Presents a 24,795-example multilingual instruction dataset for teaching LLMs to deliver educational content grounded in Indian Knowledge Systems. Spans seven languages and bridges a gap in non-Western pedagogical content for instruction tuning. Demonstrates that domain-specific educational datasets improve LLM performance on culturally grounded knowledge tasks.
Key Findings
IKS-Instruct contains 24,795 instruction-response pairs for teaching language models to deliver educational content grounded in Indian Knowledge Systems (IKS), spanning seven languages (English, Hindi, Sanskrit, Tamil, Telugu, Kannada, Malayalam).The dataset covers 41 pedagogical techniques from Vedic oral and mathematical traditions and is aligned with the Central Board of Secondary Education (CBSE) curriculum for classes 6 through 12.Pairs derive from six source types: classical text corpora (Bhagavad Gita, Thirukkural, Sangam literature, Vedic texts), curriculum-aligned pedagogical templates, Vedic mathematical sutra demonstrations, bilingual instruction pairs, technique-grounded multi-turn dialogues, and cross-tradition comparative analyses.Quality is assessed through a multi-judge evaluation framework in which independent language models score responses on 12 dimensions, including technique fidelity, pedagogical quality, factual accuracy, and IKS cultural depth.Under a uniform five-judge external panel (median aggregation over 1,201 stratified items), the strongest IKS-Instruct fine-tune of a compact 7B model reaches a median judge score of 6.39, within 0.15 of a strong general-purpose reference model (Nemotron-Nano at 6.54) at a fraction of its deployment cost.The base model without IKS fine-tuning scores near zero on IKS-specific dimensions, and model quality does not increase monotonically with data curation — a result the authors report alongside data-quality gains.Dataset Design & Method
IKS-Instruct targets a gap in instruction tuning: existing datasets such as Alpaca, FLAN, and Dolly are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains. The six source types are engineered so that each pair carries structured metadata — source provenance, technique classification, language, subject, class level, and quality scores — released in JSONL format. This design lets the dataset support both instruction-following and culturally grounded pedagogy rather than generic fact recall.
Implications for AI in Education
The evaluation shows that a compact, domain-tuned model can approach the IKS-specific quality of a far larger general-purpose model at a fraction of deployment cost, which matters for resource-constrained educational deployments and for Language Learning and Culturally Relevant Pedagogy in multilingual classrooms. The non-monotonic relationship between curation and quality also warns against assuming "more data is always better," reinforcing the value of technique-fidelity-aware evaluation when adapting LLMs to non-Western knowledge traditions.
Connected Concepts
Language LearningCulturally Relevant PedagogyAdaptive LearningKnowledge TracingPedagogical AgentOpen SourceAffective ComputingReinforcement LearningConnected Articles
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Shwetha Singaravelu, Gayathri Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani (2026). IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems. arXiv:2607.23322. cs.CL, cs.CY.