Exploring the Integration of Gestalt Theory, Generative AI, and Stigmergic Schema in Text-based Educational Environments

Potential Abstract:
This research article investigates the potential of integrating principles from Gestalt theory, generative artificial intelligence (AI), and stigmergic schema in text-based educational environments. The aim is to enhance learning experiences by leveraging the power of AI to generate interactive and adaptive educational content that aligns with learners’ cognitive processes.

The study proposes a novel pedagogical approach that capitalizes on the concept of gestalt perception to organize textual content into coherent and meaningful wholes, thereby promoting deep understanding and knowledge retention. This approach involves the utilization of generative AI techniques to dynamically generate educational texts that adapt to learners’ individual needs, preferences, and prior knowledge. Furthermore, the incorporation of stigmergic schema provides a distributed and self-organizing framework for learners to collaboratively construct knowledge and make sense of complex concepts within the text-based environment.

To investigate the educational efficacy of this proposed approach, a mixed-methods research design will be employed, combining quantitative data from learner performance measures and qualitative data from learner feedback and observations. The study will involve learners from diverse educational backgrounds and age groups, engaging them in authentic learning tasks within the text-based educational environment.

The anticipated outcomes of this research include insights into the effectiveness of integrating gestalt theory, generative AI, and stigmergic schema in improving learning outcomes, as well as recommendations for instructional design and implementation strategies. Additionally, this study contributes to the broader field of AI in education by exploring the potential of generative AI models to create adaptive and personalized educational materials.

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