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- Vol. 5 Issue 3
- Article
Artificial Intelligence in Education: A Review of Learning Analytics, Personalized Tutoring, Generative AI and Academic Integrity
Authors
1 Assistant Professor, Department of Physics, The American College, Madurai, Tamilnadu, India. 2 3 Assistant Professor, School of Computer Science and IT, JAIN (Deemed to be) University, Jayanagar, Bangalore, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 433-440
- Article Type
- Review Article
References
1. L. Chen, P. Chen, and Z. Lin, "Artificial intelligence in education: A review," IEEE Access, vol. 8, pp. 75264–75278, 2020, doi: 10.1109/ACCESS.2020.2988510.
2. O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic review of research on artificial intelligence applications in higher education – where are the educators?" International Journal of Educational Technology in Higher Education, vol. 16, Art. no. 39, 2019, doi: 10.1186/s41239-019-0171-0.
3. S. Pandikumar, C. Suresh Kumar, M. Athigopal, and E. Jacob Jebaraj, "Impact of social media in higher education," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2018.
4. G. Siemens and P. Long, "Penetrating the fog: Analytics in learning and education," EDUCAUSE Review, vol. 46, no. 5, pp. 30–40, 2011.
5. S. Pandikumar, B. Maram, T. Margaret Mary, and Nageshwari, "Personalized and context-aware text suggestion using deep reinforcement learning," in Proc. International Conference on Future and Advanced Computing Technologies (ICFACT), Krishnankoil, India, 2026, pp. 1–7, doi: 10.1109/ICFACT66887.2026.11518436.
6. E. Kasneci et al., "ChatGPT for good? On opportunities and challenges of large language models for education," Learning and Individual Differences, vol. 103, Art. no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
7. S. Pandikumar, C. Menaka, T. Kiran, and T. John Paul Antony, "Evolution and analysis of modern plagiarism detection methods: A systematic review," in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 131–140, doi: 10.48001/978-81-980647-5-2-10.
8. C. Romero and S. Ventura, "Educational data mining and learning analytics: An updated survey," WIREs Data Mining and Knowledge Discovery, vol. 10, no. 3, Art. no. e1355, 2020, doi: 10.1002/widm.1355.
9. T. Foltýnek, N. Meuschke, and B. Gipp, "Academic plagiarism detection: A systematic literature review," ACM Computing Surveys, vol. 52, no. 6, Art. no. 112, 2019, doi: 10.1145/3345317.
10. S. Slade and P. Prinsloo, "Learning analytics: Ethical issues and dilemmas," American Behavioral Scientist, vol. 57, no. 10, pp. 1510–1529, 2013, doi: 10.1177/0002764213479366.
11. R. S. Baker and A. Hawn, "Algorithmic bias in education," International Journal of Artificial Intelligence in Education, vol. 32, no. 4, pp. 1052–1092, 2022, doi: 10.1007/s40593-021-00285-9.
12. K. E. Arnold and M. D. Pistilli, "Course Signals at Purdue: Using learning analytics to increase student success," in Proc. 2nd International Conference on Learning Analytics and Knowledge (LAK), Vancouver, BC, Canada, 2012, pp. 267–270, doi: 10.1145/2330601.2330666.
13. J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open University Learning Analytics dataset," Scientific Data, vol. 4, Art. no. 170171, 2017, doi: 10.1038/sdata.2017.171.
14. T. Margaret Mary, K. Soumya, G. Ramanathan, and G. Clinton, "An assessment on classification in Python using data science," in Proc. 3rd International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 551–555.
15. J. Gardner and C. Brooks, "Student success prediction in MOOCs," User Modeling and User-Adapted Interaction, vol. 28, no. 2, pp. 127–203, 2018, doi: 10.1007/s11257-018-9203-z.
16. B. Arivazhagan, S. Pandikumar, S. B. Sethupandian, and R. S. Subramanian, "Pattern discovery and analysis of customer buying behavior using association rules mining algorithm in e-commerce," in Proc. International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022, pp. 1–5, doi: 10.1109/ICEEICT53079.2022.9768473.
