
This paper revisits the foundational principles of Theosophy and discusses how their esoteric context and methodology can be applied to modern science with the well-established scientific research and academic instructional methodology, which has a rather traditional, conventional exoteric model. While emerging Generative AI tools open new possibilities for scientific discovery, with extended functionality of knowledge presentation, summarisation, and composition in a form that can provide a basis for and support effective scientific reasoning and hypothesis testing, they still require better defined integrative knowledge methodology that can be offered by Theosophy and esoteric teachings. The paper presents a landscape analysis to identify the possible links of Theosophy to modern scientific domains and provides suggestions for actionable strategies to empower scientific research with esoteric methodology. The presented analysis of the esoteric knowledge space and properties, and a disciple’s path to access esoteric knowledge as it is defined in Theosophical and esoteric practices. It is supported by the proposed model that illustrates the interaction between exoteric and esoteric knowledge spaces via the human consciousness that needs to be purified and tuned with the gradual spiritual development. By introducing the Two Horizons Model for Knowledge Horizon and Wisdom Horizon, the presented research proposes an actionable model for using Generative AI tools to assist the researcher or disciple in building their esoteric knowledge space.
Philosophy, Knowledge Horizon, Theosophy, Science, Generative AI, Esoteric Methodology, FOS: Philosophy, ethics and religion
Philosophy, Knowledge Horizon, Theosophy, Science, Generative AI, Esoteric Methodology, FOS: Philosophy, ethics and religion
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
