AI Literacy as a Core 21st Century Competency in Secondary Education: A Systematic Review and Conceptual Framework
Authors: Research Scholar Shefali Sharma, Assistant Professor Dr. Jyoti Kumari
Abstract: Adolescents have become routine users of artificial intelligence (AI) systems faster than school systems have become teachers of them. This paper asks whether AI literacy has the standing of a core twenty-first century competency for secondary education, and what a defensible framework for it would look like. Adopting a structured integrative review, we analysed a purposive corpus of 29 sources across four strands: nine influential competency frameworks, eight review syntheses, six instrument-validation studies and seven system-level survey datasets. Cross-framework mapping shows strong convergence on five constructs understanding AI, using AI, evaluating AI, creating with AI, and positioning oneself toward AI but persistent divergence on whether creation belongs in general education, whether the affective dimension is a competency or a mediator, and how progression should be indexed. Measurement lags conceptualisation: of sixteen validated scales identified in the field, thirteen are self-report and only two are purpose-built and large-sample validated for secondary students, so the higher-order strands are largely evidenced by learners' claims about themselves. System-level data reveal a widening gap: reported use by students rose from 13% to 54% in two survey years, while provision of student training, policy and explicit instruction all remain below 45%. We propose the AILSE framework, which organises the five convergent strands across three progression levels indexed to autonomy, abstraction and accountability, and specifies four enabling conditions teacher capacity, curricular host, assessment validity and equitable access without which the competencies are not attainable. The contribution is a framework that treats implementation not as an afterthought but as part of the construct.
Diet Effectiveness in Teacher Education: A Regional Assessment of the West Zone of Arunachal Pradesh
Authors: Mr Navajit Saikia, Dr Nirmala Singh Rathore, Ms Amisha Raj
Abstract: The emergence of Digital Humanities (DH) has fundamentally transformed the methods and objectives of literary studies. Integrating computational technologies with traditional humanistic inquiry, Digital Humanities enables scholars to analyse literary texts at unprecedented scales while preserving the interpretative richness of close reading. The rapid advancement of Artificial Intelligence (AI), particularly machine learning and Natural Language Processing (NLP), has further revolutionized textual analysis by facilitating automated interpretation, thematic mapping, sentiment analysis, authorship attribution, and stylistic evaluation. This paper examines the evolving relationship between Digital Humanities and literary studies, highlighting how AI-driven methodologies are reshaping literary criticism without replacing the critical insight of human scholars. It argues that AI should be viewed as an intellectual collaborator that enhances literary research by expanding analytical possibilities while preserving the centrality of human interpretation. The study also explores ethical concerns, methodological challenges, and future directions of AI-assisted literary scholarship.
DOI: http://doi.org/10.5281/zenodo.21900932
Unmaking the ‘Ideal Woman’: Gender, Power, and Resistance in Divakaruni’s Heroines
Authors: Research Scholar Poonam Pareek, Dr. Vineet Kumar Purohit
Abstract: This paper, titled Unmaking the ‘Ideal Woman’: Gender, Power, and Resistance in Chitra Banerjee Divakaruni's Heroines, explores how female agency and resistance is being depicted in selected novels of Chitra Banerjee Divakaruni. This study examines how Divakaruni problematizes the patriarchal representation of the “ideal woman” as being socially proper, self-sacrificing, submissive and obedient in The Palace of Illusions, The Forest of Enchantments and Sister of My Heart. The paper examines the social constraint to social self-awareness and increasing social autonomy of Draupadi, Sita, Anju and Sudha using a feminist literary approach with the help of Simone de Beauvoir, Judith Butler and postcolonial feminist perspectives. The analysis also uncovers how Divakaruni's characters are traditionally seen as passive subjects, and how they are empowered as actors who question patriarchy, regains their voice, negotiate cultural expectations, and assert their identity. The paper also underscores the importance of female solidarity, mythological reworking, marriage and cultural power in influencing women's resistance. It concludes that Divakaruni's fiction is not just opposition to tradition, but an interpretation of tradition from the women's point of view, leading to new models of femininity developed through agency, dignity, voice and self-definition. The protagonists of her stories then embody a process of transformation from silence to speech, from oppression to rebellion, from the predetermined woman to the woman who chooses herself.
Structural and Dynamic Analysis of a Lightweight Electric Vehicle Chassis Using Finite Element Analysis
Authors: Abhinav Singh Dangi, Dr. Arun Kumar Yadav
Abstract: The increasing demand for sustainable and energy-efficient transportation has accelerated the development of lightweight electric vehicles. Among the major structural components of an electric vehicle, the chassis plays a critical role in ensuring vehicle strength, stability, safety and dynamic performance. This study focuses on the structural and dynamic analysis of a lightweight electric vehicle chassis using Finite Element Analysis (FEA), investigating the effect of lightweight chassis design on stress distribution, deformation, vibration characteristics and overall vehicle performance. Lightweight materials and optimised structural configurations are considered to reduce vehicle mass while maintaining sufficient stiffness and crashworthiness, and static structural analysis and dynamic analysis are examined for evaluating chassis behaviour under different loading and operating conditions. Twelve studies published between 2013 and 2025 are reviewed and organised into three themes covering lightweight chassis design, lightweight materials with multi-material optimisation, and structural optimisation with crash performance. The reviewed evidence is consolidated into comparative tables that map each study to its vehicle class, method, principal finding and limitation, alongside a specification of the analysis types applied in lightweight chassis development and an assessment of vehicle class coverage across the corpus. Five research gaps are identified and mapped to corresponding research directions. The findings indicate that lightweight chassis optimisation improves energy efficiency, reduces structural stress concentration and enhances vehicle stability without compromising safety, and that Finite Element Analysis is an effective tool for designing lightweight and durable chassis systems for sustainable transportation.
Predictive Analytics and Customer Segmentation for Intelligent Customer Attrition Management
Authors: Research Scholar Akash Verma, Associate Professor Dr. Richa Pareek
Abstract: Customer attrition has become a major challenge for organisations operating in highly competitive and digitally driven markets. Retaining existing customers is increasingly recognised as more cost-effective than acquiring new ones, making intelligent attrition management a strategic priority. This study explores the role of predictive analytics and customer segmentation in developing intelligent customer attrition management systems, integrating machine learning, deep learning, explainable artificial intelligence and customer behavioural analytics to improve churn prediction accuracy and support strategic retention decision-making. Twelve studies published between 2024 and 2026 are reviewed and organised into three themes covering predictive analytics and machine learning, segmentation with explainable analytics, and intelligent decision support with risk analytics. The reviewed evidence is consolidated into comparative tables that map each study to its sector, method, principal finding and limitation, alongside a layer-by-layer specification of the proposed framework and an assessment of the data sources and monitoring modes the corpus relies upon. Six research gaps are identified and mapped to corresponding research directions. The framework emphasises real-time analytics, explainable models and personalised retention strategies to enhance customer engagement and long-term profitability, and the findings suggest that intelligent analytics frameworks can improve proactive retention, optimise managerial decision-making and strengthen competitive advantage in dynamic business environments.
A Multi-Factor Machine Learning Framework for Gold Price Forecasting and Investment Decision Support
Authors: Research Scholar Govind Kumar Verma, Associate Professor Dr. Richa Pareek
Abstract: Gold price forecasting plays a crucial role in financial investment planning, portfolio diversification and risk management because of the volatile nature of global financial markets. Accurate prediction is challenging because gold prices are influenced by multiple economic, financial, geopolitical and market-related factors. This study proposes a multi-factor machine learning framework for gold price forecasting and investment decision support, integrating machine learning, deep learning, financial analytics, sentiment analysis and optimisation techniques. The framework uses macroeconomic indicators, market volatility, inflation rates, exchange rates, commodity prices and financial news sentiment to improve forecasting accuracy and support strategic investment decisions. Fourteen studies published between 2020 and 2026 are reviewed and organised into three themes covering machine learning and deep learning techniques, multi-factor analytics and financial indicator integration, and intelligent investment decision support. The reviewed evidence is consolidated into comparative tables that map each study to its data context, method, principal finding and contribution to the proposed design, and seven research gaps are identified and mapped to corresponding research directions. The framework is specified through five factor blocks and six predictive components including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM, Support Vector Regression, Random Forest Regression and optimisation-based neural networks, together with a decision-support layer covering portfolio optimisation, risk assessment and adaptive forecasting. The findings indicate that multi-factor machine learning frameworks can enhance forecasting reliability, reduce prediction uncertainty and improve investment strategy optimisation in dynamic financial markets.
A Machine Learning Framework for Corporate Valuation and Dividend Policy Analysis in FMCG Companies
Authors: Research Scholar Jyoti Wadhwani, Associate Professor Dr. Uttam Kumar Jha
Abstract: Corporate valuation and dividend policy analysis are essential components of financial management and investment decision-making, particularly in the Fast-Moving Consumer Goods (FMCG) sector, where stable financial performance, investor confidence and market competitiveness play a critical role. The rapid advancement of machine learning and predictive financial analytics has transformed traditional valuation methods into intelligent data-driven decision-support systems. This study proposes a machine learning framework for corporate valuation and dividend policy analysis in FMCG companies that integrates financial indicators, market variables, corporate governance factors and dividend-related analytics into a unified predictive structure. The framework incorporates profitability, liquidity, solvency, ownership structure, dividend payout ratios, market volatility and macroeconomic indicators to improve firm valuation forecasting and investment analysis, and applies regression models, random forests, gradient boosting, neural networks and ensemble forecasting systems to analyse complex financial relationships and improve predictive accuracy. Twelve recent studies published between 2019 and 2026 are reviewed and organised into three themes covering market-facing dividend effects, governance and financial determinants, and artificial intelligence in financial prediction. The reviewed evidence is consolidated into comparative tables that map each study to its context, focus, principal finding and contribution to the proposed design, and eight research gaps are identified and mapped to corresponding research directions. The findings indicate that machine learning-based predictive financial analytics can enhance corporate valuation reliability, optimise dividend decision-making and support strategic investment planning in the FMCG sector.
