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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

An 8-Hour Livestream Seminar Taught by Hudson Golino, Ph.D.

A Network Integrated Approach using Large Language Models in R

This innovative course introduces a new way to create and validate questionnaires and scales using artificial intelligence, specifically large language models (LLMs).

In this course you will learn a fully automated scale development and validation in silico using R. You will learn to leverage large language models (LLMs) and advanced network psychometric techniques to develop new items using Large Language Models and do a complete structural validation process without collecting data in humans. This significantly reduces the time and resources traditionally required for scale development.

In simple terms, we’ll teach you how to:

1. Use AI to automatically generate questions for new scales.
2. Check if these items are good at measuring what they’re supposed to measure (structural validity) and if the items and dimensions are stable (dimensionality and item stability).
3. Do all of this without needing to test the questions on real people first.

Traditionally, creating a good questionnaire or test (which we call a “scale” in research) takes a lot of time and money. It usually involves writing many questions, testing them on hundreds of people, and then using complex math to figure out which questions work best.

Our course shows you how to do all this using R and AI. This new method can save researchers and businesses a lot of time and resources.

Automatic Item Generation and Validation via Network Integrated Evaluation (AI-Genie) leverages large language models (LLMs) and advanced network psychometric techniques to streamline the item generation and validation process without the need to collect data in humans. Traditional scale development is resource-intensive, time-consuming, and costly, often requiring extensive human expert intervention and costly data collection for psychometric validation.

Recent advancements in AI and LLMs offer promising solutions to generate expert-quality text for scale items. The challenge lies in efficiently selecting and validating non-redundant, high-quality items that accurately represent intended psychological constructs, and that present adequate dimensionality (structural validity) AND item/dimension stability. AI-GENIE automates the entire process, from item generation to validation, enhancing efficiency and scalability in psychological assessment creation.

Previous research has shown that AI-generated items can create adequate psychological assessments, but the item selection process remains resource-intensive (with rounds of in-human data collection). AI-GENIE eliminates the need for extensive human expert involvement in generating, selecting, and validating items, potentially saving researchers significant time and money. The methodology combines open and closed-source LLMs, generative AI, and network psychometrics to facilitate scale generation, selection, and validation. AI-GENIE is the first fully automated methodology to generate, assess, and validate the quality of AI-generated items for psychometric scales.

By the end of this course, you will be able to:

1. Understand the principles and applications of AI-GENIE in scale development.
2. Utilize various LLMs for item generation in R.
3. Apply network psychometric techniques for item validation and selection in silico, using R.
4. Critically evaluate the effectiveness and limitations of AI-generated items.
5. Design and implement a full-scale development project using AI-GENIE methodology.