Gestalt-Inspired Whole-to-Part Analysis: Integrating Chunking and Meta-Prompting for Insightful Learning in AI-powered Large Language Models

AI-embedded large language Models (LLMs) process any teaching learning input into mechanical form aligning with word to word manner. The students and teaching practitioners generally face immense difficulties as these models produce fragmented and incoherent pieces of text that lack deeper comprehension. They usually fail to internalize the bigger narrative behind a complex learning situation. This challenging situation can be truly addressed with the Gestalt psychological principle which strictly assumes that a learner can only understand a complex topic when the learning follows the rule of whole-to- the parts. This paper aims to propose a novel educationally grounded framework titled “Gestalt-inspired whole to part analysis for AI-mediated learning environment.  This framework stresses on the effectiveness of Gestalt psychology in AI-driven learning output. This paper adopts a conceptual analytical method where, the principle of proximity and closure, the fundamental core of Gestalt psychology can optimize the learning productivity. This research study shows that the whole-to-part analysis prevents the AI-powered model from experiencing the confusion created out of disjointed context. This framework establishes the urgent need to curate the AI-based LLM models to achieve a form of insightful learning which strongly points out a sudden mental clarity on how discrete chunk of information connect to constitute a comprehensive whole. Henceforth, the framework provides an inherent human-centred blueprint for designing intelligent educational tools that can facilitate learners to become an insightful individual.