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Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context.

The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi.

The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students.

The implications of the study include the significance of integrating AI education initiatives into curricula, prioritising professional development programmes for educators and making sure that every student has equitable access to AI technologies.

The study introduces a novel perspective by examining variables such as AI literacy, AI usage, AI learning outcomes and academic performance and developing a model that has not been previously studied. It provides a new discourse and proposes a framework uniquely combining AI-infused curriculum design, educator empowerment, robust assessment mechanisms and sustainable practices.

This research aims to analyse the current state of research on the application of Artificial Intelligence (AI) in libraries by examining document type, publication year, keywords, country and research methods. The overarching aim is to enrich the existing knowledge of AI-powered libraries by identifying the prevailing research gaps, providing direction for future research and deepening the understanding needed for effective policy development.

This study used advanced tools such as bibliometric and network analysis, taking the existing literature from the SCOPUS database extending to the year 2022. This study analysed the application of AI in libraries by identifying and selecting relevant keywords, extracting the data from the database, processing the data using advanced bibliometric visualisation tools and presenting and discussing the results. For this comprehensive research, the search strategy was approved by a panel of computer scientists and librarians.

The majority of research concerning the application of AI in libraries has been conducted in the last three years, likely driven by the fourth industrial revolution. Results show that highly cited articles were published by Emerald Group Holdings Ltd. However, the application of AI in libraries is a developing field, and the study highlights the need for more research in areas such as Digital Humanities, Machine Learning, Robotics, Data Mining and Big Data in Academic Libraries.

This study has excluded papers written in languages other than English that address domains beyond libraries, such as medicine, health, education, science and technology.

This article offers insight for managers and policymakers looking to implement AI in libraries. By identifying clusters and themes, the article would empower managers to plan ahead, mitigate potential drawbacks and seize opportunities for sustainable growth.

Previous studies on the application of AI in libraries have taken a broad approach, but this study narrows its focus to research published explicitly in Library and Information Science (LIS) journals. This makes it unique compared to previous research in the field.

An individual's capacity to manage finances has become critical in today's environment. The availability of various sophisticated financial instruments, combined with the economy's complexity and rising uncertainty, has prompted a significant push to analyse from where the youth learn about managing their money. This study intends to investigate the differences in the selected social predictors (Parents, Friends, School, Books, Job Experiences, Life experiences and Media) that influence the money management behaviour of emerging adults. The data was collected through a structured questionnaire from 230 undergraduates in the age group of 18–22 years. To test the normality of data, Kolmogorov–Smirnov (KS) test was applied and further Kruskal–Wallis test was found to be the appropriate method based on the identification of statistically significant deviations. The results show that parents have been considered as the most influential predictor (X = 3.565) of money management behaviour among emerging adults. followed by Life Experiences (X = 3.526). Whereas School and Job Experience were the least influential social predictors with mean value of 2.278 and 2.130 respectively. The study provides insights to the regulators, academicians and policymakers to initiate innovative strategies and processes for helping emerging adults for effective money management to increase their academic performance in a stress-free environment. Further, this paper contributes towards effective money management advice by recommending implementation of tools, apps and programs relating to Financial Literacy for better Financial Behaviour. Lastly, the paper provides implications that focus on enhancing the financial literacy of the parents as they act as role models for their children by teaching them skills to manage money.