IT Tech and Media
Role of AI chatbots in education systematic literature review
Formatted :
Africa, Europe
Description
AI chatbots shook the world not long ago with their potential to revolutionize education systems in a myriad of ways. AI chatbots can provide immediate support by answering questions, offering explanations, and providing additional resources. Chatbots can also act as virtual teaching assistants, supporting educators through various means. In this paper, we try to understand the full benefits of AI chatbots in education, their opportunities, challenges, potential limitations, concerns, and prospects of using AI chatbots in educational settings. We conducted an extensive search across various academic databases, and after applying specific predefined criteria, we selected a final set of 67 relevant studies for review. The research findings emphasize the numerous benefits of integrating AI chatbots in education, as seen from both students' and educators' perspectives. We found that students primarily gain from AI-powered chatbots in three key areas: homework and study assistance, a personalized learning experience, and the development of various skills. For educators, the main advantages are the time-saving assistance and improved pedagogy. However, our research also emphasizes significant challenges and critical factors that educators need to handle diligently. These include concerns related to AI applications such as reliability, accuracy, and ethical considerations.
Overview
The traditional education system faces several issues, including overcrowded classrooms, a lack of personalized attention for students, varying learning paces and styles, and the struggle to keep up with the fast-paced evolution of technology and information. As the educational landscape continues to evolve, the rise of AI-powered chatbots emerges as a promising solution to effectively address some of these issues. Some educational institutions are increasingly turning to AI-powered chatbots, recognizing their relevance, while others are more cautious and do not rush to adopt them in modern educational settings. Consequently, a substantial body of academic literature is dedicated to investigating the role of AI chatbots in education, their potential benefits, and threats.
AI-powered chatbots are designed to mimic human conversation using text or voice interaction, providing information in a conversational manner. Chatbots history dates back to the 1960s and over the decades chatbots have evolved significantly, driven by advancements in technology and the growing demand for automated communication systems. Created by Joseph Weizenbaum at MIT in 1966, ELIZA was one of the earliest chatbot programs (Weizenbaum,1966). ELIZA could mimic human-like responses by reflecting user inputs as questions. Another early example of a chatbot was PARRY, implemented in 1972 by psychiatrist Kenneth Colby at Stanford University (Colby,1981). PARRY was a chatbot designed to simulate a paranoid patient with schizophrenia. It engaged in text-based conversations and demonstrated the ability to exhibit delusional behavior, offering insights into natural language processing and AI. Developed by Richard Wallace in 1995, ALICE (Artificial Linguistic Internet Computer Entity) was an early example of a chatbot using natural language processing techniques that won the Loebner Prize Turing Test in 20002001 (Wallace,1995), which challenged chatbots to convincingly simulate human-like conversation. Later in 2001 ActiveBuddy, Inc. developed the chatbot SmarterChild that operated on instant messaging platforms such as AOL Instant Messenger and MSN Messenger (Hoffer et al.,2001). SmarterChild was a chatbot that could carry on conversations with users about a variety of topics. It was also able to learn from its interactions with users, which made it more and more sophisticated over time. In 2011 Apple introduced Siri as a voice-activated personal assistant for its iPhone (Aron,2011). Although not strictly a chatbot, Siri showcased the potential of conversational AI by understanding and responding to voice commands, performing tasks, and providing information. In the same year, IBM's Watson gained fame by defeating human champions in the quiz show Jeopardy (Lally & Fodor,2011). It demonstrated the power of natural language processing and machine learning algorithms in understanding complex questions and providing accurate answers. More recently, in 2016, Facebook opened its Messenger platform for chatbot development, allowing businesses to create AI-powered conversational agents to interact with users.
Content
A systematic review follows a rigorous methodology, including predefined search criteria and systematic screening processes, to ensure the inclusion of relevant studies. This comprehensive approach ensures that a wide range of research is considered, minimizing the risk of bias and providing a comprehensive overview of the impact of AI in education. Firstly, we define the research questions and corresponding search strategies and then we filter the search results based on predefined inclusion and exclusion criteria. Secondly, we study selected articles and synthesize results and lastly, we report and discuss the findings. To improve the clarity of the discussion section, we employed Large Language Model (LLM) for stylistic suggestions.
Form for Brochure / Sample Report Request
Enter your details to download the sample report.
What you get?
All three authors collaborated to select the articles, ensuring consistency and reliability. Each article was reviewed by at least two co-authors. The article selection process involved the following stages: Initially, the authors reviewed the studies' metadata, titles, abstracts, keywords and eliminated articles that were not relevant to research questions. This reduced the number of studies to 139. Next, the authors evaluated the quality of the studies by assessing research methodology, sample size, research design, and clarity of objectives, further refining the selection to 85 articles. Finally, the authors thoroughly read the entire content of the articles. Studies offering limited empirical evidence related to our research questions were excluded. This final step reduced the number of papers to 67. Figure1presents the article selection process.