17. H. Khosravi, S. Buckingham Shum, G. Chen, C. Conati, Y.-S. Tsai, J. Kay, S. Knight, R. Martinez-Maldonado, S. Sadiq, and D. Gašević,"Explainable artificial intelligence in education," Computers and Education: Artificial Intelligence, vol. 3, Art. no. 100074, 2022, doi: 10.1016/j.caeai.2022.100074.
18. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, "AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems," Results in Nonlinear Analysis, vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
19. H. Drachsler and W. Greller, "Privacy and analytics: It's a DELICATE issue. A checklist for trusted learning analytics," in Proc. 6th International Conference on Learning Analytics and Knowledge (LAK), Edinburgh, U.K., 2016, pp. 89–98, doi: 10.1145/2883851.2883893.
20. K. Soumya, T. Margaret Mary, and G. Clinton, "Innovative concepts and techniques of data analytics in edge computing paradigms," in Research Anthology on Edge Computing Protocols, Applications, and Integration. Hershey, PA, USA: IGI Global, 2022, pp. 182–196.
21. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proc. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR vol. 54, 2017, pp. 1273–1282.
22. S. Pandikumar, C. Menaka, and N. Sevugapandi, "Behavior prediction in social networks using feedforward neural network algorithm," in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 1–8, doi: 10.48001/978-81-980647-5-2-1.
23. A. T. Corbett and J. R. Anderson, "Knowledge tracing: Modeling the acquisition of procedural knowledge," User Modeling and User-Adapted Interaction, vol. 4, no. 4, pp. 253–278, 1994, doi: 10.1007/BF01099821.
24. A. Piech, J. Bassen, J. Huang, S. Ganguli, M. Sahami, L. J. Guibas, and J. Sohl-Dickstein, "Deep knowledge tracing," in Advances in Neural Information Processing Systems, vol. 28, 2015, pp. 505–513.
25. S. Pandikumar, S. B. Sethupandian, M. S. Saravanan, S. N. Prasad, and M. Arun, "Deep learning based long short-term memory recurrent neural network for stock price movement prediction," Indian Journal of Science and Technology, vol. 15, no. 11, pp. 474–480, 2022, doi: 10.17485/IJST/v15i11.27.
26. J. A. Kulik and J. D. Fletcher, "Effectiveness of intelligent tutoring systems: A meta-analytic review," Review of Educational Research, vol. 86, no. 1, pp. 42–78, 2016, doi: 10.3102/0034654315581420.
27. P. Savarimalai, M. Chellappan, M. S. Nidhya, T. Margaret Mary, S. Nallathambi, and M. Selvaraj, "A context-aware intelligent scheduling framework for modern operating systems using hybrid machine learning models," Journal of Computational and Cognitive Engineering, vol. 5, no. 2, pp. 321–332, 2026, doi: 10.47852/bonviewJCCE62026664.
28. G. Kestin, K. Miller, A. Klales, T. Milbourne, and G. Ponti, "AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting," Scientific Reports, vol. 15, Art. no. 17458, 2025, doi: 10.1038/s41598-025-97652-6.
29. H. Bastani, O. Bastani, A. Sungu, H. Ge, Ö. Kabakcı, and R. Mariman, "Generative AI without guardrails can harm learning: Evidence from high school mathematics," Proceedings of the National Academy of Sciences, vol. 122, no. 26, Art. no. e2422633122, 2025, doi: 10.1073/pnas.2422633122.
30. T. Margaret Mary, V. S. Prakash, K. S. Divya, and A. George, "Hybrid ML algorithms for learning disability forecast in school going children using Python in machine learning techniques," in Proc. 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2023, pp. 1043–1049, doi: 10.1109/ICECA58529.2023.10395259.
31. D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, "Chatting and cheating: Ensuring academic integrity in the era of ChatGPT," Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024, doi: 10.1080/14703297.2023.2190148.
32. D. Weber-Wulff, A. Anohina-Naumeca, S. Bjelobaba, T. Foltýnek, J. Guerrero-Dib, O. Popoola, P. Šigut, and L. Waddington, "Testing of detection tools for AI-generated text," International Journal for Educational Integrity, vol. 19, Art. no. 26, 2023, doi: 10.1007/s40979-023-00146-z.
