This paper presents multi- and interdisciplinary approaches for finding the appropriate AI technologies for research information. Professional research information management (RIM) is becoming increasingly important as an expressly data-driven tool for researchers. It is not only the basis of scientific knowledge processes, but also related to other data. A concept and a process model of the elementary phases from the start of the project to the ongoing operation of the AI methods in the RIM is presented, portraying the implementation of an AI project, meant to enable universities and research institutions to support their researchers in dealing with incorrect and incomplete research information, while it is being stored in their RIMs. Our aim is to show how research information harmonizes with the challenges of data literacy and data quality issues, related to AI, also wanting to underline that any project can be successful if the research institutions and various departments of universities, involved work together and appropriate support is offered to improve research information and data management.

翻译：本文提出了多学科与跨学科方法，旨在为研究信息寻找合适的人工智能技术。专业的研究信息管理正日益成为研究者明确依赖的数据驱动工具，其重要性不断提升。它不仅是科学知识过程的基础，也与各类数据紧密关联。本文提出了从项目启动到人工智能方法在研究信息管理中持续运行的基础阶段概念与流程模型，描绘了人工智能项目的实施路径，旨在使高校与研究机构能够在其研究信息管理系统中存储信息的同时，协助研究者处理错误与不完整的研究信息。我们的目标是阐明研究信息如何与数据素养挑战及人工智能相关的数据质量问题相协调，同时强调：只有当研究机构与高校各部门协同合作，并提供适当支持以改进研究信息与数据管理时，任何项目才能取得成功。