Integrating Mathematical Foundations with Artificial Intelligence and Data Analytics in B.Tech Programs Under the National Education Policy 2020
DOI:
https://doi.org/10.63561/jmns.v3i1.1159Keywords:
NEP 2020, Artificial Intelligence (AI), Data Analytics, B.Tech Student, Personalized LearningAbstract
The rapid advancement of Artificial Intelligence (AI) and Data Analytics has significantly influenced the landscape of higher technical students in India, particularly within the framework of the National Education Policy 2020. This study examines the role and impact of AI and Data Analytics in B. Tech education, focusing on student awareness, adoption of AI-driven tools, effectiveness in teaching–learning processes, and alignment with the objectives of NEP 2020. The research is based on a quantitative approach using a structured questionnaire administered to 100 B. Tech students. A five-point Likert scale was employed to measure awareness and perceived impact levels. Statistical tools such as mean score and percentage analysis were used for data interpretation. The findings reveal that 50% of students demonstrate high or very high awareness of AI-driven tools and data analytics applications, while the calculated mean score of 3.39 indicates above-average awareness. Furthermore, 55% of students perceive a strong positive impact of AI and Data Analytics on academic performance, skill enhancement, and outcome-based education, with a mean impact score of 3.47. The results suggest that AI integration supports personalized learning, improves analytical competencies, and enhances employability skills among B. Tech students. However, challenges such as limited infrastructure, uneven faculty training, and implementation gaps remain significant concerns. The study concludes that AI and Data Analytics play a transformative role in strengthening technical education and are aligned with the vision of NEP 2020, which emphasizes digital integration, multidisciplinary learning, and innovation-driven growth. Strategic curriculum reforms, institutional support, and capacity-building initiatives are essential to maximize the long-term benefits of technology-enabled engineering education in India.
References
Appana, S. M., Pasupuleti, R. S., Tulimelli, J., Dokku, Y., Chen, W. K., & Nalluri, V. (2025). Future agenda on higher education policy using artificial intelligence tools: A review and bibliometric analysis. Engineering Proceedings, 98(1), 12.
Daniel, B. (2017). Big data and learning analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 48(1), 1–9. https://doi.org/10.1111/bjet.12423
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Patel, S., & Mehta, R. (2021). Challenges in implementing artificial intelligence under NEP 2020 in engineering institutions. Journal of Engineering Education Transformations, 34(3), 45–52.
Rajesh, D. (2025). A Comprehensive Learning Analytics Framework for Data-Driven Student Success in Alignment with NEP 2020. Journal of Informatics Education and Research.
Rao, K., & Joshi, A. (2022). Artificial intelligence tools and student engagement in engineering education under NEP 2020. International Journal of Emerging Technologies in Learning, 17(12), 112–125.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
Singh, V., &Verma, N. (2023). AI and data analytics integration under NEP 2020: Bridging academia–industry gaps in technical education. Journal of Technical Education and Training, 15(2), 78–92.
Zawacki-Richter, O., Bai, J. Y., Lee, K., Slagter van Tryon, P. J., & Prinsloo, P. (2024). New advances in artificial intelligence applications in higher education?. International Journal of Educational Technology in Higher Education, 21(1), 32.
