- Current Issue
- Past Issues
- Conference Proceedings
- Submit Manuscript
- Join Our Editorial Team
- Join as a Member
| S.No | Particular | Page No. | |
|---|---|---|---|
| 1 |
Anita Dr. Veerpal KaurAbstract: Government nutrition programs are very important for the health, growth, and overall development of children in low-income areas where hunger and food insecurity are still big problems. This study investigates the influence of significant government nutrition programs—specifically the Integrated Child Development Services (ICDS), Mid-Day Meal Scheme, and POSHAN Abhiyaan—on the physical, cognitive, and immunological development of children. The study concentrates on evaluating enhancements in essential health metrics, such as height, weight, body mass index (BMI), school attendance, and overall well-being among children from economically disadvantaged households.A descriptive research design was employed to gather primary and secondary data from chosen low-income households, educational institutions, and healthcare facilities. The results suggest that kids who consistently use government nutrition programs grow faster, have more energy, and learn better than kids who don't have access to these programs. The study also finds problems that make these programs less effective, such as an inconsistent food supply, parents not knowing enough about them, and gaps in how they are carried out.The study finds that government nutrition programs do help kids grow and develop in a good way, but to get the most long-term benefits and make sure kids in low-income communities have healthier futures, monitoring systems need to be better, service delivery needs to be better, and community participation needs to be higher. |
|
1-12 |
| 2 |
Dr. Sajitha RajanAbstract: Artificial Intelligence (AI) is increasingly being used in online higher education to support personalized learning, where instruction is adapted to meet individual learner needs. While these systems are often seen as a way to improve student engagement and outcomes, concerns about fairness, access, and bias are still not fully addressed. This study examines how AI-driven personalized learning affects equity and inclusion by reviewing recent academic literature published between 2018 and 2024. Through a close reading of selected studies, common patterns and concerns were identified. Five key areas emerged from the analysis: the balance between personalization and standardization, the presence of algorithmic bias, the impact of unequal access to technology, changes in teaching roles, and ethical issues related to data use. |
|
13-21 |
