Item Response Theory (IRT) is commonly used in educational assessments to model the relationship between one or more latent traits and the observed responses. Traditional IRT methods often rely on frequentist approaches, which can be limited by assumptions and computational constraints. This article aims to introduce school psychology researchers to Bayesian methods for IRT analyses, highlighting their advantages over traditional approaches. We provide an overview of Bayesian IRT and discuss key concepts that make up the Bayesian workflow. This workflow includes model and prior specification, prior predictive checks, model estimation and comparison, posterior distribution interpretation, posterior predictive checks, and prior sensitivity analyses. To illustrate this workflow, we used a sample of 329 teachers who completed the 16-item Teacher Stress Inventory -- Short Form (TSI-SF). Our Bayesian IRT analysis revealed that the TSI-SF is best represented by a three-correlated-traits model (measuring Discipline and Motivation, Professional Investment, and Work-Related Stress as sources of stress).