Empowering Saudi Children: A Proposed Culturally Sensitive Emotional Intelligence Model Integrating Cultural Resilience, Educational Innovation, and Community Engagement
Bibliographic record
Abstract
This paper presents the development and implementation of an Emotional Intelligence (EI) program designed to enhance young children's emotional skills while preserving Saudi cultural values. The program integrates three key components: Cultural Resilience, Educational Innovation, and Community Engagement, ensuring a holistic approach to emotional intelligence development. Through a series of thoughtfully designed activities, the program fosters self-awareness, empathy, emotional regulation, and social skills, helping children navigate their emotions in a culturally relevant context. To assess the program’s effectiveness, it was implemented in 10 private nurseries and kindergartens in Riyadh, where teachers actively engaged children in various emotionally enriching activities. Following the implementation, a survey was conducted with 85 kindergarten teachers who facilitated the program, gathering their insights on its structure, activities, and impact. The findings revealed overwhelmingly positive feedback, with teachers reporting significant improvements in children's emotional awareness, interpersonal relationships, and ability to express and manage emotions. Teachers also highlighted the effectiveness of the program's culturally embedded activities, emphasizing their engaging and impactful nature. The results underscore the program’s success in fostering emotional intelligence while reinforcing cultural identity, demonstrating the importance of integrating cultural and community elements into early childhood education. This paper advocates the broader adoption of such culturally sensitive EI programs across Saudi Arabia, recognizing their role in nurturing emotionally intelligent children who are prepared to thrive in both personal and social spheres.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".