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ABSTRACT GIS and GIScience education have continually evolved over the past three decades, responding to technological advances and societal issues. Today, the content and context in which GIScience is taught continue to be impacted by these disruptions, notably from technology through artificial intelligence (AI) and society through the myriad environmental and social challenges facing the planet. These disruptions create a new landscape for training within the discipline that is affecting not onlywhatis taught in GIScience courses but alsowhois taught,whyit is being taught, andhowit is taught. The aim of this paper is to structure a direction for developing and delivering GIScience education that, amid these disruptions, can generate a capable workforce and the next generation of leaders for the discipline. We present a framework for understanding the various emphases of GIScience education and use it to discuss how the content, audience, and purpose are changing. We then discuss how pedagogical strategies and practices can change how GIScience concepts and skills are taught to train more creative, inclusive, and empathetic learners. Specifically, we focus on how GIScience pedagogy should (1) center on problem‐based learning, (2) be open and accelerate open science, and (3) cultivate ethical reasoning and practices. We conclude with remarks on how the principles of GIScience education can extend beyond disciplinary boundaries for holistic spatial training across academia. 

The number of reproduction and replication studies undertaken across the sciences continues to rise, but such studies have not yet become commonplace in geography. Existing attempts to reproduce geographic research suggest that many studies cannot be fully reproduced, or are simply missing components needed to attempt a reproduction. Despite this suggestive evidence, a systematic assessment of geographers’ perceptions of reproducibility and use of reproducible research practices remains absent from the literature, as does an identification of the factors that keep geographers from conducting reproduction studies. We address each of these needs by surveying active geographic researchers selected using probability sampling techniques from a rigorously constructed sampling frame. We identify a clear division in perceptions of reproducibility among geographic subfields. We also find varying levels of familiarity with reproducible research practices and a perceived lack of incentives to attempt and publish reproduction studies. Despite many barriers to reproducibility and divisions between subfields, we also find common foundations for examining and expanding reproducibility in the field. These include interest in publishing transparent and reproducible methods, and in reproducing other researchers’ studies for a variety of motivations including learning, assessing the internal validity of a study, or extending prior work. 

Despite recent calls to make geographical analyses more reproducible, formal attempts to reproduce or replicate published work remain largely absent from the geographic literature. The reproductions of geographic research that do exist typically focus on computational reproducibility—whether results can be recreated using data and code provided by the authors—rather than on evaluating the conclusion and internal validity and evidential value of the original analysis. However, knowing if a study is computationally reproducible is insufficient if the goal of a reproduction is to identify and correct errors in our knowledge. We argue that reproductions of geographic work should focus on assessing whether the findings and claims made in existing empirical studies are well supported by the evidence presented. We aim to facilitate this transition by introducing a model framework for conducting reproduction studies, demonstrating its use, and reporting the findings of three exemplar studies. We present three model reproductions of geographical analyses of COVID‐19 based on a common, open access template. Each reproduction attempt is published as an open access repository, complete with pre‐analysis plan, data, code, and final report. We find each study to be partially reproducible, but moving past computational reproducibility, our assessments reveal conceptual and methodological concerns that raise questions about the predictive value and the magnitude of the associations presented in each study. Collectively, these reproductions and our template materials offer a practical framework others can use to reproduce and replicate empirical spatial analyses and ultimately facilitate the identification and correction of errors in the geographic literature.