Authors: Mr. Santosh Kumar Choudhary

Abstract: Education is gradually moving from traditional teacher-centered methods toward student-centered practices that emphasize active learning, critical thinking, and learner autonomy. This paper explores modern techniques supporting this shift, focusing on adaptive learning platforms, virtual reality (VR), and project-based learning. Adaptive platforms personalize content based on individual needs, VR offers immersive experiences for deeper understanding, and project-based learning promotes collaboration and real-world problem solving. These methods encourage students to take an active role in their learning, boosting motivation and creativity. Despite their benefits, challenges such as insufficient teacher training, lack of digital infrastructure, and poor alignment between technology and curriculum limit their effectiveness. Many educators are not adequately prepared to apply new tools, while resource constraints, especially in underdeveloped areas, further hinder adoption. The study provides recommendations for effective implementation, including continuous professional development for teachers, investment in digital resources, and clear frameworks that link technology to learning objectives. By addressing these barriers, educational institutions can foster more engaging, interactive, and personalized learning environments, better preparing students for future challenges.

DOI: https://doi.org/10.5281/zenodo.17827279

Published by: vikaspatanker

Authors: Mr. Suresh Lohar

Abstract: This study looks at how home science education helps Indian families change their lives for the better. It focuses on how home science helps promote healthy living, eco-friendly habits, and women’s confidence. Home science is a mix of subjects like nutrition, clothes, home management, and family care. It has become very important in India. The government runs many programs to spread home science education, especially in villages, because it helps women and their families live better. To understand the effect, we used both numbers (quantitative data) and personal stories (qualitative data). We surveyed 100 Indian families and talked in detail to 20 women who studied home science. The results showed that home science education helped women improve their knowledge about healthy food and how to use it well, which led to better health for families. It also encouraged them to use fewer resources, save energy, and throw away less food. Women became more confident and empowered to manage their homes. The study shows that home science education can improve health, promote eco-friendly habits, and empower women in Indian families. The findings are useful for government officers, teachers, and others who work in home science and women’s welfare. By spreading home science education, especially in villages, India can help women feel more independent, improve family health, and support eco-friendly development.

DOI: https://doi.org/10.5281/zenodo.17826454

Published by: vikaspatanker

Authors: Naghma Firdous, Shireen, Darakhshan, Sabreen Khan

Abstract: Artificial intelligence (AI) is transforming educational landscapes by offering novel tools and methodologies that augment teaching effectiveness across diverse contexts. This paper explores the multifaceted applications of AI in supporting teachers’ pedagogical practices, administrative tasks, and professional development. The abstract summarizes the motivation, key themes, methodology, proposed contribution, and implications. Recent advances in machine learning, natural language processing, and adaptive learning technologies offer educators intelligent systems for student assessment, personalized instruction, classroom management, and lesson planning. However, adoption remains uneven, owing to technical, ethical, and practical constraints. The present study aims to synthesize current literature, propose an integrative AI-assisted framework tailored to teacher needs, and empirically investigate its impact on teaching efficacy. Using a mixed-methods research design, quantitative data will be collected through controlled classroom experiments measuring teaching outcomes, time allocation, and teacher satisfaction when employing AI tools. Qualitative data will be gathered via interviews and focus groups to explore teacher perceptions, challenges, and context-specific experiences. Analysis will utilize statistical evaluation of quantitative outcomes and thematic coding for qualitative responses. The proposed AI-assisted teaching framework integrates components for automated assessment feedback, adaptive lesson recommendation, predictive analytics for student learning challenges, and chatbot support for administrative queries. This system is designed to minimize teacher workload while enhancing decision-making and instructional quality. Expected outcomes include statistically significant reductions in administrative burden, increased personalization of instruction, improved student engagement, and higher perceived teacher efficacy. Ethical considerations—such as data privacy, algorithmic transparency, and equitable access—will be addressed through system design and policy guidelines. This research contributes to both educational technology scholarship and practical teaching advancement by presenting an end-to-end AI system co-designed with educators. Findings will inform policy, guide AI tool development, and support effective implementation in diverse educational settings.

DOI: https://doi.org/10.5281/zenodo.17826153

Published by: vikaspatanker

Authors: Mr. Rishav Kumar Singh

Abstract: The use of Artificial Intelligence (AI) technology in education is considered a major driver of educational innovation. While extensive literature exists on the integration of AI technologies in educational environments, the focus on teachers’ roles and their professional development needs remains limited. This study presents a systematic review of research conducted between 2015 and 2024, analyzing how teachers are using AI technology in their teaching and professional development. Specifically, the study focuses on the relationship between the supply of professional development opportunities and the demand for AI integration by teachers. Utilizing PRISMA principles and protocols, this review identified and compiled a total of 95 significant research articles. The findings indicate a disproportionate focus of research. About 65% of the studies centered on the application of AI in teaching, including conversational AI, AI-based learning and assessment systems, immersive technologies, visual and auditory computing, and teaching and learning analytics. In contrast, only 35% of the studies explored the role of AI in teacher professional development. This review highlights the lack of sufficient research on integrating AI technologies into teaching practices, keeping in mind the professional development needs of teachers. It emphasizes the need for future research specifically studying how AI can be used to empower teacher professional development. Moreover, technical and ethical challenges in AI-based professional development should be prioritized to ensure responsible and effective AI integration in education.

