Enhancing intercultural education: strategies for challenging Eurocentrism and centring Indigenous perspectives in teacher education
Bibliographic record
Abstract
Navigating Eurocentric teacher education settings presents significant challenges for both educators and learners, particularly when engaging with issues of race, privilege, and Indigenous perspectives. This paper draws on interviews with 16 Indigenous experts from Australia, Canada, the US, and New Zealand, woven together with reflective practice from compulsory Indigenous studies courses in Queensland, Australia. Our analysis reveals three key strategies: critical self-reflection, the use of metaphors, and intergenerational mentorship, which enable initial teacher education (ITE) programs to challenge deficit thinking and promote meaningful inclusion of Indigenous Knowledges. Our paper examines the affective and intellectual tensions experienced by preservice teachers when they are invited to confront their cultural positioning, whiteness, and colonial complicity. We contend that scaffolded engagement with Indigenous epistemologies provides a potent means for challenging deficit discourses and fostering more ethically responsive educational practices. Methodologically, our paper integrates dialogic reflection and Indigenous-led analysis to generate a praxis-oriented contribution for those engaged in curriculum and pedagogical reform. While grounded in the Australian context, comparative references to other settler-colonial nations enhance global debates on decolonising teacher education, offering applied strategies for critical reflexivity, relational accountability, and the ethical inclusion of Indigenous knowledges.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".