  We introduce CONA, a novel context-aware instruction paradigm for effective
knowledge dissemination using generative pre-trained transformer (GPT) models.
CONA is a flexible framework designed to leverage the capabilities of Large
Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge,
Wisdom) hierarchy to automatically instruct and optimise presentation content,
anticipate potential audience inquiries, and provide context-aware answers that
adaptive to the knowledge level of the audience group. The unique aspect of the
CONA paradigm lies in its combination of an independent advisory mechanism and
a recursive feedback loop rooted on the DIKW hierarchy. This synergy
significantly enhances context-aware contents, ensuring they are accessible and
easily comprehended by the audience. This paradigm is an early pioneer to
explore new methods for knowledge dissemination and communication in the LLM
era, offering effective support for everyday knowledge sharing scenarios. We
conduct experiments on a range of audience roles, along with materials from
various disciplines using GPT4. Both quantitative and qualitative results
demonstrated that the proposed CONA paradigm achieved remarkable performance
compared to the outputs guided by conventional prompt engineering.
