Daily Archives: July 23, 2026

Authors: Dr Abhijit Das, Dr Namrata Yadav Das

Abstract: Background: Artificial intelligence (AI) technologies are increasingly integrated into educational settings to enhance student engagement and classroom interaction. However, the evidence base regarding their effectiveness remains fragmented. This systematic review synthesizes empirical evidence on the role of AI in improving student engagement and classroom interaction in K-12 and higher education contexts. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive literature search across multiple databases (SciSpace, Google Scholar, PubMed) from January 2016 to March 2026. Studies were included if they reported empirical evidence on AI interventions targeting student engagement or classroom interaction outcomes in educational settings. Two independent reviewers screened 267 unique records, assessed 204 full-text articles, and included 142 studies in the final synthesis. Risk of bias was assessed using the ROBINS-I tool for six representative studies. Results: From 267 unique records identified, 142 studies met inclusion criteria after title/abstract screening and full-text assessment. Six representative studies (N=50–20,000+ participants) demonstrated that AI interventions—including personalized recommendation systems, intelligent tutoring systems, conversational agents, and adaptive learning platforms—consistently improved student engagement metrics. Two randomized controlled trials showed low risk of bias, while four quasi-experimental studies showed moderate risk. AI-driven interfaces increased engagement by up to 25.13% in large-scale field tests. Personalized AI recommendations significantly improved learning performance and engagement, particularly for students with moderate motivation levels. AI tutors enabled students to learn more than twice as much in less time compared to traditional active learning approaches. Conclusions: The evidence demonstrates that AI technologies can effectively enhance student engagement and classroom interaction across diverse educational contexts. Randomized controlled trials provide the strongest evidence, while quasi-experimental studies show consistent positive effects despite moderate methodological limitations. Future research should prioritize rigorous experimental designs with preregistration, comprehensive reporting of missing data, and investigation of long-term effects and equity considerations.

DOI: http://doi.org/10.5281/zenodo.21509136

Published by: vikaspatanker

Authors: Dr. Bayomock Linwa André Claude, Mrs. Diagone Grace

Abstract: In modern days, getting his first job is a very hard process for newly graduated students. Several factors contribute to this issue: no experience, poor resume qualities, few knowledge of job market and required skills for a given job title, no real exposure to job interview (phone, virtual, physical). In the past 30 to 50 years, structured human resources agencies played great role helping young graduates to be well prepared for job readiness. Techniques as how to write a good resume, determining job title and job profile in a particular domain, how to handle a phone, virtual and physical interview were taught to a candidate. Traditional advisory systems are often insufficient in addressing these structural issues at scale. This paper presents an AI-powered web-based career development platform designed to enhance employability among students and job seekers in Côte d'Ivoire. The built application is called DIJOB (Digitalized Job), DIJOB integrates Groq’s LLaMA 3.3-70B large language model to provide four core services: intelligent job search with AI-based CV matching, automated résumé quality assessment, skill compatibility scoring against job descriptions, and a personalized eleven-module career coaching system. The coaching module includes salary estimation in FCFA, LinkedIn profile optimization, and interview preparation guidance. The system is implemented using Jakarta EE 10, GlassFish 7, MySQL 8, and a responsive XHTML frontend, following a three-tier architecture with a RESTful API layer. Results indicate that the platform provides real-time, actionable career insights, including résumé scoring, skill gap analysis, and personalized career recommendations. DIJOB shows strong potential in improving alignment between job seekers and employer expectations, thereby contributing to reduced skills mismatch and improved employability outcomes.

DOI: http://doi.org/10.5281/zenodo.21505674

Published by: vikaspatanker

Authors: Research Scholar Krishnapriya T S, Associate Professor Dr.P Arumugam

Abstract: Inflation is one of the most significant macroeconomic indicators influencing the stability and sustainable growth of an economy. During periods of economic uncertainty, particularly the COVID-19 pandemic, inflation exhibited substantial fluctuations characterized by extreme observations and increased volatility, indicating the presence of fat-tailed behaviour. Such characteristics often reduce the effectiveness of conventional statistical measures, making robust estimation techniques essential for reliable analysis. This study investigates the behaviour of inflation in India and examines its relationship with the Reserve Bank of India's (RBI) monetary policy, represented by the repo rate, over the period from 2014 to 2022. Monthly data obtained from the Reserve Bank of India (RBI) Database on Indian Economy (DBIE) are used for the empirical analysis. To obtain a robust measure of central tendency in the presence of extreme observations, a 25% trimmed mean is employed and compared with the conventional arithmetic mean. Furthermore, a simple linear regression model is developed to examine the relationship between inflation and the repo rate, and the model parameters are estimated using the Maximum Likelihood Estimation (MLE) method. The empirical findings indicate that inflation exhibited noticeable fat-tailed characteristics during the pandemic period, while the trimmed mean provided a more stable and representative estimate than the conventional mean. The regression analysis further demonstrates a significant association between inflation and the repo rate, highlighting the effectiveness of monetary policy interventions in maintaining price stability during periods of economic uncertainty. The study concludes that robust statistical techniques, such as trimmed mean estimation combined with maximum likelihood methods, provide valuable tools for analysing inflation dynamics and supporting evidence-based monetary policy decisions.

Published by: vikaspatanker