Template-Type: ReDIF-Article 1.0 Author-Name: P. U. Nishanthi Title: The Paths to Equal: Multidimensional Framework to Measure Empowerment Deficiency and Gender Gap Abstract: Paths to equal is a comprehensive view of nations' advancements in women's empowerment and gender equality because this is based on two indices WEI and GGPI. The main theme of this article is to analyse different aspects of multidimensional framework used by UNDP to measure empowerment gap and gender gap. This is a review article based on ‘Paths to equal: Twin Indices on Women's Empowerment and Gender Equality'. Only secondary data has been used. Source of data is the report itself. In this article dimensions and indicators of each WEI and GGGI has been analysed. Women empowerment and gender gap has been analysed separately and jointly by country wise and region wise. A different picture of gender parity and women empowerment is derived when two indices are analysed separately and jointly across various countries, regions and dimensions. The results, when viewed through this new perspective, are dismal. No nation has attained complete gender parity, and women's autonomy and authority to make decisions and take advantage of opportunities are still severely limited. Significant gender disparities and low levels of women's empowerment are prevalent. Additionally, the data demonstrates that improving human development alone is not the solution. Some of the narrowest gender inequalities are seen in World nations that rank lower on the Human Development Index. Definitely, twin indices offer a clear synopsis of complicated topics, they can be helpful for policy analysis and decision-making. While each focus on a distinct set of concerns, taken as a whole, they offer a more comprehensive view of nations' progress towards gender parity and women's empowerment. Journal: Shanlax International Journal of Arts, Science and Humanities Pages: 1-13 Volume: 13 Issue: 1 Year: 2025 Month: July File-URL: https://shanlaxjournals.in/journals/index.php/sijash/article/view/8964 File-Format: text/html File-URL: https://shanlaxjournals.in/journals/index.php/sijash/article/view/8964/8078 File-Format: Application/pdf Handle: RePEc:acg:sijash:v:13:y:2025:i:1:p:1-13 Template-Type: ReDIF-Article 1.0 Author-Name: Kanchan Chetiwal Author-Name: S. Arulsamy Title: Enhancing Education with AI: A Comparative Study of Traditional and Generative AI Chatbots Abstract: Chatbot technologies are transforming education through the integration of artificial intelligence (AI). The present study compares the educational applications of traditional rule-based chatbots (ELIZA, ALICE, Mitsuku) and modern generative AI-powered chatbots (Chat GPT, Google Bard, Jira, Hugging face and Jasper AI). Natural Language Processing (NLP) is the common ground for both types of chatbots, while the traditional chatbots employ the rule-based NLP techniques – pattern matching and scripted response, and generative AI chatbots rely on deep learning and dynamic interactions. The core objective of the present study is to assess and contrast the concept, functionality, adaptability and integration capabilities of both chatbot types within educational contexts. The evaluation methodology involves secondary data analysis drawn from academic sources, using criteria such as background, setup, cost, knowledge, personalization, privacy, security, ethics, accessibility, teaching impact, privacy & security providing a qualitative basis for comparison. This approach helped uncover their strengths and weaknesses, offering insights for schools, teachers, and educational technologists. The primary purpose of this study is to compare the roles and effectiveness of both types of AI chatbots in education. Ultimately, it helps educators and stakeholders choose the right chatbot for their specific learning environment. Findings show that while both types of chatbots aim to streamline communication and support students with routine tasks, their core mechanisms differ. Traditional chatbots rely on static, rule-based logic, whereas generative AI chatbots adapt dynamically and generate human-like responses. Privacy concerns are also key differentiators—generative bots need strict data regulation compliance compared to the more controlled data usage in traditional bots. This study concludes by emphasizing AI chatbots can shape the future of learning through enhanced personalization and support, while acknowledging technological, ethical, and implementation challenges that need addressing in future research. Journal: Shanlax International Journal of Arts, Science and Humanities Pages: 42-51 Volume: 13 Issue: 1 Year: 2025 Month: July File-URL: https://shanlaxjournals.in/journals/index.php/sijash/article/view/9034 File-Format: text/html File-URL: https://shanlaxjournals.in/journals/index.php/sijash/article/view/9034/8082 File-Format: Application/pdf Handle: RePEc:acg:sijash:v:13:y:2025:i:1:p:42-51