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Towards AI-Supported Research: a Vision of the TIB AIssistant

Sören Auer, Allard Oelen, Mohamad Yaser Jaradeh, Mutahira Khalid, Farhana Keya, Sasi Kiran Gaddipati, Jennifer D'Souza, Lorenz Schlüter, Amirreza Alasti, Gollam Rabby, Azanzi Jiomekong, Oliver Karras

TL;DR

The paper addresses the challenge of integrating Generative AI into diverse research domains by proposing a domain-agnostic, human-centered platform. It introduces the TIB AIssistant architecture with three core modules (Prompt Library, Tool Library, Data Store) and a Model Context Protocol to orchestrate AI agents across the research life cycle. Key contributions include a formal framework, design principles, and an initial prototype demonstrating feasibility and potential impact. The work aims to lower barriers to AI adoption, enhance transparency and reproducibility, and enable collaboration between researchers and AI agents.

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, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

Towards AI-Supported Research: a Vision of the TIB AIssistant

TL;DR

The paper addresses the challenge of integrating Generative AI into diverse research domains by proposing a domain-agnostic, human-centered platform. It introduces the TIB AIssistant architecture with three core modules (Prompt Library, Tool Library, Data Store) and a Model Context Protocol to orchestrate AI agents across the research life cycle. Key contributions include a formal framework, design principles, and an initial prototype demonstrating feasibility and potential impact. The work aims to lower barriers to AI adoption, enhance transparency and reproducibility, and enable collaboration between researchers and AI agents.

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, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

Paper Structure

This paper contains 19 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Vision of the TIB AIssistant
  3. Related Work
  4. Single Task Assistants
  5. Multi-Task Assistants
  6. Framework for AI-Assisted Research
  7. Core System Modules
  8. Prompt Library
  9. Tool Library
  10. Data Store
  11. System Design Principles
  12. Human-Machine Collaboration
  13. Personalization
  14. Customizability
  15. Transparency and Trustworthiness
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Proposed framework of AI-assisted research, highlighting system actors, external MCP (Model-Context Protocol) servers consisting of collections of tools, core system modules, and the system's design principles.
  • Figure 2: Example research life cycle starting from ideation until post-publication. The listed items provide concrete examples of the three core system modules, as shown in \ref{['fig:framework']}. External tools are listed, along with items stored within the data store.