[RCAC Workshop]AI in Scientific Research & Education
📅 Date: May 1st 2026 ⏰ Time: 1:00PM 💻 Location: Virtual 🏫 Instructor: Ashish
Description AI is creating new opportunities across both scientific research and education, but it also introduces governance challenges that institutions must address thoughtfully. This session examines how AI can support research discovery, analysis, drafting, teaching, tutoring, assessment, and administrative workflows, while also highlighting the risks related to integrity, confidentiality, privacy, learner outcomes, and institutional trust. Rather than treating research and education as the same problem, this workshop explores how each domain has distinct governance needs, along with a shared need for transparent accountability and evidence-based oversight. Through policy context, practical governance tools, and real-world case studies, we will discuss how institutions can enable innovation while protecting academic values and public trust.
Who Should Attend Faculty, researchers, academic technologists, research computing professionals, instructional staff, administrators, library and IT leaders, graduate students, and others involved in supporting or governing AI use in research and educational settings. This session is particularly relevant for those developing institutional guidance, evaluating AI-enabled workflows, or trying to balance innovation with integrity and privacy.
Topics Where AI can add value in scientific research and education The distinct governance challenges in research and in teaching and learning environments Core risks such as fabricated content, reproducibility issues, confidentiality concerns, privacy risks, over-reliance, and unequal access The evolving policy landscape, including UNESCO guidance, education quality signals, funder expectations, publisher norms, and data protection considerations Governance frameworks for lifecycle review, risk tiering, and evidence-based oversight Practical toolkits for research reproducibility, documentation, disclosure, academic integrity, and assessment redesign Institutional roles, decision rights, monitoring practices, and leadership metrics Lessons from case studies involving AlphaFold, Khanmigo, AI detection limits, and paper mills
Level Intermediate. Attendees should be broadly familiar with AI concepts, but no specialized expertise in governance, policy, or advanced technical systems is required. 🔗 Register now:LINK