Teacher Readiness for Deep Learning in Islamic Education: A Rasch Model Analysis of Challenges and Opportunities
Bibliographic record
Abstract
The integration of deep learning in education has the potential to enhance pedagogical practices, personalized learning, and adaptive instruction. However, Islamic schools face unique challenges in adopting AI-driven educational models due to technological limitations, digital literacy disparities, and regulatory constraints. This study assesses the readiness of Islamic school teachers in Indonesia to implement deep learning-based curricula, analyzing knowledge, attitudes, barriers, and demographic influences on AI adoption. A structured questionnaire was administered to 1,120 teachers across madrasahs, pesantrens, and Islamic private schools, with data analyzed using the Rasch measurement model to ensure psychometric validity. Differential Item Functioning (DIF) analysis was conducted to examine variations in readiness across gender, age, education level, teaching experience, and ICT knowledge. The results reveal moderate teacher readiness, with significant gaps in deep learning comprehension and practical implementation. Female teachers, mid-career educators (36–45 years), and secondary school teachers exhibit higher AI readiness, while novice and older teachers face greater barriers. ICT literacy emerges as the strongest predictor of readiness, underscoring the need for targeted digital training programs. Findings highlight infrastructure deficits, professional development gaps, and policy misalignment as primary obstacles to deep learning adoption. While urban teachers demonstrate higher AI engagement, rural educators require greater institutional support. The study emphasizes the necessity of differentiated professional development programs that cater to teachers at different career stages and digital literacy levels. These insights provide critical implications for policymakers, educational leaders, and curriculum developers in designing AI-driven pedagogical strategies for Islamic schools. Future research should explore mentorship initiatives and hybrid training models to foster sustainable AI adoption in religious education settings.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".