Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects
Project Overview
This document explores the advancements of generative AI, particularly focusing on Large Language Models (LLMs) like DeepSeek, ChatGPT, and Google Gemini, and their transformative applications in education. It details how these models can enhance learning experiences through personalized tutoring, automated grading, and content generation, thereby facilitating a more interactive and adaptive learning environment. The paper evaluates the unique architectures and performance metrics of these LLMs, addressing their strengths, such as improved engagement and accessibility, alongside weaknesses like potential biases and the need for careful implementation. Furthermore, it discusses the implications of these technologies on teaching methodologies and student outcomes, emphasizing the importance of integrating generative AI responsibly within educational frameworks. The document concludes by considering future prospects for generative AI, suggesting that ongoing advancements could lead to even more innovative educational tools, ultimately reshaping how knowledge is imparted and acquired in various learning contexts.
Key Applications
Generative AI for Educational Support
Context: Utilized for tutoring, generating educational content (text, images, and coding assistance), and delivering domain-specific information in advanced subjects like healthcare and law.
Implementation: Integrated into various educational platforms and tools to support personalized learning experiences, multimedia content generation, and domain-specific knowledge delivery.
Outcomes: ['Improved student engagement and understanding of complex subjects.', 'Enhanced learning experiences through multimodal content generation.', 'High accuracy and contextual relevance in educational materials.']
Challenges: ['Potential for generating incorrect or misleading information.', 'High computational resource requirements and potential biases in generated content.', 'Limited generalization beyond specific domains and reliance on high-quality datasets.']
Implementation Barriers
Technical Barrier
Challenges in integrating generative AI into existing educational systems due to technical limitations.
Proposed Solutions: Developing robust APIs and ensuring compatibility with educational platforms.
Ethical Barrier
Concerns over bias and misinformation generated by AI models in educational contexts.
Proposed Solutions: Implementing strict data curation processes and introducing human oversight in AI-generated content.
Resource Barrier
High computational costs associated with running advanced AI models like Gemini.
Proposed Solutions: Exploring more efficient training methods and leveraging cloud computing resources.
Project Team
Anichur Rahman
Researcher
Shahariar Hossain Mahir
Researcher
Md Tanjum An Tashrif
Researcher
Airin Afroj Aishi
Researcher
Md Ahsan Karim
Researcher
Dipanjali Kundu
Researcher
Tanoy Debnath
Researcher
Md. Abul Ala Moududi
Researcher
MD. Zunead Abedin Eidmum
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Anichur Rahman, Shahariar Hossain Mahir, Md Tanjum An Tashrif, Airin Afroj Aishi, Md Ahsan Karim, Dipanjali Kundu, Tanoy Debnath, Md. Abul Ala Moududi, MD. Zunead Abedin Eidmum
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai