Pengembangan Sistem Pembelajaran Berbasis Kecerdasan Buatan untuk Pendidikan Jarak Jauh
Bibliographic record
Abstract
Perkembangan pendidikan jarak jauh menuntut adanya inovasi teknologi yang mampu meningkatkan kualitas dan efektivitas pembelajaran. Kecerdasan buatan (Artificial Intelligence/AI) menjadi salah satu solusi potensial dalam menjawab tantangan tersebut melalui pembelajaran yang adaptif, personal, dan berbasis data. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem pembelajaran berbasis AI yang dirancang untuk mendukung pendidikan jarak jauh secara efektif dan beretika. Metode penelitian yang digunakan adalah penelitian dan pengembangan (Research and Development) dengan pendekatan mixed methods, yang meliputi analisis kebutuhan, perancangan sistem, pengembangan prototipe, implementasi terbatas, serta evaluasi sistem. Hasil penelitian menunjukkan bahwa sistem pembelajaran berbasis AI mampu meningkatkan personalisasi pembelajaran, keterlibatan peserta didik, serta kualitas umpan balik pembelajaran. Selain itu, penelitian ini mengidentifikasi pentingnya penerapan prinsip etika, transparansi, dan perlindungan data dalam penggunaan AI di bidang pendidikan. Dengan demikian, sistem pembelajaran berbasis AI berpotensi menjadi solusi strategis dalam meningkatkan kualitas pendidikan jarak jauh apabila diterapkan secara bertanggung jawab dan terintegrasi dengan kebijakan institusional yang tepat
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".