How should AI be defined, validated, and reported when it becomes part of the research method itself? In this online Instats seminar for PhD students and professional researchers across the sciences and humanities, Professor Michael Zyphur examines AI not as a generic productivity tool, but as a configurable methodological component: an instrument, coder, analyst, simulator, critic, or orchestrator. Drawing on his work on Responsible AI for Academic Research and institutional AI readiness, the seminar offers a practical framework for designing AI-enabled studies, assessing validity, reliability, robustness, bias, reproducibility, and generalizability, and writing methods sections, limitations, and disclosure statements that can withstand peer review. Participants will learn how to distinguish incidental AI assistance from methodologically significant AI use, define human–AI divisions of labor, build validation structures, assess model stability, and align research claims with the strength of the supporting evidence. The workshop includes demonstrations, access to recordings and materials for 30 days, a monitored Q&A forum, and an official Instats certificate of completion. It is also part of the 4-part Agentic AI for Academic Research series, with a 10% discount available for participants who register for all four seminars. #HealthSciences #LifeSciences #NaturalSciences #SocialSciences #PhilosophyofScience #Statistics #DataScience #ComputerScience #InformationScience #Ethics #Epistemology #Education #ChatGPT #Claude #Gemini #Copilot #ClaudeCode #Codex #Grok #MistralVibe #Research #ResearchTraining #Instats

How can AI be used in research in ways that are not only efficient, but scientifically defensible? In this online seminar for PhD students and professional researchers, Professor Michael Zyphur examines AI as a configurable research method rather than a generic productivity tool, showing how its role can be specified, justified, validated, and reported across the sciences and humanities. Participants will learn how to distinguish AI as an instrument, coder, analyst, simulator, critic, or orchestrator; evaluate validity, reliability, robustness, bias, reproducibility, and generalizability; and design AI-enabled workflows with clear human–AI divisions of labor, validation structures, and defensible evidentiary boundaries. The seminar includes practical demonstrations and provides participants with an AI Method Pack to support stronger research designs, methods sections, disclosure statements, theses, manuscripts, grant applications, and responses to reviewer concerns. This workshop is part of the 4-part Agentic AI for Academic Research series, with a 10% discount for participants who register for all four. Livestream access is available via Zoom, with recordings, materials, a monitored Q&A forum, and an official Instats certificate of completion provided after the seminar. #HealthSciences #LifeSciences #NaturalSciences #SocialSciences #PhilosophyofScience #Statistics #DataScience #ComputerScience #InformationScience #Ethics #Epistemology #ChatGPT #Claude #Gemini #Copilot #ClaudeCode #Codex #Grok #MistralVibe #Research #ResearchTraining #Instats

AI is rapidly becoming part of the research process, but using it well requires more than simply stating that “AI was used.” In this online seminar for PhD students and professional researchers across the sciences and humanities, Professor Michael Zyphur examines AI as a configurable research method whose role must be clearly defined, justified, validated, and reported. Participants will learn how to distinguish AI as an instrument, coder, analyst, simulator, critic, or orchestrator; evaluate validity, reliability, robustness, bias, reproducibility, and generalizability; and design AI-enabled workflows with clear human–AI divisions of labor, validation structures, and defensible claims. The seminar includes practical demonstrations and provides an AI Method Pack to support stronger dissertations, manuscripts, grant applications, and peer-review responses. Offered via Zoom with 30-day access to recordings, materials, and a Q&A forum, this workshop is also part of the 4-part Agentic AI for Academic Research series, with a 10% discount for participants who enroll in all four seminars. #HealthSciences #LifeSciences #NaturalSciences #SocialSciences #PhilosophyofScience #Statistics #DataScience #ComputerScience #InformationScience #Ethics #Epistemology #ChatGPT #Claude #Gemini #MicrosoftCoPilot #ClaudeCode #Codex #Research #ResearchTraining #Instats

How can AI be used in research in ways that are methodologically defensible, transparent, and publishable? In this online seminar for PhD students and professional researchers, Professor Michael Zyphur examines AI not as a generic productivity tool, but as a configurable research method whose role, validation burden, human oversight, and inferential limits must be clearly specified. Participants will learn how to treat AI as an instrument, coder, analyst, simulator, critic, or orchestrator; evaluate validity, reliability, robustness, bias, reproducibility, and generalizability; design AI-enabled qualitative, quantitative, mixed-methods, and synthesis studies; and produce stronger methods sections, disclosure statements, and reviewer responses. The seminar includes demonstrations, practical frameworks, and an AI Method Pack with tools such as a Role Map, Validation Matrix, Protocol, Claim Ladder, reproducibility checklist, and reporting framework. It is part of the 4-part Agentic AI for Academic Research series, with a 10% discount for participants who register for all four: https://instats.org/course/agentic-ai-for-academic-research1. Livestream access is via Zoom, with recordings, materials, Q&A support, and an official Instats certificate available after the seminar. #HealthSciences #LifeSciences #NaturalSciences #SocialSciences #PhilosophyofScience #Statistics #DataScience #ComputerScience #InformationScience #Ethics #Epistemology #ChatGPT #Claude #Gemini #MicrosoftCoPilot #ClaudeCode #Codex #Research #ResearchTraining #Instats