MindMap Mentor: AI-Generated Concept Maps

Abstract

In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student’s understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of the concepts identified in the concept map developed by the student. The evaluation of a student’s understanding of the topic is assessed by analyzing the curve of the graph generated by this tool. This technique makes extensive use of XML parsing to perform the required evaluation. The tool was successfully tested with students from two undergraduate courses and the results of testing are described in this paper.

Introduction

The text describes Mind Map Mentor, an AI-based educational system that automatically converts text or study material into visual concept maps. It uses Natural Language Processing (NLP) and machine learning to extract key concepts and relationships, helping students understand complex topics more easily and efficiently compared to traditional manual note-making.

The literature review highlights that concept maps improve learning and memory, but manual creation is time-consuming. Recent research shows that AI and large language models can automate concept map generation, making the process faster and more effective.

The existing systems rely mostly on manual creation or basic tools without AI support, making them inefficient and dependent on user effort. In contrast, the proposed system automates the entire process by analyzing input text and generating structured, interactive concept maps. It also allows users to edit, customize, and export maps.

The system includes key modules such as a user-friendly interface, an AI assistant for answering questions, a mind map generation module, and a key concept extraction module. Overall, it enhances learning by combining AI with visual representation, making studying simpler, faster, and more interactive.

Conclusion

The Mind Map Mentor AI project demonstrates how artificial intelligence can enhance learning by automatically generating structured concept maps from complex information. By transforming unorganized content into visually connected ideas, the system helps users better understand relationships, improve memory retention, and simplify knowledge acquisition. Throughout the project, we explored techniques in natural language processing, knowledge representation, and intelligent visualization. The AI model successfully analyzed input data, identified key concepts, and organized them into meaningful hierarchical and associative structures. This reduces the manual effort required to create mind maps and makes the learning process more efficient and interactive.

References

[1] Novak, J. D., & Cañas, A. J. (2008). The Theory Underlying Concept Maps and How to Construct Them. Florida Institute for Human and Machine Cognition. (Foundational work on concept maps and knowledge representation) [2] Buzan, T. (2018). The Mind Map Book: Unlock your creativity, boost memory, change your life. BBC Active. (Core reference for traditional mind mapping techniques) [3] Sweller, J. (2011). Cognitive Load Theory. Psychology of Learning and Motivation, 55, 37–76. (Useful for explaining why structured visual learning like mind maps is effective) [4] Mayer, R. E. (2009). Multimedia Learning (2nd Edition). Cambridge University Press. (Supports AI-based visual learning systems like concept map generation)

Copyright

Copyright © 2026 P. Jhansi Lakshmi, P. Jyothi, T. Venkata Kumari, V. Manasa. 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.