OpenAI Academy teaches how to use ChatGPT in research: sources, analysis, and citations

OpenAI Academy has released a training course "ChatGPT for research" — a practical guide for those who want to use a language model in systematic research work: from searching and analyzing sources to creating structured materials with proper citation.

Why this course is needed

Using ChatGPT in research remains one of the most contentious topics in academic and professional circles. On one hand, the tool clearly accelerates work with information: it allows quick immersion into a topic, synthesizing large volumes of text, and generating preliminary structures for reports. On the other hand, there are no established practices for doing this correctly without risking the reliability of results and academic integrity. Some universities introduce bans on AI in academic work, others require mandatory disclosure of AI use. Scientific journals develop their own policies. In this gap emerges OpenAI Academy's course: a structured guide with specific techniques and honest warnings about limitations.

Three stages covered by the course

The program is built around three sequential stages of any research process. Source collection. ChatGPT helps quickly orient oneself within a topic: finding key terms, mapping search directions, identifying related concepts.

The course teaches how to formulate queries to get specific and structured answers rather than general introductions to the topic. Information analysis. The model is capable of comparing arguments from multiple texts, identifying contradictions between sources, summarizing data, and building thematic summaries.

Particularly useful is the ability to maintain an iterative dialogue — refining queries within a single conversation, deepening analysis without losing context. The course covers techniques for working with texts in various formats: from scientific articles to industry reports. **Structuring and citation.

* How to format research conclusions while observing attribution rules when working with AI-generated content — this is one of the central questions of the course. The course reviews existing academic standards and recommendations. The practical part covers specific scenarios: Creating a source map on a given topic Comparing authors' viewpoints on a controversial issue Reducing large text to key points Writing a structured literature review Rules for attributing AI content in academic work ## Being honest about limitations OpenAI does not shy away from its tool's weak points.

"Hallucinations" — the key problem when using ChatGPT in research: the model regularly generates plausible but factually incorrect statements. In an academic context, this is particularly dangerous — ChatGPT can generate a nonexistent scientific article with a realistic-sounding title, plausible authors, and imitation DOI. Dates, precise figures, bibliographic data — all of this requires mandatory verification against primary sources.

The course recommends using ChatGPT as a navigator through a topic, while final verification should be conducted in real academic databases: Google Scholar, PubMed, Semantic Scholar, Web of Science.

"ChatGPT works best as a starting point for research, not as a final source of truth" — this is the principle underlying the

OpenAI Academy course.

What this means

OpenAI consistently moves from the position of "a universal tool for everything" toward creating specialized methodologies for specific professional contexts. An educational track for researchers is an important step toward establishing basic standards for using AI in academic environments, especially when universities and scientific publications are still developing their own policies regarding AI content. The more such resources appear — the faster professional culture of responsible AI use in science will develop.