ThesisPen AI

Project Overview

The ThesisPen system is an advanced natural language processing (NLP) application designed to automatically generate comprehensive thesis papers on any academic topic. In an era where students and researchers face significant time constraints, this tool offers a critical solution to help jumpstart the thesis writing process by providing well-structured, content-rich documents that can be further refined.

The system analyzes user inputs about a project topic and description, then leverages large language models to generate structured academic content including a table of contents, abstract, and full thesis chapters. By leveraging sophisticated AI models and NLP techniques, it can produce coherent, well-researched academic documents with proper citations and references.

Key Features

Technologies Used

Implementation Details

Data Processing

The system employs sophisticated text processing techniques to gather and analyze research materials. It connects to the ArXiv API to retrieve relevant academic papers based on the thesis topic, then processes these papers using NLP techniques to extract key information, including abstracts, authors, and publication dates. The system ranks these papers by semantic similarity to the thesis topic using TF-IDF vectorization and cosine similarity metrics to ensure only the most relevant sources are incorporated.

Content Generation

To transform user inputs into comprehensive thesis documents, the system utilizes carefully crafted prompt templates that guide language models in generating academically appropriate content. Each section of the thesis has specialized templates that ensure consistency in tone, style, and academic rigor throughout the document. The system intelligently incorporates citations from retrieved academic papers to support the generated content.

Parallel Processing & Document Assembly

For efficiency, the system implements multi-threading to generate different thesis sections simultaneously. This parallel approach significantly reduces overall generation time. The final content is assembled into a properly formatted academic document using the Python-docx library, maintaining consistent formatting, proper citation styles, and a coherent structure throughout.

Implementation Challenges

Several technical challenges were overcome during system development:

• Academic Rigor: Ensuring generated content maintained academic standards and factual accuracy across disciplines
• Prompt Engineering: Developing templates that consistently produced appropriate academic content across diverse topics
• API Optimization: Implementing efficient parallel processing without exceeding rate limits
• Content Integration: Seamlessly incorporating retrieved academic papers with proper citations
• Balance: Finding the right mix between general content generation and specialized academic writing requirements

Results & Impact

The ThesisPen system successfully generates comprehensive thesis documents with minimal user input:

• Reduces thesis planning and drafting time by up to 70%, allowing researchers to focus on refinement
• Produces content that follows academic writing standards with proper structure and formatting
• Integrates relevant citations from academic sources to support generated content
• Provides a powerful starting point that can be further customized and expanded
• Allows academic writers to focus more on original research rather than basic content development

Future Enhancements

• Integration with reference management systems like Zotero and Mendeley
• Enhanced semantic analysis for more precise research paper relevance ranking
• Development of discipline-specific templates for specialized academic fields
• Implementation of advanced plagiarism detection and factual verification
• Addition of collaborative editing features for research teams
• Support for multiple citation styles (MLA, Chicago, IEEE) beyond APA