Paradigm Construction for Human-Machine Dialogic Learning: Model Design, Prompt Strategies, and Implementation Pathways Based on Generative AI

Abstract

The rapid advancement of artificial intelligence (AI) has increasingly permeated various sectors, with generative AI technologies, such as ChatGPT and ERNIE Bot, creating unprecedented opportunities and challenges for educational practices. Dialogic learning, an enduring method of instruction, is on the brink of transformation in forms and models with the integration of these cutting-edge technologies. This paper examines existing literature on the nature of dialogic learning and the types of dialogues while highlighting the advantages and characteristics of generative AI. It explores the value implications of human-machine dialogic learning, proposes a strategic framework for crafting effective prompts to engage with generative AI, and introduces a foundational model for human-machine dialogic interaction. Furthermore, the paper outlines strategies and methodologies to promote this model, with a focus on fostering problem awareness, identifying linguistic risks, enhancing digital literacy, and mitigating the risks of technology dependency.