AI Enhanced Offline Notes Tutor with Intelligent Difficulty Heatmap

Abstract

In this project, our target is to create an offline notes tutor for students, which will enable them to make efficient use of their PDFs. This system will take the input from the PDF and analyze it using various NLP techniques. It will also compute the complexity of each portion of the notes using machine learning techniques. Then, it will present the output using a heatmap technique, making it easy for students to understand which portion of the notes needs more effort. As this system runs entirely offline, it minimizes the risk of getting distracted by other elements online.

Introduction

It explains that while digital learning resources are widely used, most existing systems are online, dependent on internet access, and do not analyze study content itself. They also lack tools to highlight difficult topics, making revision less efficient.

To address this, the proposed system works completely offline on a local machine. It allows users to upload PDFs, extract text using parsing and OCR techniques, and then apply natural language processing (NLP) to evaluate content complexity. The system visualizes difficult sections using a heatmap, helping students focus on challenging areas and improve revision efficiency.

The literature review shows that although AI-based tutoring systems are increasingly common, most are web-based and tutor-centered, not document-centered or offline, creating a gap this system aims to fill.

The system is modular, including components for file upload, text extraction, content analysis, quiz generation, visualization, and offline processing. It also generates practice questions and reports.

Technologies used include Python, Flask, OCR tools, NLP libraries, and web technologies, with local storage via SQLite.

Conclusion

In conclusion, this research project proposes a successful offline means for study method analysis and improvement through AI. The system uses text extraction, NLP techniques, and visualization to help learners comprehend their study material more effectively. The modular nature of the system makes maintenance and future extension straightforward. This approach enables focused study without the need for internet- based approaches, making it accessible to a wider range of students.

References

[1] Al-Emran, “AI in Intelligent Tutoring Systems,” Int. J. Emerging Technologies in Learning, vol. 20, no. 1, pp. 1–18, 2025. [2] C. Dong, “Adaptive AI Tutor Using KG-RAG,” in Proc. IEEE Int. Conf. on Educational Technology, pp. 45–52, 2024. [3] Y. Guo, “Visualization of Intelligent Tutor Interactions,” Computers & Education, vol. 198, p. 104753, 2023. [4] IGI Global, Personalized Learning with Intelligent Tu- tors. Hershey, PA: IGI Global, 2023. [5] M. Smith and J. Doe, “PyPDF2: A Python library for PDF manipulation,” J. Open Source Software, vol. 6, no. 58, p. 2982, 2021. [6] G. A. Miller, “The magical number seven, plus or minus two: Some limits on our capacity for processing infor- mation,” Psychological Review, vol. 63, no. 2, pp. 81–97, 1956. [7] R. Flesch, “A new readability yardstick,” J. Applied Psychology, vol. 32, no. 3, pp. 221–233, 1948. [8] M. Kincaid et al., “Derivation of new readability for- mulas for Navy enlisted personnel,” Naval Technical Training Command, Memphis, TN, Research Branch Report 8–75, 1975. [9] A. Vaswani et al., “Attention is all you need,” Adv. Neural Inf. Process. Syst., vol. 30, 2017. [10] X. Chen et al., “Intelligent tutoring systems in higher education: A review,” Computers & Education, vol. 144, p. 103700, 2020. [11] B. D. Nye, “Intelligent tutoring systems by the numbers: A review of 50+ years of research,” in Artificial Intelli- gence in Education, Springer, 2015, pp. 1–12. [12] H. Wang, Y. Zhang, and X. Ma, “Deep learning for behaviour recognition in classroom surveillance videos,” IEEE Trans. Learning Technologies, vol. 15, no. 1, pp. 24–36, 2022. [13] S. Pandey and G. Karypis, “A self-attentive model for knowledge tracing,” in Proc. AIED, Springer, 2019, pp. 405–415. [14] Y. Kim, H. Lee, and G. Lee, “EdNet: A large-scale hier- archical dataset in education,” in Proc. AIED, Springer, 2020, pp. 39–43. [15] S. Ramesh, R. S. Rajesh, and A. N. Rajagopalan, “Au- tomated question generation using generative adversarial networks and LSTM encoders,” Procedia Computer Sci- ence, vol. 172, pp. 251–258, 2020. [16] A. Mishra and S. Kumar, “Comparative analysis of tra- ditional and ensemble classifiers for student performance prediction,” Education and Information Technologies, vol. 26, no. 3, pp. 2999–3017, 2021. [17] L. Zhang and Y. Xu, “A hybrid decision-making ap- proach for education quality evaluation using entropy weight and improved TOPSIS,” Expert Systems with Applications, vol. 184, 2021. [18] T. Chen et al., “A simple framework for contrastive learning of visual representations,” in Proc. ICML, 2020. [19] B. Vishwanath and S. Vaddepalli, “Enhancing student engagement and performance with artificial intelligence,” Int. J. Educ. Technol., vol. 19, no. 1, pp. 126–146, 2025. [20] S. L. Jagannadham et al., “Brain tumour detection using CNN,” in 2021 5th Int. Conf. I-SMAC, Palladam, India, [21] pp. 734–739, doi: 10.1109/I-SMAC52330.2021.9640875. [22] D. Venkatareddy et al., “Explainable fetal ultrasound classification with CNN and MLP models,” in 2024 1st Int. Conf. ICICEC, Davangere, India, pp. 1–7, doi: 10.1109/ICICEC62498.2024.10808626. [23] C. Piech et al., “Deep knowledge tracing,” in Adv. Neural Inf. Process. Syst., vol. 28, 2015. [24] M. Khajah, R. V. Lindsey, and M. C. Mozer, “How deep is knowledge tracing?” in EDM, 2016, pp. 94–101. [25] Y. Zhang et al., “Dynamic key-value memory networks for knowledge tracing,” in Proc. WWW, 2017, pp. 765– 774. [26] S. Ghosh, N. Heffernan, and A. S. Lan, “Context-aware knowledge tracing using transformer-based models,” in LAK, 2022.

Copyright

Copyright © 2026 Sk. Shabana Khanum, I. Mohana Naga Sindhuja, G. Keerthi, G. Nandini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.