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PustakAI: Curriculum-Aligned and Interactive Textbooks Using Large Language Models

arXiv 2511.10002 ↗First posted 13 Nov 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like content. This has revolutionized various sectors such as healthcare, software development, and education. In education, LLMs offer potential for personalized and interactive learning experiences, especially in regions with limited teaching resources. However, adapting these models effectively to curriculum-specific content, such as the National Council of Educational Research and Training (NCERT) syllabus in India, presents unique challenges in terms of accuracy, alignment, and pedagogical relevance. In this paper, we present the framework "PustakAI"footnotePustak means `book' in many Indian languages. for the design and evaluation of a novel question-answering dataset "NCERT-QA" aligned with the NCERT curriculum for English and Science subjects of grades 6 to 8. We classify the curated QA pairs as Factoid, Inferential, and Others (evaluative and reasoning). We evaluate the dataset with various prompting techniques, such as meta-prompt, few-shot, and CoT-style prompting, using diverse evaluation metrics to understand which approach aligns more efficiently with the structure and demands of the curriculum. Along with the usability of the dataset, we analyze the strengths and limitations of current open-source LLMs (Gemma3:1b, Llama3.2:3b, and Nemotron-mini:4b) and high-end LLMs (Llama-4-Scout-17B and Deepseek-r1-70B) as AI-based learning tools in formal education systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 13 Nov 2025.

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References · 14

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  7. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  8. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  9. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 79
  10. 2022How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Modelscited by 43
  11. 2025PeerQA: A Scientific Question Answering Dataset from Peer Reviewscited by 21
  12. 2024SyllabusQA: A Course Logistics Question Answering Datasetcited by 9
2 more
  1. 2025COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Contentcited by 2
  2. 2025Evaluating Multimodal Large Language Models on Educational Textbook Question Answeringcited by 1

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  2. 2024Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications
  3. 2026IndicEval: A Bilingual Indian Educational Evaluation Framework for Large Language Models
  4. 2025Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AI
  5. 2025Instructional Goal-Aligned Question Generation for Student Evaluation in Virtual Lab Settings: How Closely Do LLMs Actually Align?
  6. 2024Pedagogical Alignment of Large Language Models
  7. 2025Small Models, Big Support: A Local LLM Framework for Educator-Centric Content Creation and Assessment with RAG and CAG
  8. 2025SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala