Sibonangaye Dick Nkalanga published a 2026 Frontiers in Education paper on how artificial intelligence is changing spatial analysis in Geography, with special attention to knowledge production in the Global South. The paper says AI-enhanced spatial tools are creating new analytical possibilities while also producing epistemological tensions, meaning disputes over how knowledge is made and validated.

The study used a qualitative, interpretive design based on critical geography, postcolonial theory, and science and technology studies. Nkalanga conducted a systematic literature review and thematic synthesis using 847 records from Scopus, Web of Science, Google Scholar, and ACM Digital Library covering 2015–2025. Of those records, 52 sources met inclusion criteria for geographic relevance, peer-review status, and full-text accessibility, and the screening followed a PRISMA-compatible protocol.

The findings say machine-learning-based remote sensing, predictive geospatial modelling, and large language model-assisted GIS are expanding Geography's analytical capabilities. The paper also identifies risks tied to algorithmic bias, data extractivism, and colonial spatial imaginaries being re-inscribed in computational form. Researchers in the Global South face structural barriers including data poverty, inadequate infrastructure, and exclusion from foundational AI spatial dataset development, which deepens North–South knowledge production asymmetries.

The paper recommends investment in sovereign spatial data infrastructure, AI literacy frameworks for geographical research, and ethical guidelines that foreground the epistemological rights of marginalised communities. It argues that technical adoption in Geography should be accompanied by critical epistemological vigilance.

Sibonangaye Dick Nkalanga published a 2026 Frontiers in Education paper on how artificial intelligence is changing spatial analysis in Geography, with special attention to knowledge production in the Global South. The paper says AI-enhanced spatial tools are creating new analytical possibilities while also producing epistemological tensions, meaning disputes over how knowledge is made and validated.

The study used a qualitative, interpretive design based on critical geography, postcolonial theory, and science and technology studies. Nkalanga conducted a systematic literature review and thematic synthesis using 847 records from Scopus, Web of Science, Google Scholar, and ACM Digital Library covering 2015–2025. Of those records, 52 sources met inclusion criteria for geographic relevance, peer-review status, and full-text accessibility, and the screening followed a PRISMA-compatible protocol.

The findings say machine-learning-based remote sensing, predictive geospatial modelling, and large language model-assisted GIS are expanding Geography's analytical capabilities. The paper also identifies risks tied to algorithmic bias, data extractivism, and colonial spatial imaginaries being re-inscribed in computational form. Researchers in the Global South face structural barriers including data poverty, inadequate infrastructure, and exclusion from foundational AI spatial dataset development, which deepens North–South knowledge production asymmetries.

The paper recommends investment in sovereign spatial data infrastructure, AI literacy frameworks for geographical research, and ethical guidelines that foreground the epistemological rights of marginalised communities. It argues that technical adoption in Geography should be accompanied by critical epistemological vigilance.