My presentation explores the challenges and opportunities of integrating chatbots and generative AI into teaching geomatics courses. It reflects on the experience gained since the arrival of ChatGPT in 2022. Challenges include inequalities in access to technological tools, the risk that students overly rely on ready-made solutions (e.g., AI-generated code), and ethical issues such as plagiarism. Opportunities lie in personalized coding assistance (e.g., debugging with a chatbot), the creation of innovative geomatics projects (e.g., integration of advanced concepts), and the enhancement of skills when these tools are used as a pedagogical lever. By calling these new tools the substitute who knew too much, it aptly captures the concerns of the education community. However, it also presents an opportunity to rethink our teaching methods and transform geomatics learning when they are skillfully mastered.

Categorical covariates such as race, sex, or group are ubiquitous in regression analysis. While main-only (or ANCOVA) linear models are predominant, cat-modified linear models that include categorical-continuous or categorical-categorical interactions are increasingly important and allow heterogeneous, group-specific effects. However, with standard approaches, the addition of cat-modifiers fundamentally alters the estimates and interpretations of the main effects, often inflates their standard errors, and introduces significant concerns about group (e.g., racial) biases. We advocate an alternative parametrization and estimation scheme using abundance-based constraints (ABCs). ABCs induce a model parametrization that is both interpretable and equitable. Crucially, we show that with ABCs, the addition of cat-modifiers 1) leaves main effect estimates unchanged and 2) enhances their statistical power, under reasonable conditions. Thus, analysts can, and arguably should include cat-modifiers in linear regression models to discover potential heterogeneous effects—without compromising estimation, inference, and interpretability for the main effects. Using simulated data, we verify these invariance properties for estimation and inference and showcase the capabilities of ABCs to increase statistical power. We apply these tools to study demographic heterogeneities among the effects of social and environmental factors on STEM educational outcomes for children in North Carolina. An R package lmabc is available.