33. V. S. Sadasivan, A. Kumar, S. Balasubramanian, W. Wang, and S. Feizi, "Can AI-generated text be reliably detected?" arXiv preprint arXiv:2303.11156, 2023, doi: 10.48550/arXiv.2303.11156.
34. W. Liang, M. Yuksekgonul, Y. Mao, E. Wu, and J. Zou, "GPT detectors are biased against non-native English writers," Patterns, vol. 4, no. 7, Art. no. 100779, 2023, doi: 10.1016/j.patter.2023.100779.
35. J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein, "A watermark for large language models," in Proc. 40th International Conference on Machine Learning (ICML), PMLR vol. 202, 2023, pp. 17061–17084.
36. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, "A novel framework for differential privacy based federated continual learning for dynamic medical data analysis," SN Computer Science, vol. 7, no. 6, Art. no. 664, 2026, doi: 10.1007/s42979-026-05245-5.
37. S. Mystakidis, "Metaverse," Encyclopedia, vol. 2, no. 1, pp. 486–497, 2022, doi: 10.3390/encyclopedia2010031.
38. C. Anusha, P. S. Ambili, P. D. Sowmya, A. Surendran, and T. Margaret Mary, "The role of AI technologies in data privacy and protection in the metaverse era: A detailed review," in Proc. International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE), 2025, pp. 712–717, doi: 10.1109/INSPIRE67328.2025.11300487.
39. A. Pardo and G. Siemens, "Ethical and privacy principles for learning analytics," British Journal of Educational Technology, vol. 45, no. 3, pp. 438–450, 2014, doi: 10.1111/bjet.12152.
40. M. V. Reddy, T. Margaret Mary, and M. Hanumanthappa, "Phoneme-to-speech dictionary for Indian languages," in Proc. International Conference on Soft-Computing and Networks Security (ICSNS), 2015, doi: 10.1109/ICSNS.2015.7292388.
41. L. Rello and M. Ballesteros, "Detecting readers with dyslexia using machine learning with eye tracking measures," in Proc. 12th International Web for All Conference (W4A), Florence, Italy, 2015, Art. no. 16, doi: 10.1145/2745555.2746644.
42. T. Margaret Mary and M. Hanumanthappa, "Hybrid classification approach HDLMM for learning disability prediction in school going children using data mining technique," Journal of Theoretical and Applied Information Technology, vol. 95, no. 13, pp. 2989–2998, 2017.
2. O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic review of research on artificial intelligence applications in higher education – where are the educators?" International Journal of Educational Technology in Higher Education, vol. 16, Art. no. 39, 2019, doi: 10.1186/s41239-019-0171-0.
3. S. Pandikumar, C. Suresh Kumar, M. Athigopal, and E. Jacob Jebaraj, "Impact of social media in higher education," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2018.
4. G. Siemens and P. Long, "Penetrating the fog: Analytics in learning and education," EDUCAUSE Review, vol. 46, no. 5, pp. 30–40, 2011.
5. S. Pandikumar, B. Maram, T. Margaret Mary, and Nageshwari, "Personalized and context-aware text suggestion using deep reinforcement learning," in Proc. International Conference on Future and Advanced Computing Technologies (ICFACT), Krishnankoil, India, 2026, pp. 1–7, doi: 10.1109/ICFACT66887.2026.11518436.
6. E. Kasneci et al., "ChatGPT for good? On opportunities and challenges of large language models for education," Learning and Individual Differences, vol. 103, Art. no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
7. S. Pandikumar, C. Menaka, T. Kiran, and T. John Paul Antony, "Evolution and analysis of modern plagiarism detection methods: A systematic review," in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 131–140, doi: 10.48001/978-81-980647-5-2-10.
8. C. Romero and S. Ventura, "Educational data mining and learning analytics: An updated survey," WIREs Data Mining and Knowledge Discovery, vol. 10, no. 3, Art. no. e1355, 2020, doi: 10.1002/widm.1355.
9. T. Foltýnek, N. Meuschke, and B. Gipp, "Academic plagiarism detection: A systematic literature review," ACM Computing Surveys, vol. 52, no. 6, Art. no. 112, 2019, doi: 10.1145/3345317.
