-8.
  7. Elkhatat AM, Elsaid K, Almeer S. Some students plagiarism tricks, and tips for effective check. Int J Educ Integr. 2021;17:15. doi:10.1007/s40979-021-00082-w.
  8. Foltýnek T, Dlabolová D, Anohina-Naumeca A, Razı S, Kravjar J, Kamzola L, et al. Testing of support tools for plagiarism detection. Int J Educ Technol High Educ. 2020;17(1):46. doi:10.1186/s41239-020-00192-4.
  9. Swiecki Z, Khosravi H, Chen G, Martinez-Maldonado R, Lodge JM, Milligan S, Selwyn N, Gašević D. Assessment in the age of artificial intelligence. Comput Educ Artif Intell. 2022;3:100075. doi:10.1016/j.caeai.2022.100075.
  10. Chemaya N, Martin D. Perceptions and detection of AI use in manuscript preparation for academic journals. PLOS One. 2024;19(7):e0304807. doi:10.1371/journal.pone.0304807.
  11. Weegar R, Idestam-Almquist P. Reducing workload in short answer grading using machine learning. Int J Artif Intell Educ. 2024;34(2):247–273. doi:10.1007/s40593-022-00322-1.
  12. Xie Y, Wu S, Chakravarty S. AI meets AI: Artificial intelligence and academic integrity—A survey on mitigating AI-assisted cheating in computing education. In: Proceedings of the 24th Annual Conference on Information Technology Education (SIGITE ’23); 2023. Marietta (GA): Association for Computing Machinery; 2023. p. 79–83. doi:10.1145/3585059.3611449.
  13. Yimyam W, Ketcham M. The grading multiple choice tests system via mobile phone using image processing technique. Int J Emerg Technol Learn. 2018;13(10):260. doi:10.3991/ijet.v13i10.8341.
  14. Pushpa G, Mallesh A, Siva Ranjan Das D. Enhancing e-assessment with image processing for exam evaluation. Ind Eng J. 2022;51(10):100–106.
  15. Migut G, Wiersma R. Are we consistent? The effects of digitized exams grading. In: Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE ’20); 2020. Portland (OR): Association for Computing Machinery; 2020. p. 1338. doi:10.1145/3328778.3372630.
  16. Cao Y, Porter L, Liao SN, Ord R. Paper or online? A comparison of exam grading techniques. In: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ’19); 2019 Jul 15–17; Aberdeen, Scotland, UK. New York (NY): Association for Computing Machinery; 2019. p. 1–6. doi:10.1145/3304221.3319739.
  17. Flodén J. Grading exams using large language models: A comparison between human and AI grading of exams in higher education using ChatGPT. Br Educ Res J. 2025;51(1):201–224. doi:10.1002/berj.4069.
  18. Chen J, Hartline J, Zoeter O. Fair grading algorithms for randomized exams. [Preprint]. 2023. arXiv:2304.06254v1. doi:10.48550/arXiv.2304.06254.
  19. Liu T, Chatain J, Kobel-Keller L, Kortemeyer G, Willwacher T, Sachan M. AI-assisted automated short answer grading of handwritten university level mathematics exams. arXiv [Preprint]. 2024 Aug 21:arXiv:2408.11728.
  20. Nithin T, Nasim M, Shekhar TR, Gautam OS, Gholap Y. OMR auto grading system. Int J Innov Sci Eng Technol. 2015;2(5):522–526.
  21. Grocevs A, Prokofjeva N. Modern algorithms to identify plagiarism. In: Environment. Technology. Resources: Proceedings of the International Scientific and Practical Conference. 2019 Jun;Vol 2:61–64. doi:10.17770/etr2019vol2.4058.
Regular Issue Subscription Original Research
Volume 16
Issue 01
Received 19/05/2025
Accepted 16/09/2025
Published 17/11/2025
Publication Time 182 Days
62

Login


My IP

PlumX Metrics