This seminar explores how to make AI-assisted academic research verifiable, meaning inspectable, reproducible, and defensible by turning vague prompting into bounded, auditable workflows. It offers practical methods for building audit trails, testing workflow reliability, and documenting AI use in ways that support publications and scholarly integrity.
This seminar explores how to treat artificial intelligence as a configurable research method rather than a chatbot or generic software tool. It provides a rigorous framework for validating AI-enabled workflows, designing defensible studies, and making scientific claims that remain consistent with the available evidence...
This workshop explores how AI can be used responsibly across the research lifecycle, from literature discovery and question development to computational analysis, writing, and publication and gives a practical framework for deciding what AI can assist with and what must remain human-led.
This seminar explores how agentic AI can be integrated into academic research as a disciplined, verifiable, and human-led method rather than a generic productivity tool. It provides practical frameworks for responsible use, workflow verification, and multi-agent research design across literature work, analysis, writing...
This work presents a safety-by-design assurance architecture combining model-level supervision, such as Scientist AI, with system-level controls over scaffolds and harnesses, independent verification, monitoring, and evidence infrastructure, supported by governance for accountability and evidence interoperability.
This seminar explores responsible AI as a practical research skill for scholars who use AI in their research practices, including searching, summarizing, coding, analyzing, writing, reviewing, translation, translation, and research administration.
This “Practitioner’s Digest” column highlights emerging software engineering for AI research presented at the IEEE/ACM International Conference on Software Engineering 2026, focusing on reliability, runtime safety, sustainability, requirements alignment, and scalable AI operations.
S. Abrahão, Miroslaw Staron, S. Abrahão et al.· IEEE Software· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
