Publication Details
Authors: Sören Auer, Allard Oelen, Mohamad Yaser Jaradeh, Mutahira Khalid, Farhana Keya, Sasi Kiran Gaddipati, Jennifer D’Souza, Lorenz Schlüter
ArXiv: 2512.16447
Submitted: December 18, 2025
Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to:
- Varying domain requirements
- Limited AI literacy
- Complexity of coordinating tools and agents
- Unclear accuracy of Generative AI in research
Vision: TIB AIssistant
We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery. The platform includes AI assistants supporting tasks across the entire research life cycle.
Key Components
- Prompt and tool libraries - Reusable components for common research tasks
- Shared data store - Centralized knowledge management
- Flexible orchestration framework - Coordinate multiple AI agents
Supported Research Activities
The platform facilitates:
- Ideation and research planning
- Literature analysis and synthesis
- Methodology development
- Data analysis and validation
- Scholarly writing and communication
Prototype Implementation
We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach to AI-augmented research.