10. S. Slade and P. Prinsloo, "Learning analytics: Ethical issues and dilemmas," American Behavioral Scientist, vol. 57, no. 10, pp. 1510–1529, 2013, doi: 10.1177/0002764213479366.
11. R. S. Baker and A. Hawn, "Algorithmic bias in education," International Journal of Artificial Intelligence in Education, vol. 32, no. 4, pp. 1052–1092, 2022, doi: 10.1007/s40593-021-00285-9.
12. K. E. Arnold and M. D. Pistilli, "Course Signals at Purdue: Using learning analytics to increase student success," in Proc. 2nd International Conference on Learning Analytics and Knowledge (LAK), Vancouver, BC, Canada, 2012, pp. 267–270, doi: 10.1145/2330601.2330666.
13. J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open University Learning Analytics dataset," Scientific Data, vol. 4, Art. no. 170171, 2017, doi: 10.1038/sdata.2017.171.
14. T. Margaret Mary, K. Soumya, G. Ramanathan, and G. Clinton, "An assessment on classification in Python using data science," in Proc. 3rd International Conference on Inventive Research in Computing Applications (ICIRCA), 2021, pp. 551–555.
15. J. Gardner and C. Brooks, "Student success prediction in MOOCs," User Modeling and User-Adapted Interaction, vol. 28, no. 2, pp. 127–203, 2018, doi: 10.1007/s11257-018-9203-z.
16. B. Arivazhagan, S. Pandikumar, S. B. Sethupandian, and R. S. Subramanian, "Pattern discovery and analysis of customer buying behavior using association rules mining algorithm in e-commerce," in Proc. International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022, pp. 1–5, doi: 10.1109/ICEEICT53079.2022.9768473.
17. H. Khosravi, S. Buckingham Shum, G. Chen, C. Conati, Y.-S. Tsai, J. Kay, S. Knight, R. Martinez-Maldonado, S. Sadiq, and D. Gašević,"Explainable artificial intelligence in education," Computers and Education: Artificial Intelligence, vol. 3, Art. no. 100074, 2022, doi: 10.1016/j.caeai.2022.100074.
18. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, "AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems," Results in Nonlinear Analysis, vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
19. H. Drachsler and W. Greller, "Privacy and analytics: It's a DELICATE issue. A checklist for trusted learning analytics," in Proc. 6th International Conference on Learning Analytics and Knowledge (LAK), Edinburgh, U.K., 2016, pp. 89–98, doi: 10.1145/2883851.2883893.
20. K. Soumya, T. Margaret Mary, and G. Clinton, "Innovative concepts and techniques of data analytics in edge computing paradigms," in Research Anthology on Edge Computing Protocols, Applications, and Integration. Hershey, PA, USA: IGI Global, 2022, pp. 182–196.
21. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proc. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR vol. 54, 2017, pp. 1273–1282.
22. S. Pandikumar, C. Menaka, and N. Sevugapandi, "Behavior prediction in social networks using feedforward neural network algorithm," in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 1–8, doi: 10.48001/978-81-980647-5-2-1.
23. A. T. Corbett and J. R. Anderson, "Knowledge tracing: Modeling the acquisition of procedural knowledge," User Modeling and User-Adapted Interaction, vol. 4, no. 4, pp. 253–278, 1994, doi: 10.1007/BF01099821.
24. A. Piech, J. Bassen, J. Huang, S. Ganguli, M. Sahami, L. J. Guibas, and J. Sohl-Dickstein, "Deep knowledge tracing," in Advances in Neural Information Processing Systems, vol. 28, 2015, pp. 505–513.
25. S. Pandikumar, S. B. Sethupandian, M. S. Saravanan, S. N. Prasad, and M. Arun, "Deep learning based long short-term memory recurrent neural network for stock price movement prediction," Indian Journal of Science and Technology, vol. 15, no. 11, pp. 474–480, 2022, doi: 10.17485/IJST/v15i11.27.
26. J. A. Kulik and J. D. Fletcher, "Effectiveness of intelligent tutoring systems: A meta-analytic review," Review of Educational Research, vol. 86, no. 1, pp. 42–78, 2016, doi: 10.3102/0034654315581420.