Advanced Preprocessing and Hybrid Neural Architecture for High-Accuracy Image Deepfake Detection
Authors: Research Scholar Priya Mishra, Dr. Deepika Pathak
Abstract: The widespread adoption of generative artificial intelligence has accelerated the creation of highly realistic deepfake images, creating major concerns regarding digital security, misinformation, identity fraud and media authenticity. Traditional detection approaches often struggle to maintain robustness against evolving synthetic image generation techniques and real-world image transformations. This review paper presents a comprehensive analysis of advanced preprocessing strategies and hybrid neural architectures for high-accuracy image deepfake detection. The study critically examines state-of-the-art frameworks including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), attention-based models and hybrid architectures such as GenConViT, and investigates the role of preprocessing techniques including normalisation, facial alignment, edge enhancement, frequency-domain transformation and feature optimisation. Eleven studies are reviewed and organised into three themes covering deep learning architectures, advanced preprocessing and feature enhancement, and challenges with generalisation. The reviewed evidence is consolidated into comparative tables that map each study to its focus, approach, principal finding and limitation, alongside a comparison of architecture families and a summary of preprocessing techniques. Challenges such as adversarial attacks, cross-dataset generalisation, zero-shot detection limitations and computational complexity are discussed, and seven research gaps are identified and mapped to corresponding future research directions. Directions focusing on explainable AI, lightweight hybrid models, generalised feature learning and multimodal detection are presented to support the development of scalable, interpretable and computationally efficient deepfake detection systems.
Computational Investigation of Lightweight Composite Wind Turbine Blades for Enhanced Stability and Fatigue Life
Authors: Research Scholar Rishabh Dev Singh, Associate Professor Dr. Banarsi Pandey
Abstract: The rapid growth of renewable energy systems has increased the demand for efficient and reliable wind turbine technologies capable of operating under complex environmental and loading conditions. Wind turbine blades are continuously subjected to aerodynamic forces, cyclic stresses, vibration and environmental degradation, which significantly influence their structural stability and fatigue life. This study presents a computational investigation of lightweight composite wind turbine blades for enhanced stability and fatigue life using advanced simulation techniques. Lightweight composite materials and optimised blade configurations are considered to improve structural stiffness while reducing blade weight and operational stress concentration, and computational simulations evaluate aerodynamic loading, stress distribution, deformation behaviour, vibration response and fatigue performance under varying wind speed conditions. The performance of the optimised composite blade is compared with conventional blade structures in terms of structural stability, fatigue resistance and aerodynamic efficiency. Twelve studies published between 2000 and 2023 are reviewed and organised into three themes covering blade design with structural optimisation and aerodynamic performance, composite materials with morphing concepts and structural testing, and structural health monitoring with damage detection. The reviewed evidence is consolidated into comparative tables that map each study to its focus, method, principal finding and limitation, alongside an assessment of the damage mechanisms the corpus addresses and the evidence base on which its conclusions rest. Five research gaps are identified and mapped to corresponding research directions. The simulation results demonstrate that lightweight composite blade designs significantly improve fatigue life, reduce deformation and enhance operational reliability while maintaining aerodynamic performance.
Design and Comparative Analysis of Lightweight CFRP Blended Winglets for Drag Reduction in Airbus A380 Aircraft
Authors: Satyaprakash Tiwari, Dr. Alok Choudhary
Abstract: The aviation industry continuously seeks advanced aerodynamic technologies to improve fuel efficiency, reduce drag and enhance aircraft performance. Winglets are widely used in modern commercial aircraft to minimise induced drag and control wingtip vortices generated during flight. This study focuses on the design and comparative analysis of lightweight Carbon Fibre Reinforced Polymer (CFRP) blended winglets for drag reduction in Airbus A380 aircraft. A lightweight blended winglet model using CFRP composite materials is developed and compared with the conventional A380 wingtip configuration, and Computational Fluid Dynamics (CFD) analysis with ANSYS simulation evaluates airflow behaviour, pressure distribution, lift characteristics, drag reduction and wake turbulence formation around the winglet structure. Eleven sources published between 2004 and 2022 are reviewed and organised into three themes covering commercial aircraft design and performance characteristics, lightweight structures with optimisation and technology development, and aircraft reliability with operational and economic considerations. The reviewed evidence is consolidated into comparative tables that map each source to its domain, focus, principal contribution and limitation, alongside an assessment of which of the study's claim areas the corpus supports and which efficiency levers it addresses. Five research gaps are identified and mapped to corresponding research directions. The comparative results indicate that the CFRP blended winglet improves aerodynamic efficiency by reducing induced drag and wake vortex intensity while enhancing lift-to-drag ratio and flight stability, and that the lightweight composite structure contributes to reduced structural weight and improved fuel efficiency.