Immersive Digital Technologies: A New Horizon in Geography Education
Bibliographic record
Abstract
In the rapidly evolving landscape of geographical education, the integration of immersive digital technologies is crucial. This article proposes a pedagogical shift where technologies such as virtual reality, augmented reality, and geographic information systems become conduits for grounding students in diverse geographical contexts. These tools immerse students in real-world scenarios, facilitating an experiential learning environment that is interactive and engaging. They enable exploration, analysis, and understanding of complex geographical processes from multiple perspectives, democratizing educational spaces by bringing marginalized places and communities into academic discussions. The adoption of new media in geography education must be navigated with a critical lens, however, to avoid reinforcing existing power structures. A critical pedagogical approach ensures that the integration of digital technologies promotes equity in both content representation and access. This article underscores the need for a balanced integration of immersive digital technologies in geography education—a fusion that enriches learning while promoting inclusivity and critical engagement with emerging media landscapes. Moreover, in settler-colonial states like Canada, the United States, and Australia, the inclusion of Indigenous voices, knowledges, and perspectives is vital for reconciliation and place-based geographies within the digital realm. The methodology involves a systematic exploratory review of academic publications on immersive digital technologies in geography education, followed by the development of a conceptual framework to advance the scholarly conversation and enable educators and students to address inclusivity and critical engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".