AcademAI: An Intelligent Framework for Automated Research Synthesis and Conference Recommendation

Abstract

This paper introduces AcademAI, an AI-powered framework that leverages automated synthesis of IEEE-standard research papers to reshape the manner in which knowledge related to software engineering is spread. AcademAI offers a single workspace that integrates Large Language Models with a “Human-First Encouragement AI” architecture to bridge the critical documentation gap between formal academic reporting and rapid software development. AcademAI uses a dual-pipeline approach: a Generative Synthesis Engine driven by the Gemini 2.0 Flash model and a Repository Analysis Subsystem that extracts architectural patterns and semantic context from GitHub repositories, in contrast to generic documentation tools or previous automated writing assistants that primarily serve as text editors. Additionally, the system offers a split-view, real-time authoring interface that can perform intelligent conference referencing, LaTeX serialization, and live IEEE formatting. In order to demonstrate AcademAI’s ability to lower the latency of knowledge transfer and democratize access to high-quality academic publishing, this paper describes its architectural design, algorithmic implementation, and theoretical foundations.

Introduction

Modern software engineering evolves faster than formal documentation, leaving a “documentation gap” where code repositories capture implementation details but not theoretical foundations, architectural decisions, or broader scientific context. Existing tools like README generators or static analyzers fail to translate code into coherent academic narratives suitable for peer-reviewed publications.

AcademAI is a Human-First Encouragement AI designed to bridge this gap by converting source code into fully formatted, IEEE-compliant research papers. Unlike conventional writing assistants, it interprets repositories as semantic graphs, extracts low- and high-level system logic, and generates structured academic content (Abstract, Methodology, Results, Conclusion, References). Features include:

• PaperHumanizer: Improves readability and natural flow of AI-generated text.

• Automated IEEE Formatting Engine: Produces real-time compliant HTML, LaTeX, and DOCX outputs.

• Context-Aware Conference Recommendation: Suggests relevant publication venues based on repository metadata.

AcademAI employs a microservices backend with FastAPI, asynchronous MongoDB, Google Gemini 2.0 Flash for LLM processing, and Redis for session management, paired with a responsive React frontend featuring split-view editing. It uses semantic translation, hierarchical code analysis, and prompt engineering to ensure the generated papers capture both the technical rigor and scholarly argumentation of traditional research.

The system significantly reduces the cognitive load of scientific writing for developers, enabling rapid, high-quality paper generation from code, while maintaining transparency, interactivity, and compliance with publication standards. AcademAI demonstrates potential to democratize academic publishing and integrate software engineering contributions into the scientific discourse.

Conclusion

AcademAI is a research generation model that advances the field of automated research generation, filling the gap between software repositories and academic prose with both a humanization pipeline and insight into software repositories. Single point workspace of the system, real time IEEE formatting, and conference recommendations is a comprehensive solution to the scholars and developers who intend to share their work. With the further development of LLMs, AcademAI similar frameworks will gain even more significance in accelerating the process of innovation and knowledge dissemination of the software engineering community and ensuring that the value of meaningful technical contributions is not lost in the documentation gap.

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Copyright

Copyright © 2025 Amol Bhilare, Varad Kulkarni, Aniket Kalbhor, Jannu , Karan Harshey. 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.