A Framework to Overcome the Dark Side of Generative Artificial Intelligence (GAI) Like ChatGPT in Social Media and Education

As the performance of generative artificial intelligence (GAI), such as ChatGPT, improves, content created by GAI will be distributed in the social media space, and knowledge and writings from unknown sources will be disseminated and reproduced. Now that GAI is becoming widespread, it is necessary to distinguish GAI from human intelligence, which constitutes knowledge. The data, information, knowledge, and work (DIKW) hierarchy is a useful framework for teaching and for checking metacognitive and explainable artificial intelligence (XAI) literacy. There are two types of collaboration between GAI and human intelligence: a combined intelligence model and a parallel intelligence model. The combined intelligence model is a method of using GAI for creating works by collecting data, organizing information, and deriving knowledge from information. This model is suitable for GAI-assisted tasks (GAIATs). The parallel intelligence model is suitable for GAI-assisted learning (GAIAL); it is a method in which a person develops abilities by analyzing and comparing tasks created by GAI after going through the data-information-knowledge-work process. The zone of proximal development (ZPD) created by educational scaffolding is a quantitative framework that is appropriate for evaluating the effects of GAI. The ZPD generated by GAI that corresponds to scaffolding should be managed so as not to favor or disadvantage specific individuals.