Skip to content

Pemanfaatan AI dalam Menunjang cara mengajar Guru di MA Al-Munawaroh Kejajar

Sep 2026 · Jurnal Ilmu Pendidikan dan Sains Islam Interdisipliner · pp. 3225-3239

Abstract

Perkembangan teknologi Artificial Intelligence (AI) memberikan peluang baru dalam mendukung guru dalam mempersiapkan dan melaksanakan kegiatan pembelajaran. Penelitian ini bertujuan untuk mengetahui pemanfaatan AI dalam menunjang kegiatan mengajar guru di MA Al-Munawaroh Kejajar, khususnya dalam konteks madrasah berbasis pondok yang tidak memperkenankan peserta didik membawa telepon genggam. Penelitian ini menggunakan metode penelitian lapangan (field research) dengan pendekatan deskriptif kualitatif. Data diperoleh melalui observasi, wawancara, keterlibatan langsung dalam kegiatan pembelajaran, dan dokumentasi selama pelaksanaan Praktik Pengalaman Lapangan (PPL). Hasil penelitian menunjukkan bahwa AI dimanfaatkan oleh guru terutama pada tahap persiapan pembelajaran, seperti mencari referensi dan alternatif penjelasan materi, menyusun bahan ajar, membuat soal dan LKPD, merancang pertanyaan pemantik, serta mengembangkan ide kegiatan dan media pembelajaran. Hasil yang diperoleh dari AI tidak digunakan secara langsung, tetapi terlebih dahulu diperiksa, dikoreksi, dan disesuaikan dengan kebutuhan peserta didik, kurikulum, serta nilai-nilai keislaman yang diterapkan di lingkungan madrasah dan pondok. Pemanfaatan AI juga menunjukkan adanya penguatan peran guru sebagai perancang pembelajaran, pengguna teknologi, validator informasi, penyesuai hasil AI, fasilitator pembelajaran, sekaligus pendidik yang tetap menjalankan fungsi humanis. Meskipun demikian, pemanfaatan AI masih menghadapi beberapa tantangan, antara lain literasi digital guru yang belum merata, keterbatasan waktu dalam memvalidasi hasil AI, serta keterbatasan sarana dan akses internet. Dengan demikian, AI dapat berfungsi sebagai alat bantu yang mendukung profesionalisme guru, tetapi tidak menggantikan peran pedagogis dan humanis guru dalam proses pendidikan

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.

Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al. · 109 citations · ⚡19

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.

Jiaqi Xue, Meng Zheng, Yebowen Hu et al. · 109 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4

Related blog posts

MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.