DOI: https://doi.org/10.5281/zenodo.17826006

Published by: vikaspatanker

Authors: Ritika Sharma

Abstract: The rapid evolution of technology in education has transformed the pedagogical landscape, with Artificial Intelligence (AI) emerging as a critical driver of innovation. In recent years, hybrid classrooms—an integration of AI-driven tools and human intelligence—have gained prominence as a sustainable teaching methodology for the 21st century. This paper explores the multifaceted future of hybrid teaching, emphasizing how AI and human educators can complement one another to create effective, inclusive, and future-ready learning environments. AI technologies such as adaptive learning systems, intelligent tutoring systems, predictive analytics, and automated grading have revolutionized instructional delivery by enabling personalization, efficiency, and data-informed decision-making. These tools ensure that students receive content tailored to their abilities, pace, and preferences, which enhances engagement and learning outcomes. At the same time, the role of human intelligence in education remains indispensable. Teachers bring empathy, creativity, ethical judgment, and emotional intelligence—qualities that machines cannot replicate—to foster holistic development among learners. The hybrid classroom model represents a shift from the conventional perception of teaching as knowledge transmission toward a more collaborative, learner-centered framework. In this model, AI acts as an assistant that supports both students and teachers by reducing repetitive tasks, identifying learning gaps, and enabling individualized instruction. Teachers, in turn, are empowered to focus on higher-order skills such as critical thinking, mentoring, and cultivating a supportive classroom culture. This paper synthesizes existing literature to highlight the benefits and challenges of AI-driven education. While AI offers efficiency and personalization, ethical concerns such as data privacy, algorithmic bias, and inequitable access to digital infrastructure present significant challenges. The research methodology section proposes a qualitative framework for testing a Hybrid Classroom Model (HCM), combining AI tools with human-centered pedagogy to maximize outcomes. The discussion emphasizes that the integration of AI and human intelligence should be viewed not as a replacement strategy but as a partnership that leverages the strengths of both. In conclusion, the future of teaching methodologies lies in hybrid classrooms where AI handles scalable, data-driven aspects of learning while teachers nurture social, emotional, and intellectual growth. By balancing technological efficiency with human values, hybrid teaching promises to redefine education in ways that are adaptive, inclusive, and responsive to the demands of a rapidly changing global society.

DOI: https://doi.org/10.5281/zenodo.17825755

Published by: vikaspatanker

Authors: Raju kumar

Abstract: In the digital age, learning is no longer confined to classrooms, formal education systems, or age-specific stages of life. The growing demand for continuous skill development, professional adaptability, and personal growth has positioned lifelong learning as a critical need of the 21st century. Artificial Intelligence (AI) has emerged as a transformative force in enabling and shaping this shift, extending the boundaries of education far beyond traditional walls. Through intelligent tutoring systems, adaptive learning platforms, and personalized content delivery, AI supports learners at every stage—be it students, professionals, or senior citizens—by providing resources that match individual learning styles, pace, and goals. AI-driven recommendation engines curate customized pathways, allowing learners to access relevant courses, multimedia resources, and real-time feedback that bridge skill gaps effectively. Intelligent chatbots and virtual assistants act as on-demand mentors, providing immediate support and guidance, while natural language processing tools enhance language learning and communication skills across global contexts. Additionally, AI facilitates microlearning and mobile-based education, empowering individuals to engage with content in flexible, bite-sized modules suitable for modern, fast-paced lifestyles. Beyond technical upskilling, AI also fosters critical soft skills through simulations, gamified environments, and scenario-based training, preparing individuals to thrive in dynamic professional landscapes. Furthermore, AI supports inclusivity in lifelong learning by offering tools for differently-abled learners, breaking linguistic and accessibility barriers. This democratization of knowledge has profound societal implications, enabling equitable participation in knowledge economies worldwide. However, the integration of AI into lifelong learning also raises challenges. Issues related to data privacy, digital dependency, algorithmic bias, and equitable access must be addressed to ensure responsible and ethical implementation. Balancing automation with human interaction is essential to preserve the social, emotional, and cultural aspects of learning. In conclusion, AI is not merely an educational technology but a catalyst for redefining lifelong learning in holistic, accessible, and personalized ways. By transcending classroom walls, AI empowers individuals to continuously adapt, innovate, and contribute meaningfully in an ever-changing world. This transformation positions AI as a pivotal partner in creating sustainable learning ecosystems that nurture both personal development and collective progress.

