Trump-Era Visa Policies and the Transformation of US Education Diplomacy: Implications for Islamic Education and Global Geopolitics
Bibliographic record
Abstract
This study examines the impact of President Donald Trump’s visa policies on shifts in US education diplomacy within the context of global geopolitics, with particular attention to their implications for international students from Muslim-majority countries and Islamic education networks. Restrictive measures during his administration, including limitations on F-1 and J-1 visas and travel bans targeting several Muslim-majority states, led to a notable decline in international student mobility and weakened the United States’ position as a leading global education destination. Using a descriptive qualitative approach and desk study method, this research is supported by individual-level foreign policy theory and education diplomacy theory. The analysis explores how Trump’s perceptions and leadership style shaped policies affecting higher education, including Islamic education-oriented exchanges and collaborations. It also highlights responses from competing countries such as Canada, Australia, and China, which expanded inclusive education policies and strengthened engagement with Islamic and Global South students. The findings show that these visa policies reduced US soft power and created opportunities for other states to lead in global education diplomacy, underscoring the need for more inclusive policy reforms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".