Mangalayatan University

India's National Education Policy 2020 integrates Artificial Intelligence to address educational disparities across heterogeneous contexts—rural-urban divides, resource-constrained government schools, and technologically advanced academies. The chapter employs qualitative policy examination, institutional readiness assessments, and critical synthesis of empirical research on intelligent tutoring systems and learning analytics within Indian educational ecosystems. Analysis reveals AI's multifaceted potential: personalised learning pathways, augmented teacher capacity in under-resourced contexts, and equitable access in geographically dispersed communities. The 2026-27 mandatory AI curriculum presents developmental opportunities; success requires substantial investment in teacher preparation and critical AI literacy. Realising transformative potential demands balanced integration of technological innovation with institutional capacity building, teacher empowerment, and transparent governance, prioritising educational equity and resource allocation.

Artificial Intelligence (AI) is transforming societies by reshaping economies, democracies, and public services. This paper explores AI's dual role as both an enabler of progress and a source of new challenges. It examines its impact on labor markets, governance, healthcare, education, and urban development, highlighting both opportunities and risks such as bias, inequality, and misinformation. Through international cooperation, ethical frameworks, and inclusive policies, AI can be guided toward enhancing human well-being. Real-world case studies from Estonia and India demonstrate how AI can strengthen democracy and public services when developed responsibly.

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Among the various styles of indian miniature painting, the Kangra style holds a significant place. The Kangra style is considered to be the most sensitive and aesthectic style in indian miniature painting. This style exhibits a synthesis of nature, human emotion, and devotion that makes it distinct from other styles. Especially in depicting moonlit night, Kangra artists have demonstrated their imagination and emotional depth to the extreme. The artists have not merely depicted the moonlit night as a natural background element, but have depicted it as a key component influencing the emotional atmosphere of the paintings.The Kangra School artists have imbued Radha Krishna’s love life, flute playing, and other romantic scenes against the backdrop of a moonlit night, giving them a transcendental beauty. The combination of white and blue colors in these paintings, along with the shadows of trees, the waves of the Yamuna River, and the moonlight spread across the sky, creates an atmosphere in which various emotions such as love, separation, and devotion emerge effortlessly, and the mere sight or experience of them evokes a sense of supreme beauty .The purpose of this research is to analyse the artistic, emotional, and symbolic meanings of the Kangra style depicton of moonlit nights. The primary objective of this research is to explain how Kangra artists have transformed the moonlit night from a mere symbolic of natural beauty to a vehicle of emotions and spiritual experience. This research will thus seek to deeply understand the emotional sensitivity and spiritual aesthectic perspective of indian painting

Folk music is a cultural reminiscence, social ideals, and historical records of people, but numerous traditions are struggling with the issue of continuity in the environment of globalization and shifting musical realities. This article discusses how modern jazz improvisation has been used as a creative and culturally sensitive tool to maintain and recontextualize folk music traditions. Instead of seeing preservation as a conservation that is unchanging, the study conceptualizes heritage as a living process that continues to survive by changing. Using ethnomusicological viewpoints, jazz improvisation theory, and intercultural theory the study examines how rhythmic patterns, modal systems and performance practices of folk music can be incorporated in the modern jazz environment without the erosion of cultural identity. The paper shows that improvisation makes it possible to have a dynamic dialogue between tradition and modernity and the folk elements can keep the symbolic and emotional meaning they have and broaden their expressive opportunities that are achieved through harmonic reinterpretation, rhythmic adaptation, and group performance. The problematization of jazz improvisation as a cultural negotiation and sustainability points to the research as a means of heritage preservation through a living archive and pedagogical tool, as well as an ethical platform. The results provide a wider-ranging discourse on the field of musicology and performance studies by establishing preservation through transformation as a possible paradigm of maintaining traditional music in the context of the contemporary artistic practice.

Student drop out of the art education programs in the academic institutions is a major problem because the students tend to drop out of the programs due to a complex interplay of creative, behavioral, psychological, and the socio-economic factors and not as a result of their work performance. This research paper introduces a predictive dropout analysis model that suits the field of art education management, and which can be used to predict potential at-risk students at an early stage and effectively implement data-driven, time-based response. The framework combines institutional data that is heterogeneous, such as attendance data, studio and portfolio submissions, assessment data, and traces of use of digital tools with psycho-creative data, such as creativity indices, portfolio development rates, and qualitative feedback on critique. The advanced feature engineering methods are used to extract the measures of engagement-trajectories, skill-growth-slopes, and creative-consistency indicators of the longitudinal dynamics of learning specific to art-based programs. Several machine learning models, which include logistic regression, random forest, support vectors machines, artificial neural networks, and gradient boosting are trained and tested through a structured training-validation pipeline through hyperparameter optimization. The accuracy, area under the ROC curve, F1-score, and the precision-recall are the measures of model performance that are evaluated to guarantee that the model can be robust under class imbalance conditions. The experimental findings show that ensemble and non-linear models have a higher performance compared to the traditional baselines and show the predictive power of creative interaction and behavioral characteristics as well as academic indicators.

