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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. 

Abstract. Driven by foundation models, recent progress in AI and machine learning has reached unprecedented complexity. For instance, the GPT-3 language model consists of 175 billion parameters and a training-data size of 570 GB. While it has achieved remarkable performance in generating text that is difficult to distinguish from human-authored content, a single training of the model is estimated to produce over 550 metric tons of CO2 emissions. Likewise, we see advances in GeoAI research improving large-scale prediction tasks like satellite image classification and global climate modeling, to name but a couple. While these models have not yet reached comparable complexity and emissions levels, spatio-temporal models differ from language and image-generation models in several ways that make it necessary to (re)train them more often, with potentially large implications for sustainability. While recent work in the machine learning community has started calling for greener and more energy-efficient AI alongside improvements in model accuracy, this trend has not yet reached the GeoAI community at large. In this work, we bring this issue to not only the attention of the GeoAI community but also present ethical considerations from a geographic perspective that are missing from the broader, ongoing AI-sustainability discussion. To start this discussion, we propose a framework to evaluate models from several sustainability-related angles, including energy efficiency, carbon intensity, transparency, and social implications. We encourage future AI/GeoAI work to acknowledge its environmental impact as a step towards a more resource-conscious society. Similar to the current push for reproducibility, future publications should also report the energy/carbon costs of improvements over prior work. 

Abstract In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.