Envisioning the future of engineering education through Africanfuturism
Bibliographic record
Abstract
This paper explores how Africanfuturism, specifically Nnedi Okorafor’s Binti series, offers a transformative framework for reimagining engineering education to promote inclusivity, belonging, and diverse epistemologies. Grounded in Marcus Garvey’s call to honor cultural histories, we critique the colonial legacies of engineering, which favor Western knowledge systems while sidelining non-Western contributions. Drawing from bell hooks’ concept of transformative education, we argue that Africanfuturism challenges exclusionary practices by incorporating ancestral knowledge, cultural traditions, and liberatory visions into STEMM (science, technology, engineering, mathematics, and medicine) fields. Through thematic analyses of race and prejudice, innovation rooted in tradition, and cultural identity, Binti illustrates how Black epistemologies—grounded in community, spirituality, and historical consciousness—can reshape engineering education. We emphasize the potential of Africanfuturist narratives to decenter whiteness, confront systemic racism, and foster educational environments where marginalized students can thrive. By centering Black radical imagination, this paper advocates for an interdisciplinary, justice-oriented approach that integrates storytelling, cultural identity, and diverse ways of knowing to cultivate engineers committed to equity, empathy, and social transformation. Updated with minor typographical corrections: June 30, 2025.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".