Generative AI technologies like ChatGPT have the potential to drastically change teaching and learning approaches in educational environments through human-robot collaboration and natural language processing capabilities.

Objective: The main objectives of this study were to: 1) develop technology trends on the application of AI in education to address learning demands driven by Society 5.0, 2) examine the future separation of human and artificial intelligence in educational contexts, and 3) investigate how generative AI understands and produces human-like text by analyzing intricate patterns and structures of human language.

Methods: The study employed design science methodology to create scientific artifacts, specifically an Actor Network Theory (ANT) model for both current ChatGPT and future generative AI applications in educational pedagogy. The researchers conducted extensive scholarly and scientific investigations to design a framework for ChatGPT and analyzed its transformer architecture. They explored how the ANT model could be applied in National Research and Education Network (NREN) environments to enhance teaching and research in higher education institutions.

Key Findings:

• ChatGPT represents a significant technological achievement as a revolutionary tool for natural language processing (NLP) and a transformative educational business tool.
• The boundaries between AI agents and humans are becoming increasingly blurred, although scientists still have significant progress to make before creating connections beyond isolated exchanges.
• Educational robots with NLP capabilities are becoming more accessible, enabling personalized coaching and learning strategy modifications for individual students.
• ChatGPT's capacity to understand and produce human-like language by employing NLP to generate semantics has been essential to its ability to replicate advanced human technology.
• Through the ANT model framework, the authors identified that more actors should be involved in creating NREN service portfolios to raise teaching and research standards in higher education.
• The model demonstrated how AI educational robots and users can exchange intelligence to achieve educational business purposes.

Implications: The study highlights that generative AI technologies have the potential to significantly transform educational approaches by:

• Enabling personalized learning routes through analyzing student performance data
• Offering immediate feedback, tailored direction, and corrective assistance
• Supporting the creation of high-quality educational content based on specific learning objectives
• Enhancing engagement levels and improving learning outcomes
• Facilitating human-robot collaboration in educational contexts
• Automating customer service in educational institutions and personalizing student interactions
• Filling gaps left by unqualified teachers in subject areas like programming and language learning

Limitations: The study acknowledges several limitations and concerns:

• The implementation of AI in education faces challenges, particularly in areas requiring digital flexibility and knowledge transfer
• There are ethical concerns about the responsible deployment of technologies like ChatGPT in educational contexts
• The research primarily focused on higher education contexts rather than a broader spectrum of educational environments
• The ANT technique assumes that if one active player leaves the network, it will negatively affect all other active players
• Networks are constantly evolving due to the complexity and unpredictability of social developments within organizational enterprises
• Limited access and skills related to ICTs were identified as barriers to implementation

Future Directions: The authors suggest several areas for future research:

• Identifying proper devices and components for the design of educational robots
• Exploring the long-term impact of AI-dominated educational futures where general and educational technologies combine
• Further research on organizational robotics with a focus on human collaboration and education
• Developing better frameworks for AI pedagogic learning using instructional robotics applications
• Investigating how society may impact and contribute to AI development while reducing potential misfortunes
• Examining ways to ensure appropriate human responsibility as producers of AI educational tools
• Monitoring the transition from Industry 4.0 to Society 5.0 and its impact on educational institutions

Title and Authors: "Generative Artificial Intelligence (AI) Educational Pedagogy Development: Conversational AI with User-Centric ChatGPT4" by Ugochukwu Okwudili Matthew, Kafayat Motomori Bakare, Godwin Nse Ebong, Charles Chukwuebuka Ndukwu, and Andrew Chinonso Nwanakwaugwu.

Published On: December 13, 2023

Published By: Journal of Trends in Computer Science and Smart Technology (ISSN: 2582-4104), Volume 5, Issue 4, Pages 401-418

The research provides a comprehensive analysis of how generative AI, particularly ChatGPT, is shaping the educational landscape. It emphasizes that as AI technologies continue to evolve, they will likely become more integrated into educational settings, creating new opportunities for personalized learning and human-machine collaboration. The authors constructed an Actor Network Theory model that demonstrates how various elements interact within an AI-enhanced educational environment, providing a framework for understanding these complex relationships. While acknowledging concerns about ethics, implementation challenges, and potential job displacement, the study ultimately views the integration of generative AI in education as an inevitable and potentially beneficial development that will require careful management and ongoing research.