Autonomous driving paper index
Bridging the Digital Divide: Equitable Access to AI-Enhanced Geographical Work Integrated Learning in Marginalised Landscapes
One-line summary
This chapter confronts the alluring promise of Artificial Intelligence (AI) in geographical Work-Integrated Learning (WIL) with the stark reality of its potential to perpetuate and deepen existing socio-spatial inequalities.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This chapter confronts the alluring promise of Artificial Intelligence (AI) in geographical Work-Integrated Learning (WIL) with the stark reality of its potential to perpetuate and deepen existing socio-spatial inequalities. It addresses the critical issue of how the integration of AI risks exacerbating the very disparities it claims to resolve. While AI purports to democratise access through virtual placements and sophisticated analytics, its implementation often assumes the presence of robust digital infrastructure and high AI literacy, thereby creating a new "geo-digital divide" for students in marginalised geographies, including rural, low-income, and Global South contexts. Through a systematic literature review of relevant academic scholarship, framed by a synthesis of Critical Digital Pedagogy and Spatial Justice theories, this chapter interrogates the normative assumptions underpinning "high-tech" WIL models. Findings reveal that AI tools can perpetuate epistemic injustice without deliberate intervention by marginalising local knowledge and reinforcing technological dependency. Conversely, the chapter identifies emergent, innovative strategies such as mobile-first hybrid placements, community-partnered projects employing participatory geospatial technologies, and embedded "critical AI literacy" modules that leverage appropriate technology to foster inclusive and contextually relevant geographical WIL. Thus, this study provides a robust, theoretically grounded framework for the equitable design of AI-enhanced geographical WIL. The chapter advocates for asset-based partnerships among educators and policymakers, the co-design of curricula with local communities, and a pedagogical reorientation that utilises AI to amplify, rather than replace, situated geographical understanding in order to empower spatial citizenship for all.
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