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Using a cross-sectional quantitative survey of 200 students, results identify purposes such as information seeking, problem solving, completing assignments, feedback, and motivation. PCA and regression analyze usability, ethical considerations, and technological barriers shaping adoption, offering implications for educators and policymakers.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Education-TOJDE January 2026 ISSN 1302-6488 Volume: 27 Number: 1 Article: 5  \nLEVERAGING AI CHATBOTS FOR SELF-DIRECTED LEARNING: A STUDY OF PRACTICES AND DRIVERS TO  \nADOPTION AMONG COLLEGE STUDENTS  \nAntara MUKHERJEE  \nORCID: 0009-0002-2401-8064 Department of Education Central University of Jharkhand Ranchi, INDIA  \nDr. Shashi SINGH  \nORCID: 0000-0001-8706-8590 Department of Education Central University of Jharkhand Ranchi, INDIA  \nReceived: 04/02/2025 Accepted: 20/05/2025  \nABSTRACT  \nAI chatbots, the interactive software, utilizing natural language processing and artificial intelligence (AI), are increasingly used in various domains, including education, where they offer personalized and adaptive learning support. This study explores college students’ utilization of AI chatbots in their self-directed learning experiences, where learners initiate, plan, and regulate their learning activities themselves. Employing across-sectional descriptive approach, a quantitative survey was used to scrutinize the AI chatbot usage practices and the factors that influences their adoption among randomly selected 200 college students from diverse backgrounds. Findings reveal a spectrum of purposes for using the interacting agents, including information seeking, problem solving, completing assignments, feedback, and motivation. Furthermore, Principal Component Analysis (PCA) scrutinised usability, ethical considerations, and technological barriers as the factors affecting AI chatbot adoption among students in higher education. To assess the strength of relationships between these factors and participants’ adoption behaviour, regression analysis was conducted, which depicted a significant contribution to the study’s model. Contributing to the literature on AI chatbotsand self-directed learning, this research offers implications for educators, developers, and policymakers in designing and implementing AI chatbots effectively and ethically for education. Considering recent tech advancements and educational needs, institutions should consider integrating AI chatbots into their learning systems to enhance overall educational experiences and outcomes. With the limitation of considering less factors, this study can be a starting point of research on leveraging AI-based technologies towards achieving self-guided learning goals.  \nKeywords: Artificial intelligence, AI chatbots, self-directed learning, technology adoption, educational practices, technological factors.  \nINTRODUCTION  \nThe emergence of Artificial Intelligence (AI) marks a revolutionary leap in technology, reshaping a wide array of sectors from healthcare to entertainment, and from transportation to education, with profound and far-reaching impact across the board. The capacity of AI to analyse vast quantities of data, recognise patterns, use algorithms, and make predictions has revolutionised decision-making process, optimising efficiency, and accuracy in every imaginable field. AI has rapidly advanced in today’s world, with the potential to outperform humans in various tasks (Poola, 2017) . This progression of AI has been observed since the 1950s, but has recently flourished gigantically with the emergence of smart hubs, intelligent data analytics, and self-learning systems in robot  \napplications (Jaakkola et al., 2019) . The emergence of the internet and intranets has led to an AI renaissance, with natural language processing and machine learning AI technologies being embedded in computing environments (O’Leary, 1997). The realm of AI has progressed to contemplate the interplay among the body, mind, and surroundings, leading to a broader understanding of intelligence and its applications (Lungarella, 2006) . Overall AI’s versatility and adaptability continue to unlock new possibilities and customisation at the individual level, revolutionising society and driving innovation across the globe.  \nThe National Education Policy (NEP) 2020 emphasizes on th","cbCaibGBxyRTxyjX","https://ap.wps.com/l/cbCaibGBxyRTxyjX","pdf",286689,12,"English","# Abstract\n# Introduction\n## Policy and educational context (NEP 2020)\n## Role of AI in self-directed learning\n## What AI chatbots are and where they are used","[{\"question\":\"What is the focus of the study on AI chatbots and self-directed learning?\",\"answer\":\"The study investigates how college students use AI chatbots during self-directed learning and what drives their adoption.\"},{\"question\":\"Which purposes do students report for using AI chatbots?\",\"answer\":\"Students use AI chatbots for information seeking, problem solving, completing assignments, getting feedback, and staying motivated.\"},{\"question\":\"What factors are analyzed as drivers of AI chatbot adoption?\",\"answer\":\"The study examines usability, ethical considerations, and technological barriers, and tests their relationships with students’ adoption behavior using regression analysis.\"}]","Leveraging AI Chatbots for Self-Directed Learning: A Study of Practices and Drivers to Adoption Among College Students | PDF",1792042952]