Grounding AI-in-Education Development in Teachers' Voices: Findings from a National Survey in Indonesia
arXiv:2604.01630v1 Announce Type: new Abstract: Despite emerging use in Indonesian classrooms, there is limited large-scale, teacher-centred evidence on how AI is used in practice and what support teachers need, hindering the development of context-appropriate AI systems and policies. To address this gap, we conduct a nationwide survey of 349 K-12 teachers across elementary, junior high, and senior high schools. We find increasing use of AI for pedagogy, content development, and teaching media, although adoption remains uneven. Elementary teachers report more consistent use, while senior high teachers engage less; mid-career teachers assign higher importance to AI, and teachers in Eastern Indonesia perceive greater value. Across levels, teachers primarily use AI to reduce instructional pre
View PDF HTML (experimental)
Abstract:Despite emerging use in Indonesian classrooms, there is limited large-scale, teacher-centred evidence on how AI is used in practice and what support teachers need, hindering the development of context-appropriate AI systems and policies. To address this gap, we conduct a nationwide survey of 349 K-12 teachers across elementary, junior high, and senior high schools. We find increasing use of AI for pedagogy, content development, and teaching media, although adoption remains uneven. Elementary teachers report more consistent use, while senior high teachers engage less; mid-career teachers assign higher importance to AI, and teachers in Eastern Indonesia perceive greater value. Across levels, teachers primarily use AI to reduce instructional preparation workload (e.g., assessment, lesson planning, and material development). However, generic outputs, infrastructure constraints, and limited contextual alignment continue to hinder effective classroom integration.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2604.01630 [cs.CL]
(or arXiv:2604.01630v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.01630
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Fajri Koto [view email] [v1] Thu, 2 Apr 2026 05:17:00 UTC (5,465 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
announceintegrationreport
Detecting Toxic Language: Ontology and BERT-based Approaches for Bulgarian Text
arXiv:2604.01745v1 Announce Type: new Abstract: Toxic content detection in online communication remains a significant challenge, with current solutions often inadvertently blocking valuable information, including medical terms and text related to minority groups. This paper presents a more nu-anced approach to identifying toxicity in Bulgarian text while preserving access to essential information. The research explores two distinct methodologies for detecting toxic content. The developed methodologies have po-tential applications across diverse online platforms and content moderation systems. First, we propose an ontology that models the potentially toxic words in Bulgarian language. Then, we compose a dataset that comprises 4,384 manually anno-tated sentences from Bulgarian online forums
Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition
arXiv:2604.01711v1 Announce Type: new Abstract: Vietnamese Speech Emotion Recognition (SER) remains challenging due to ambiguous acoustic patterns and the lack of reliable annotated data, especially in real-world conditions where emotional boundaries are not clearly separable. To address this problem, this paper proposes a human-machine collaborative framework that integrates human knowledge into the learning process rather than relying solely on data-driven models. The proposed framework is centered around LLM-based reasoning, where acoustic feature-based models are used to provide auxiliary signals such as confidence and feature-level evidence. A confidence-based routing mechanism is introduced to distinguish between easy and ambiguous samples, allowing uncertain cases to be delegated to
Taming CATS: Controllable Automatic Text Simplification through Instruction Fine-Tuning with Control Tokens
arXiv:2604.01779v1 Announce Type: new Abstract: Controllable Automatic Text Simplification (CATS) produces user-tailored outputs, yet controllability is often treated as a decoding problem and evaluated with metrics that are not reflective to the measure of control. We observe that controllability in ATS is significantly constrained by data and evaluation. To this end, we introduce a domain-agnostic CATS framework based on instruction fine-tuning with discrete control tokens, steering open-source models to target readability levels and compression rates. Across three model families with different model sizes (Llama, Mistral, Qwen; 1-14B) and four domains (medicine, public administration, news, encyclopedic text), we find that smaller models (1-3B) can be competitive, but reliable controlla
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
![[D] Those of you with 10+ years in ML — what is the public completely wrong about?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-robot-hand-JvPW6jsLFTCtkgtb97Kys5.webp)
[D] Those of you with 10+ years in ML — what is the public completely wrong about?
For those of you who've been in ML/AI research or applied ML for 10+ years — what's the gap between what the public thinks AI is doing vs. what's actually happening at the frontier? What are we collectively underestimating or overestimating? submitted by /u/PhattRatt [link] [comments]
What Is Base64 Encoding and Why Do Developers Use It Everywhere
You have probably seen strings like this in code, APIs, or data URLs: SGVsbG8gV29ybGQ= That is "Hello World" encoded in Base64. If you have worked with images in CSS, email attachments, API tokens, or JSON payloads, you have already used Base64 — maybe without realising it. Here is what Base64 actually is, why it exists, and when you should (and should not) use it. What Is Base64? Base64 is a way to represent binary data using only text characters. It converts any data — text, images, PDFs, anything — into a string made up of 64 characters: A-Z, a-z, 0-9, +, and /. The = sign is used for padding at the end. Every 3 bytes of input become 4 characters of output. That is why Base64 encoded data is always about 33% larger than the original. Why Does Base64 Exist? Many systems were designed to
Anthropic Just Paid $400M for a Team of 10. Here's Why That Makes Sense.
Eight months. That's how long Coefficient Bio existed before Anthropic bought it for $400 million in stock. No public product. No disclosed revenue. No conventional traction metrics. Just a small team of fewer than 10 people, most of them former Genentech computational biology researchers, and one very large claim: they were building artificial superintelligence for science. Anthropic paid up anyway. And if you look at what they've been building in healthcare and life sciences over the past year, this acquisition is less of a surprise and more of a logical endpoint. Who Is Coefficient Bio? Coefficient Bio was founded roughly eight months ago by Samuel Stanton and Nathan C. Frey. Both came from Prescient Design, Genentech's computational drug discovery unit. Frey led a group there working o
What is a Function? Simple Explanation with Examples
what is function ? 1.function is a block of code that perform specific task. *block of code is a group of instruction to perform specific task. 2.Instead of writing the same code again and again, you can write it once inside a function and reuse it whenever needed. EXAMPLES; function square(number) { return number * number; } *In this case function is keyword, square is the function name what we named, inside the parentheses have parameters if we want to use so many parameters inside the parentheses then we must separate each of them with commas. *if we want to execute this function, then we should call this name of function, this function name is square so we call this like square() or if we want to put arguments then we should call this like square(23) in this case what was happened is y
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!