From literature review to publication, faster.

Use AI to brainstorm, refine, and critically evaluate research questions, and to support rigorous, well-aligned study designs.

Develop and refine research questions through advanced brainstorming, hypothesis exploration, and structured interrogation of underlying assumptions.

Use AI-powered literature tools to find relevant research efficiently, compare search approaches, and assess the strengths and limits of different platforms.

Conduct more rigorous and comprehensive literature reviews by combining advanced discovery, systematic review, and evidence-synthesis strategies — and stay on top of the field.

Use AI tools to support quantitative and qualitative analysis — from survey design and statistical exploration to coding, thematic analysis, and transcription.

Develop and strengthen research ideas using AI as a structured writing partner — from early conceptualization through proposal drafting, revision, and peer-review simulation.

Identify relevant funding opportunities, interpret complex calls — including Horizon Europe examples — translate funder requirements into a proposal strategy, and build strong consortia.

Develop a strategic approach to journal selection by matching a manuscript's scope, audience, and contribution with appropriate venues — and by spotting predatory or unsuitable journals.

Develop a forward-looking publication strategy by identifying emerging topics, shifts in scholarly attention, and areas of growing research momentum.

Build a credible author platform and adapt research into engaging formats — presentations, podcasts, videos, reports, and visual materials — for different audiences.

Explore the playful side of generative AI — music, image transformation, voice interaction, and personalized digital avatars — and see how those capabilities support creativity, engagement, and communication.

Develop advanced strategies for making research discoverable and compelling across both human- and AI-mediated channels.

Explore how AI is reshaping the research ecosystem — from literature discovery and publication workflows to peer review, disclosure, and research integrity.

Examine how researchers can retain control over AI by making deliberate choices about privacy, personalization, trust, and which intellectual tasks should remain human-led.

Understand the core privacy, copyright, and research-integrity risks associated with using AI across the research workflow.

Distinguish between legitimate AI assistance, plagiarism, and inappropriate substitution of authorship or scholarly judgment.

Understand how funders, journals, and the European Commission regulate AI use across grant development, peer review, and scholarly publication.

Design a practical AI policy tailored to the needs, risks, and workflows of a specific institution or department.

Strengthen advanced prompting by designing clear, context-rich instructions and iteratively improving outputs for complex research and communication tasks.

Participants begin with a real research or research-support task rather than a predetermined AI tool. They identify a worthwhile opportunity, define what the customized tool should do, build a useful first version, turn the successful elements into a reusable workflow, consider whether additional knowledge or connections would improve it, identify where human judgment should remain in control, and test the result on new cases.

Use Microsoft Copilot strategically within university research and administrative workflows, recognizing where it adds value and where human-led or alternative approaches are more appropriate.

Compare the core capabilities of leading frontier AI models — reasoning, web research, image generation, coding, data analysis, and multimodal understanding.