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Paper ID:IJSREM62851

AdaptIQ : NCERT-Aligned AI Learning Platform for Indian Students

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62851

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62851

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Adithya M

Adithya M

Department of Computer Science and Engineering

Jain (Deemed-to-be) University

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MCA

Mithun Chavan A

Department of Computer Science and Engineering

Jain (Deemed-to-be) University

NTP

Naga Tejas P

Department of Computer Science and Engineering

Jain (Deemed-to-be) University

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Mentors

RLK

Ravi Lanke Kumar

Department of Computer Science and Engineering

Jain (Deemed-to-be) University

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Abstract

India has over 250 million school-going students assessed on the National Council of Educa-tional Research and Training (NCERT) curriculum, yet no mainstream EdTech platform personalises learning at the level of individual chapters, exercises, or page ref-erences within actual textbooks. AdaptIQ V2 addresses this gap by integrating three scientifically validated techniques — the 2-Parameter Logistic Item Response Theory (IRT 2PL) model for real-time ability tracking, the SM-2 Spaced Repetition algorithm for long-term retention scheduling, and Retrieval-Augmented Gen-eration (RAG) with pgvector for textbook-grounded AI tutoring — into a single NCERT-native platform for Classes 6–12. A five-step onboarding wizard captures twelve learner attributes; an adaptive quiz engine selects questions matched to each student’s current ability score (θ), targeting a 65–70% success probability; and a daily study plan generator creates personalised schedules based on weak topic history and available study time. Additional features include a PDF viewer with an inline AI tutor that cites specific NCERT pages, a bilingual English/Hindi interface using official NCERT mathematical terminology, and a GitHub-style streak calendar for gamified engagement. Built on a React 18 + TypeScript frontend, Node.js/Express backend, and Supabase PostgreSQL with pgvector, AdaptIQ V2 delivers the first platform to combine NCERT alignment, IRT-based adaptive difficulty, SM-2 spaced repetition, and a textbook-aware AI tutor in a single production-grade system.

KEYWORDS

Keywords: adaptive learning IRT 2PL NCERT curricu- lum spaced repetition retrieval-augmented generation EdTech personalised education.

Cite This Article

Adithya M, Mithun Chavan A, Naga Tejas P, Ravi Lanke Kumar, "AdaptIQ : NCERT-Aligned AI Learning Platform for Indian Students," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026. https://doi.org/10.55041/IJSREM62851

Full Article/Conclusion

AdaptIQ V2 demonstrates that a NCERT-native, AI-driven adaptive learning platform is technically feasible using modern open-source tooling. By anchoring every interaction — question selection, daily plan, AI explanation — to a specific chapter and page in the actual NCERT textbook, AdaptIQ V2 creates a study experience genuinely relevant to a student’s board examination requirements. The integration of IRT 2PL ability tracking, SM-2 spaced repetition, and RAG-powered AI tutoring into a single bilingual platform represents the first of its kind for the Indian school education segment. The system is deployed using Supabase Cloud, OpenRouter, and Vercel/Railway — a fully managed, horizontally scalable stack accessible to any student with a web browser. Future work includes expanding the curriculum to Classes 6–7 and Classes 11–12 (JEE/NEET founda-tion), enabling full pgvector semantic retrieval across all NCERT pages, building parent/teacher dashboards, and implementing a board exam simulation mode with CBSE marking scheme awareness.

References

[1]    F. Baker and S. Kim, Item Response Theory: Parameter Estimation Techniques, 2nd ed. Marcel Dekker, New York, 2004. [2]    P. Wozniak, “SuperMemo 2: Optimal Intervals for Spaced Repetition,” Acta Neurobiologiae Experimen-talis, 1994. [3]    P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” in Proc. NeurIPS 2020, arXiv:2005.11401. [4]    NCERT, Mathematics Textbooks Classes 6–10. National Council of Educational Research and Training, New Delhi, 2024. [5]    P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” in The Adaptive Web, ser. Lecture Notes in Computer Science, vol. 4321. Springer, 2007, pp. 3–53. [6]    G. Siemens and P. Long, “Penetrating the Fog: Analyt-ics in Learning and Education,” EDUCAUSE Review, vol. 46, no. 5, pp. 30–40, 2011. [7]    J. Hamari, J. Koivisto, and H. Sarsa, “Does Gamification Work? A Literature Review of Empirical Studies on Gamification,” in Proc. 47th Hawaii Int. Conf. System Sciences (HICSS), 2014. [8]    K. VanLehn, “The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutor-ing Systems,” Educational Psychologist, vol. 46, no. 4, pp. 197–221, 2011. [9]    React Developers, “React Documentation,” Meta Plat-forms Inc. [Online]. Available: https://react.dev. [Ac-cessed: 2025]. [10]   Supabase, “Supabase Documentation.” [Online]. Avail-able: https://supabase.com/docs. [Accessed: 2025].

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Publication Date

5/8/2026

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Review Rounds: 2

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