Integration of Indian Knowledge System in Higher Education Curricula under NEP 2020: Opportunities, Challenges, and Global Relevance
Bibliographic record
Abstract
The National Education Policy (NEP) 2020 represents a landmark in India’s educational transformation, offering a comprehensive vision that seeks to align higher education with the principles of multidisciplinarity, value-based learning, and rootedness in indigenous traditions. Among its most innovative dimensions is the formal recognition and curricular integration of the Indian Knowledge System (IKS), encompassing disciplines such as Ayurveda, Yoga, Vedic mathematics, classical philosophy, literature, and traditional sciences. This initiative underscores the importance of re-establishing India’s civilizational heritage as a meaningful contributor to global intellectual discourse while simultaneously addressing national educational aspirations. This paper critically examines the opportunities, challenges, and global relevance of embedding IKS into higher education curricula under the NEP 2020 framework. It highlights the multiple opportunities created by IKS integration, such as preserving cultural identity, promoting holistic learning, fostering sustainability, encouraging interdisciplinary research, and strengthening India’s role as an international education hub. At the same time, it acknowledges the institutional and practical hurdles, including the lack of standardized curricula, limited faculty expertise, insufficient empirical validation of traditional practices, funding constraints, and questions of global recognition. The analysis also situates India’s efforts within a comparative international context, drawing parallels with the integration of indigenous knowledge systems in countries like New Zealand, Canada, and Australia. These comparative insights reveal both the potential and complexities of embedding traditional knowledge in modern education systems. Ultimately, the study emphasizes that the success of NEP 2020’s vision depends on the creation of robust frameworks that combine policy support, academic innovation, and global collaboration. By doing so, India can not only preserve its intellectual traditions but also project them as globally relevant resources for sustainable development, ethical governance, and intercultural academic exchange
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".