27. P. Savarimalai, M. Chellappan, M. S. Nidhya, T. Margaret Mary, S. Nallathambi, and M. Selvaraj, "A context-aware intelligent scheduling framework for modern operating systems using hybrid machine learning models," Journal of Computational and Cognitive Engineering, vol. 5, no. 2, pp. 321–332, 2026, doi: 10.47852/bonviewJCCE62026664.
28. G. Kestin, K. Miller, A. Klales, T. Milbourne, and G. Ponti, "AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting," Scientific Reports, vol. 15, Art. no. 17458, 2025, doi: 10.1038/s41598-025-97652-6.
29. H. Bastani, O. Bastani, A. Sungu, H. Ge, Ö. Kabakcı, and R. Mariman, "Generative AI without guardrails can harm learning: Evidence from high school mathematics," Proceedings of the National Academy of Sciences, vol. 122, no. 26, Art. no. e2422633122, 2025, doi: 10.1073/pnas.2422633122.
30. T. Margaret Mary, V. S. Prakash, K. S. Divya, and A. George, "Hybrid ML algorithms for learning disability forecast in school going children using Python in machine learning techniques," in Proc. 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2023, pp. 1043–1049, doi: 10.1109/ICECA58529.2023.10395259.
31. D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, "Chatting and cheating: Ensuring academic integrity in the era of ChatGPT," Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024, doi: 10.1080/14703297.2023.2190148.
32. D. Weber-Wulff, A. Anohina-Naumeca, S. Bjelobaba, T. Foltýnek, J. Guerrero-Dib, O. Popoola, P. Šigut, and L. Waddington, "Testing of detection tools for AI-generated text," International Journal for Educational Integrity, vol. 19, Art. no. 26, 2023, doi: 10.1007/s40979-023-00146-z.
33. V. S. Sadasivan, A. Kumar, S. Balasubramanian, W. Wang, and S. Feizi, "Can AI-generated text be reliably detected?" arXiv preprint arXiv:2303.11156, 2023, doi: 10.48550/arXiv.2303.11156.
34. W. Liang, M. Yuksekgonul, Y. Mao, E. Wu, and J. Zou, "GPT detectors are biased against non-native English writers," Patterns, vol. 4, no. 7, Art. no. 100779, 2023, doi: 10.1016/j.patter.2023.100779.
35. J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein, "A watermark for large language models," in Proc. 40th International Conference on Machine Learning (ICML), PMLR vol. 202, 2023, pp. 17061–17084.
36. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, "A novel framework for differential privacy based federated continual learning for dynamic medical data analysis," SN Computer Science, vol. 7, no. 6, Art. no. 664, 2026, doi: 10.1007/s42979-026-05245-5.
37. S. Mystakidis, "Metaverse," Encyclopedia, vol. 2, no. 1, pp. 486–497, 2022, doi: 10.3390/encyclopedia2010031.
38. C. Anusha, P. S. Ambili, P. D. Sowmya, A. Surendran, and T. Margaret Mary, "The role of AI technologies in data privacy and protection in the metaverse era: A detailed review," in Proc. International Conference on Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE), 2025, pp. 712–717, doi: 10.1109/INSPIRE67328.2025.11300487.
39. A. Pardo and G. Siemens, "Ethical and privacy principles for learning analytics," British Journal of Educational Technology, vol. 45, no. 3, pp. 438–450, 2014, doi: 10.1111/bjet.12152.
40. M. V. Reddy, T. Margaret Mary, and M. Hanumanthappa, "Phoneme-to-speech dictionary for Indian languages," in Proc. International Conference on Soft-Computing and Networks Security (ICSNS), 2015, doi: 10.1109/ICSNS.2015.7292388.
41. L. Rello and M. Ballesteros, "Detecting readers with dyslexia using machine learning with eye tracking measures," in Proc. 12th International Web for All Conference (W4A), Florence, Italy, 2015, Art. no. 16, doi: 10.1145/2745555.2746644.
42. T. Margaret Mary and M. Hanumanthappa, "Hybrid classification approach HDLMM for learning disability prediction in school going children using data mining technique," Journal of Theoretical and Applied Information Technology, vol. 95, no. 13, pp. 2989–2998, 2017.
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