TIB AIssistant: a Platform for AI-Supported Research Across Research Life Cycles
Allard Oelen, Sören Auer
TL;DR
The paper addresses how AI, particularly LLMs, can support researchers throughout the research life cycle without replacing human judgment. It introduces the TIB AIssistant, a domain-agnostic platform organized as assistants, tools, and assets, enabling sequential workflows and RO-Crate provenance. The authors demonstrate a system walk-through showing ideation, literature discovery, writing, proofreading, and export to LaTeX/Overleaf, with Generative UI enabling tool-assisted interactions. They argue for a community-maintained, open-source ecosystem with no-code extension potential and future work on model diversification and domain-specific prompt/tool libraries.
Abstract
The rapidly growing popularity of adopting Artificial Intelligence (AI), and specifically Large Language Models (LLMs), is having a widespread impact throughout society, including the academic domain. AI-supported research has the potential to support researchers with tasks across the entire research life cycle. In this work, we demonstrate the TIB AIssistant, an AI-supported research platform providing support throughout the research life cycle. The AIssistant consists of a collection of assistants, each responsible for a specific research task. In addition, tools are provided to give access to external scholarly services. Generated data is stored in the assets and can be exported as an RO-Crate bundle to provide transparency and enhance reproducibility of the research project. We demonstrate the AIssistant's main functionalities by means of a sequential walk-through of assistants, interacting with each other to generate sections for a draft research paper. In the end, with the AIssistant, we lay the foundation for a larger agenda of providing a community-maintained platform for AI-supported research.