arXiv:2607. 23322v1 Announce Type: cross Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains.
arXiv:2606. 26112v1 Announce Type: cross Abstract: Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora.
arXiv:2608. 15535v1 Announce Type: cross Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs).
arXiv:2507. 03162v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has transformed various domains, particularly computer science (CS) education.
M‑SQE is a post‑retrieval framework that estimates the quality of multilingual agent skills by combining a Theory view (intrinsic quality) and an Action view (task‑grounded utility) into a domain‑conditioned score. It was evaluated on general, tool‑use, and cultural skill‑use domains, showing a task‑success improvement of at least +3.5 points over baselines across three retrievers. The method notably boosts performance for low‑resource languages, raising Hindi by +12.9 pp and Swahili by +5.6 pp, and achieves strong results across six cultural regions, advancing linguistic and cultural equality in agentic skill use.
arXiv:2609. 29672v1 Announce Type: new Abstract: Artificial intelligence helps education most where an essential provision has been rationed by cost.
EduDial is a large-scale multi-turn teacher‑student dialogue corpus covering 345 core knowledge points and 34,250 dialogue sessions, designed around Bloom’s taxonomy and ten questioning strategies such as situational, ZPD, and metacognitive questioning. The dataset includes differentiated teaching strategies for students at varying cognitive levels to provide targeted guidance. Using EduDial, the authors trained EduDial‑LLM 32B and introduced an 11‑dimensional evaluation framework that measures teaching quality and content quality, showing that most mainstream LLMs struggle with student‑centered teaching while EduDial‑LLM outperforms all baselines across all metrics.
The paper introduces MentorQA, a multilingual dataset and evaluation framework for mentorship-oriented question answering derived from long‑form videos. It contains nearly 9,000 QA pairs across four languages and defines evaluation dimensions such as clarity, alignment, and learning value that extend beyond factual accuracy. Experiments show that Multi‑Agent QA pipelines outperform other architectures, especially on complex topics and low‑resource languages, while automated LLM‑based evaluation shows variable alignment with human judgments.
arXiv:2509. 16780v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials.
The evaluation of mathematical reasoning in large language models (LLMs) has predominantly focused on high-resource languages like English. This has created a significant barrier to the equitable development and deployment of AI in linguistically diverse regions such as Bangladesh, where over 230 million people speak Bengali.
OpenAI introduces IndQA, a new benchmark for evaluating AI systems in Indian languages. Built with domain experts, IndQA tests cultural understanding and reasoning across 12 languages and 10 knowledge areas.
DeepEdu‑v1 is an AI‑tutoring system tailored for Vietnamese education that addresses data‑sovereignty and local curriculum alignment issues. It uses a long‑context inference engine to reduce retrieval calls and prefill latency by about 35%, and a self‑improving agentic layer that curates verified local knowledge without fine‑tuning. In deployment, DeepEdu achieves nearly twice the speed of standard vLLM serving and raises agentic accuracy from 70.0% to 79.5% on complex tasks, especially in financial reasoning and interactive‑agent benchmarks.