打造跨领域科研助手，用AI全流程辅助研究创新。
Towards AI-Supported Research: a Vision of the TIB AIssistant
- 构建模块化平台，整合提示库、工具库与数据存储。
- 支持选题、文献分析、方法设计到写作的全流程科研任务。
- 适合各学科研究者，尤其适合缺乏AI技能的学者使用。
生成式AI和大语言模型的快速发展为科研工作带来变革机遇，有望显著提升学术研究效率。然而，将AI有效融入科研仍面临领域差异大、研究人员AI素养不足、工具与智能体协同复杂、生成内容准确性不明等挑战。本文提出TIB AIssistant的愿景：一个面向全学科领域的、人机协同的科研支持平台，旨在通过AI助手贯穿研究生命周期。平台包含模块化组件，如提示与工具库、共享数据存储、灵活编排框架，可支持研究构思、文献分析、方法开发、数据分析及学术写作。文中描述了概念框架、系统架构及早期原型实现，验证了该方案的可行性与潜在影响力。
原文摘要 · Abstract (English)
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.
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