This course helps PhD students strategically find, evaluate, and manage information resources across disciplines. Participants will learn to plan efficient search strategies, use key databases and tools, assess the quality and relevance of sources, and organize references for long-term use. By the end, participants will be able to build and maintain a robust, transparent, and manageable evidence base for their doctoral work.
| Target Public | Duration | Schedule | Certification | Language | Method | |
| PhD Students | 3x2hours | Feb 3rd, 27th and March 27th, 2026 (09:00 - 11:00) | Yes | English | Online |
The course is divided into three interconnected modules.
The first module focuses on the foundations of effective research and information discovery. Participants will learn how to conduct traditional searches using Boolean operators and academic databases, as well as how to apply complementary strategies such as snowballing, subject-heading exploration, and natural-language search. This module aims to strengthen participants’ ability to locate high-quality, relevant sources efficiently and systematically.
The second module introduces a range of tools designed to help participants organize and manage the information they have gathered. It will cover reference-management software, methods for annotating and storing research materials, and techniques for automating the citation and bibliographic process. By the end of this module, participants will have begun building their own research library.
The third module examines how artificial intelligence tools can support various stages of the research lifecycle: from refining search strategies to synthesising information. Alongside practical demonstrations, the module will also address the ethical considerations associated with using AI in academic settings, including issues of accuracy, transparency, bias, and responsible use.
By the end of the course, participants will be able to:
- Translate their research questions into search concepts;
- Build different search strategies based on their research questions and projects;
- Understand the logics of Boolean searches and how to apply them in multidisciplinary and subject-specific databases;
- Develop complementary search strategies, such as snowballing and natural language searches;
- Manage and reference sources with the use of reference manager software;
- Integrate AI tools into their research process in different phases: brainstorming,collecting and summarizing information.