AI Seminars
We offer short training seminars on applied AI methods and LLM workflows that are becoming essential for research, analytics, and data-intensive work across academia and industry.
Cutting-edge. Hands-on. Complete.
Public Seminars
AI Horizons offers public AI and LLM training seminars. We help participants and clients build a strong foundation in LLM workflows (using tools like ChatGPT, Claude, and Gemini), prompting strategies, and AI-assisted approaches that can be applied with confidence in real projects.
Looking for customized training for your organization? See our onsite AI seminar options.
Public Seminars
Each course features small group sizes, top-notch instructors, and extensive hands-on practice. Participants will use their own laptop computers, pre-loaded with relevant software and datasets provided prior to the beginning of the seminar.
Our upcoming seminars are listed below—click a course title for details and registration. All courses are livestreamed via Zoom. Video recordings are available to participants for an additional four weeks after the seminar ends.
Certification pathways are available as well.
Longitudinal Data Analysis Using R and LLMs
This course provides a solid foundation in longitudinal data analysis in R while also equipping you with a set of structured prompts to use with your large language model (LLM) of choice.
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Claude-Powered Academic Research: From Ideation to Publication
August 18-21, 2026
Learn how to use Claude as a systematic research partner for ideation, data collection, analysis, reproducibility, and publication-ready academic writing.
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Automatic Item Generation and Validation
August 20-21, 2026
Learn a new way to create and validate questionnaires and scales using AI, specifically large language models (LLMs), and advanced network psychometric techniques.
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AI Agent-Driven Research Workflows
August 25-28, 2026
Learn simple, structured ways to use multiple AI tools together to reduce low-value work while avoiding hidden errors.
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Turn Documents into Data with AI
September 10-12, 2026
Transform documents into structured, analyzable data using AI-assisted codebooks, extraction workflows, and reliability checks.
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Multilevel and Mixed Models Using R and LLMs
September 16-18, 2026
In addition to providing a solid foundation in using mixed models in R, this course will also equip you with a set of structured prompts to use with your large language model (LLM) of choice.
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AI Agents for Teaching
September 24-26, 2026
Explore how AI agents can be used to improve teaching, strengthen assessment, and create more engaging learning environments.
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Text Classification with LLMs in R
October 7-9, 2026
Learn to use natural language processing (NLP) techniques and large language transformer models (LLMs) for research applications using R.
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Getting Work Done with AI Agents
October 21-23, 2026
Discover how to use AI agents as research collaborators by building agent-ready workspaces, project memory systems, and repeatable workflows.
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Python for R Users with AI Assistance
October 22-23, 2026
Apply your existing R experience to build practical Python skills, using AI coding assistants to translate code, troubleshoot errors, and develop workflows for data analysis, visualization, and basic machine learning.
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AI Tools for Data Analysis: From Chatbots to Coworkers
October 29-30, 2026
Explore how to use AI tools and coding agents to clean and analyze data, create visualizations and dashboards, automate workflows, and verify outputs for more reliable, shareable results.
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AI-Based Emotion Analysis for Text, Image, and Video
November 4-6, 2026
Analyze emotions and sentiment across text, images, and video using transformer AI models in R, while learning to score, evaluate, and report multimodal results transparently.
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LLM Agents for Research Using R
December 2-4, 2026
Build custom AI research agents in R that can search, retrieve, analyze, and verify information to support literature reviews, research design, and more.
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R for SPSS Users with LLM Applications
December 9-11, 2026
Learn how to translate SPSS workflows into R code while using LLMs to debug, improve analyses and visualizations, convert syntax, and navigate common coding challenges.
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