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Empowering Indian Educators through AI: Transforming Faculty Development and Pedagogical Practices in Higher Education

The rapid integration of Artificial Intelligence (AI) into education systems is redefining teaching and learning paradigms across the globe. In India, with the rollout of the National Education Policy (NEP) 2020 and the push for digital transformation, AI holds the potential to completely change faculty development and pedagogical practices in higher education. This research investigates the impact of AI-based tools on Indian educators, focusing on personalized teaching, adaptive learning systems, and data-driven decision-making. The study examines how AI technologies empower faculty through continuous upskilling and reskilling, enabling them to adapt to evolving learning environments. Using AI-driven Learning Management Systems (LMS), teachers can now access real-time analytics, automate assessments, and personalize student feedback. However, the adoption of these technologies depends on institutional readiness, faculty digital literacy, and infrastructure availability. A structured questionnaire was distributed to 100 faculty members across Indian universities. The data was analyzed using SPSS tools, including percentage analysis, multiple regression, and chi-square analysis. The study identifies eight key factors influencing faculty empowerment: digital literacy, institutional support, training programs, AI integration in LMS, perceived usefulness, ease of use, policy awareness, and resistance to change. Results reveal a strong correlation between AI adoption and enhanced teaching effectiveness. Faculty members exposed to AI tools demonstrated increased engagement, better course customization, and improved student performance metrics. However, challenges such as lack of AI training, fear of redundancy, and inadequate infrastructure remain. The study emphasizes the need to implement structured AI competency programs and clear policy directives under NEP 2020. This research contributes to ongoing discourse by offering a faculty-centric perspective on AI adoption in Indian higher education. It offers concrete recommendations for policymakers, academic leaders, and EdTech developers to collaboratively design future-ready academic ecosystems.

Published by: Dr.V.Victor Solomon

Author: Dr.V.Victor Solomon

Paper ID: V11I4-1188

Paper Status: published

Published: July 30, 2025

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Research Paper

Gender, Violence, and Memory in Meena Alexander’S Nampally Road

Meena Alexander's Nampally Road (1991) is a powerful postcolonial feminist novel that weaves together themes of gender, violence, and memory within the socio-political landscape of contemporary India. Set in Hyderabad, the novel explores the psychological and physical trauma inflicted upon women by systemic patriarchal and political violence. This research paper analyzes Nampally Road as a literary site where female subjectivity, resistance, and memory converge to critique both colonial and postcolonial structures of oppression. Drawing on feminist and postcolonial theories, the study interrogates how Alexander constructs her female protagonist Mira's journey as emblematic of the broader struggles faced by Indian women. The novel ultimately becomes a space for reimagining justice, healing, and agency in the face of deeply rooted violence.

Published by: Dr. Ganesh Pundlikrao Khandare

Author: Dr. Ganesh Pundlikrao Khandare

Paper ID: V11I4-1186

Paper Status: published

Published: July 30, 2025

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Research Paper

Towards Inclusivity: An Analysis of the National Education Policy 2020’s Potential to Address the Educational Needs of Marginalised Groups

The National Education Policy (NEP) 2020 represents a paradigm shift in India’s educational landscape, promising equity, inclusivity, and quality for all. Falling into the paradigm of developmental economics, this paper critically examines the policy’s potential to address the needs of historically marginalized communities—including socio-economically disadvantaged groups (SEDGs), Scheduled Tribes, children with disabilities, and gender minorities—through a focused analysis of key provisions such as Early Childhood Care and Education (ECCE), Special Education Zones, the Gender-Inclusion Fund, and Open and Distance Learning (ODL). Drawing upon official policy documents, existing educational statistics, and the broader socio-political context, this study evaluates whether NEP 2020 offers not just symbolic inclusion but substantive structural change. The paper argues that while the NEP makes commendable strides in intent and policy design, its success depends critically on effective implementation, inter-sectoral coordination, and sustained financial commitment. By highlighting both the strengths and gaps within NEP 2020, this analysis aims to contribute to ongoing discourse on educational equity and inform future policy refinements to better serve India’s most vulnerable learners.

Published by: Suhanee Soni

Author: Suhanee Soni

Paper ID: V11I4-1183

Paper Status: published

Published: July 29, 2025

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Research Paper

Experimental Study on the Mechanical Properties of Concrete Using GGBS and Pond Ash with Glass Fiber Reinforcement

Concrete is the most widely used construction material globally due to its versatility, durability, and cost-effectiveness. However, the production of conventional concrete requires a substantial amount of cement and sand, which leads to environmental concerns and depletion of natural resources. In the present study, an attempt has been made to prepare the sustainable concrete using partial replacement of cement with GGBS and sand by pond ash with glass fiber. The cement and sand have been replaced by 20% and 40% by GGBS and pond ash, respectively. A constant proportion of fiber (0.1% of cement) is added to evaluate the combined effect on properties of concrete. The experimental tests on fresh concrete (i.e., workability) and hardened concrete (i.e., compressive strength test, flexural strength test, and ultrasonic pulse velocity test) were performed to study the effect of partial replacement of GGBS and pond ash in the concrete. Based on durability, replacement of 20% cement up to 40% sand is recommended by GGBS and pond ash, respectively, together with glass fiber.

Published by: Dr. Tarun Kumar Rajak, Sabir Khan, Dr. Alok Kumar Jain

Author: Dr. Tarun Kumar Rajak

Paper ID: V11I4-1189

Paper Status: published

Published: July 29, 2025

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Research Paper

Decentralized Document Verification by Smart Contracts: Lightweight Open-Source Solution Design and Implementation

With the current digital era, document authenticity and existence verification become increasingly valuable across industries, including legal services, intellectual property protection, and business regulation. Authenticity and existence verification via conventional methods is usually centralized and entity-dependent, which may introduce privacy concerns, points of failure, and extra expenses. This paper presents the design and construction of a decentralized document verification system as an open-source system using blockchain smart contracts to safely register cryptographic proofs of digital material. The solution emphasizes ease, privacy, and usability using just smart contracts without storing actual document information on-chain or off-chain. By allowing users to calculate cryptographic hashes locally and directly interact with the blockchain, the system preserves immutable, transparent, and verifiable records while not compromising on confidentiality.

Published by: Kyrylo Sotnykov

Author: Kyrylo Sotnykov

Paper ID: V11I4-1177

Paper Status: published

Published: July 25, 2025

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Research Paper

Time Hierarchy in Practice: Empirical Evidence of Computational Class Separations

The Time Hierarchy Theorem states, in theory, that by providing more time, there exist strictly more problems that can be solved. However, most of these separations are very abstract and cannot be seen in a real computing environment. This paper investigates how algorithmic classes of different time complexities act under real-world time constraints. It does this by empirically evaluating standard algorithms with time complexities from O(n log n) to O(2ⁿ) and measuring the input sizes at which effective divergence is observed. The paper finds effective and observable diversions in line with theoretical expectations, empirically supporting theoretical hierarchies.

Published by: Aarush Reddy

Author: Aarush Reddy

Paper ID: V11I4-1174

Paper Status: published

Published: July 23, 2025

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