Somagraphic Learning™ Framework: A Human-First, AI-Supported Visual Cognitive Approach

Independent Researcher/ Visual Learning Specialist, Devika Toprani

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https://hdl.handle.net/2142/133152 Copy

Description

Title

Somagraphic Learning™ Framework: A Human-First, AI-Supported Visual Cognitive Approach

Author(s)

Independent Researcher/ Visual Learning Specialist, Devika Toprani

Issue Date

2026-04

Keyword(s)

• AI in Education
• Automation Bias
• AI-Mediated Learning
• AI-Supported Learning
• Cognitive Load
• Cognitive Load Theory
• Cognitive Sovereignty
• Conceptual Mapping
• Conceptual Understanding
• Desirable Difficulty
• Diagrammatic Reasoning
• Drawing Externalization
• Educational Technology
• Embodied Cognition
• External Cognition
• Human-AI Interaction
• Human-First Learning
• Human In The Loop
• Information Visualization
• Instructional Design
• Learning Sciences
• Metacognition
• Responsible AI
• Retrieval Practice
• Shape-Emotion Grammar
• Somagraphic Learning
• Somatic AI Literacy
• Visual Cognition
• Visual Orientation
• Visual Reasoning

Date of Ingest

2026-04-28T11:00:55-05:00

Abstract

Artificial intelligence systems increasingly generate explanations, summaries, and analytical outputs at speeds that exceed the natural pace of human cognition. While these technologies expand informational access, they may compress the orientation processes through which conceptual understanding normally develops. Experimental research across seven preregistered studies demonstrates that learners who receive LLM-generated summaries develop shallower knowledge compared to those who engage in active construction through web search (Melumad & Yun, 2025). Separate empirical work further suggests that repeated AI writing assistance was associated with significantly reduced neural connectivity in an EEG study, a pattern the authors term cognitive debt (Kosmyna et al., 2025) - though this finding is preliminary and has not yet been peer-reviewed. Somagraphic Learning™ introduces a visual orientation layer that precedes language, explanation, or AI output. In this stage, learners externalize conceptual relationships using simple shapes, spatial arrangements, and motion cues before engaging with symbolic reasoning or AI-generated content. The learning process unfolds through a three-stage cycle: Attempt → Map → Refine. Grounded in embodied cognition (Lakoff & Johnson, 1999; Wilson, 2002), cognitive load theory (Sweller, 1988), human-AI interaction research (Amershi et al., 2019), and desirable difficulty principles (Bjork & Bjork, 2020), the framework positions visual cognition as a structured interface between human reasoning and AI-assisted learning. A central construct is the mitigation of automation bias - the tendency to defer to algorithmic outputs when internal conceptual models are absent (Skitka et al., 1999; Endsley, 2016). This paper presents the Somagraphic Learning™ Framework as a conceptual model and proposes a structured research agenda for empirical testing. It introduces Somatic AI Literacy as a proposed competency domain: the capacity to establish embodied conceptual orientation before AI interaction begins. It does not report experimental findings.

Publisher

Open Science Framework

Has Part

• 10.17605/OSF.IO/BJWKG
• https://www.researchgate.net/profile/Devika-Toprani
• https://www.linkedin.com/in/devika-toprani/
• https://prereview.org/preprints/doi-10.35542-osf.io-fnk7z_v1
• https://scholar.google.com/citations?hl=en&user=Nzx_W7QAAAAJ

Type of Resource

text

Genre of Resource

working paper

Language

eng

DOI

https://doi.org/10.35542/osf.io/fnk7z_v2

Copyright and License Information

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

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