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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
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study, a pattern the authors term cognitive debt (Kosmyna et al., 2025) - though this finding is preliminary and has not yet been peer-reviewed.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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