Enhancing Education with AI: A Comparative Study of Traditional and Generative AI Chatbots

Kanchan Chetiwal; S. Arulsamy

doi:10.34293/sijash.v13i1.9034

Kanchan Chetiwal Research Scholar, Department of Education, Bharathiar University, Coimbatore, Tamil Nadu, India https://orcid.org/0009-0004-4274-7951
S. Arulsamy Professor, Department of Education, Bharathiar University, Coimbatore, Tamil Nadu, India https://orcid.org/0000-0002-8818-7337

DOI: https://doi.org/10.34293/sijash.v13i1.9034

Keywords: Artificial Intelligence (AI), Chatbots, Education, Generative Chatbots, Traditional 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.