DOI: https://doi.org/10.5281/zenodo.17854676

Published by: vikaspatanker

Authors: Saniya Nasar, Zohra Taushif, Imran, Ayush

Abstract: Artificial Intelligence (AI) is rapidly transforming various sectors of society, and education stands at the forefront of this revolution. The integration of AI in teaching and learning practices has opened new pathways for personalized learning, inclusive education, and effective classroom management. This paper explores the potential of AI to transform education, with a focus on four key areas: Al tools for teachers, Al for inclusive education, case studies of Al applications, and Al-driven teaching methodologies. Equally important is the role of Al in inclusive education. Al-powered applications such as text-to-speech, speech recognition, and language translation tools have broken barriers for differently-abled and linguistically diverse learners. By personalizing content delivery, Al ensures that education is more accessible, equitable, and adaptable to varied learning needs. The paper also highlights case studies where Al has been successfully implemented in educational settings worldwide. Examples include adaptive learning platforms that tailor content to student performance, chatbots that provide academic support outside class hours, and predictive analytics that help educators identify at-risk learners. These case studies demonstrate how Al enhances both student learning outcomes and teacher effectiveness. Methodologically, the paper adopts a qualitative approach by reviewing existing literature and analyzing case studies of Al applications in education. The findings suggest that AI should be viewed as a supportive tool that complements, rather than replaces, teachers. For sustainable integration, there is a need to train educators in Al literacy, address ethical concerns such as data privacy, and develop inclusive frameworks that ensure technology benefits all learners. In conclusion, Al has immense potential to transform education and teacher practices by fostering innovation, inclusivity, and efficiency. However, its successful adoption depends on striking a balance between technological advancement and human values in education. This paper contributes to the ongoing discourse by providing insights into practical applications of Al in classrooms, advocating for inclusive practices, and proposing a responsible roadmap for the future of Al in education.

DOI: https://doi.org/10.5281/zenodo.17825380

Published by: vikaspatanker

Authors: Dr. Goldi Soni, Reddi Rishitha, Karri AmruthaVarshini

Abstract: The review highlights the significant impact of artificial intelligence (AI) on agriculture, showcasing its ability to enhance efficiency, productivity, and sustainability in response to global food scarcity and a rising population. Key applications of AI include advanced soil, crop, weed, and disease management, facilitated by technologies such as automated irrigation, drones, and data analytics. Despite these advancements, challenges such as uneven distribution of mechanization, the necessity for big data, complexities in soil treatment, pest control issues, and gaps in farmers' technological knowledge hinder widespread adoption. The integration of AI with vertical farming presents a promising solution for urban land and water scarcity by significantly improving crop monitoring and yield predictions through machine learning and IoT technologies. As the agricultural sector faces pressure to increase production by 70% by 2050 amid limited resources and climate change, AI emerges as a crucial element in developing expert systems for crop management and enhancing overall economic efficiency while supporting sustainable farming practices. Nevertheless, significant hurdles remain, including high implementation costs, privacy concerns, and the need for interdisciplinary collaboration to develop holistic AI applications that consider economic, social, and environmental impacts alongside ethical implications. Overall, AI is positioned as a transformative force that could revolutionize agriculture, addressing core challenges and paving the way for smarter, more sustainable farming methods.

Published by: vikaspatanker

Authors: Dr. C.K. Gomathy, VD Sasank, R Srishreya

Abstract: Big Data in healthcare leverages advanced analytics on massive, heterogeneous datasets (electronic health records, medical images, genomics, wearable sensor streams, etc.) to improve patient outcomes and operational efficiency. Traditional healthcare IT systems cannot cope with the volume, velocity, and variety of these data. Modern distributed platforms (Hadoop, Spark, cloud) and AI methods (machine learning, deep learning) are therefore crucial for enabling real-time predictive modeling and trend analysis in medicine. This paper reviews recent (2015–2025) developments in healthcare-focused Big Data analytics, including architectures, algorithms, and applications. A comprehensive end-to-end methodology is proposed, comprising data ingestion, preprocessing, distributed model training, and deployment via containerized services. We describe the implementation of a prototype healthcare analytics system and present experimental results demonstrating its scalability and accuracy for real-time patient risk prediction. The findings underscore that Big Data analytics has become a foundational tool in healthcare, enabling evidence-based clinical decision support, disease surveillance, and personalized medicine.

DOI: http://doi.org/10.5281/zenodo.17813444

Published by: vikaspatanker

Authors: Rachna Chandra, Manoj Mittal, Anmol Dobriyal, Suneet Bhalla, Utsav

Abstract: We analysed the peer-reviewed literature published between 2021 and 2025 focusing on smart agriculture techniques which integrates advanced technologies such as the Internet of Things (IoT), wireless sensor networks (WSN), machine learning, artificial intelligence (AI), unmanned aerial vehicles (UAVs), remote sensing, edge–fog–cloud computing, and renewable energy systems to enhance farming efficiency and sustainability. Recent research shows significant progress in precision irrigation, automated monitoring, and data driven decision support systems that optimize water usage and improve crop yield. This paper presents a comprehensive review of forty research papers focused on smart agriculture techniques, highlighting IoT-based soil moisture monitoring, LoRaWAN enabled long-range communication, machine learning-based disease detection, UAV assisted irrigation planning, and solar powered intelligent water systems. The findings show that integrating smart technologies enables resource optimization, climate resilience, and scalable automation across diverse agricultural scenarios. The study contributes a consolidated view of recent advancements to support the development of sustainable agriculture.

Published by: vikaspatanker