Microfinance in these seasonal economies is a significant challenge for MFI to correspond loan cycles with smallholder farmers’ short-lived cash flow while balancing between client demand and institutional sustainability. It is a concept paper and aims to map theoretical routes for adaptive loan design that draw from financial services provision models, enterprise risk management theories, and literature on income seasonality and stochasticity. Building on a representational foundation, the article sets out a comprehensive industry architecture depicting the interplay of flexibility in the loan product, risk reduction in the portfolio, and administrative feasibility. A typology of flexible repayment schedules, principles for modular loan products, and examples of governance structures that reduce moral hazard and delinquency will be delivered. The model's emphasis is on its lending disbursal of loans and repayment amounts aligning with crop cycles and the earning calendar for micro-enterprisers. The results show that they are conceptually feasible, have the potential to make the model more comprehensive, have the potential to decrease the risk of the portfolio, and have the potential of still being an efficient functioning. The implications are used to create lending practices and policies leading to increased inclusion and resilience for clients in a seasonally variable income situation.

Crop microinsurance penetration rate is low among the smallholder farmers mainly because of the traditional lack of trust in the financial institutions and the lack of knowledge about procedures to claim. To address that challenge, this study utilizes existing trust-building theory, financial capability frameworks, and community-based outreach models to generate an integrated model applicable to product cooperatives. Using principles of systematic review and synthesis, this paper charts a course through the literature base to illustrate the ways programming implementation, communication transparency and peer-led intervention are already understood to successfully counter skepticism and misunderstanding. On such barriers such as information asymmetry, culture of negativity and historical final non-payment is been identified and thereby the mappings so derived lead the design of taxonomies in intervention trigger points in cooperative societies. The examination provides a matrix of outreach and literacy to assist in the development of context-specific, optimised interventions, which has practical implications for both practice and policy. It also identifies specific measures (e.g., trust perceptions) to assess intervention effectiveness, application of academic measures (i.e., financial literacy, intention to enrol), intermediate outcomes (i.e., reduction of misinformation among hard-to-reach populations), and participation in outreach. Our systematic approach also offers a practical foundation for the development and testing of microinsurance uptake-promoting interventions to facilitate inclusive and sustainable development of local financial systems in rural settings.

Artificial intelligence (AI) is important in minimizing costs, pressure drop, and the heat transfer area. This chapter uses AI techniques to forecast the application and performance of heat transfer coefficient, material thermal properties, and efficiencies with high accuracy according to the given parameters like geometry, properties, and operating conditions. This chapter explores how AI techniques contribute to computational fluid dynamics simulations by enhancing design accuracy, analyzing experimental data, and improving efficiency. This chapter contains machine learning (ML) methodologies and optimization algorithms that show the real world. In this chapter, we use AI techniques like ML, ANNs, and deep learning models that can be used to model complex, nonlinear relationships between design parameters and performance metrics. In this chapter, AI provides powerful tools to enhance the design and optimization of heat exchangers, offering greater efficiency and precision in the process. This chapter contains the working method and procedure of ML, optimization algorithms, and hybrid techniques while showcasing real-world applications and future trends in the field.

This study presents a deep learning-based technique for attack recognition in IoT networks, specifically using a hybrid model, named multi-kernel support vector attention autoencoder (MKSV-Attention Autoencoder), to detect botnet attacks. At first, traffic data is collected and then preprocessed by employing a number of steps to improve data balance and quality. Here, the missing data imputation, min–max normalization, and synthetic minority oversampling technique are exploited for preprocessing. Then, feature selection is done by using the LASSO model. Following that, attack detection in IoT is performed by utilizing the proposed MKSV-Attention Autoencoder. Finally, the experimentation evaluation is performed by employing metrics, such as accuracy, recall, precision, F1 score, TNR, and FAR. The assessment indicates that the proposed model attained a maximal accuracy of 0.9651, a precision of 0.9649, a recall of 0.9653, an F-measure of 0.9651, a TNR of 0.9649, a FAR of 0.0351, and a balanced accuracy of 0.9651.