Parametric Literacy Tool: Enhanced Educational Edition

Version: 3.0.1

The Parametric Literacy Tool is a browser-based educational software package for teaching digital image processing through parametric literacy: the ability to understand visual outcomes as explicit configurations of adjustable parameters.

The tool combines an HTML5 Canvas image-processing interface, real-time RGB/luminance histograms, clipping diagnostics, histogram metrics, before/after split view, point zoom inspection, structured AI prompt generation, JSON metadata export/import, and an optional AI Tutor for parameter-aware feedback.

The repository is prepared as an open-source educational software artifact. It documents the software design, classroom-oriented use cases, privacy behavior, and reproducibility features. It does not claim controlled evidence of learning effectiveness.

Live Demo

GitHub Pages:

https://gk1966.github.io/Parametric_Literacy_Learning_Tool/

Direct application:

https://gk1966.github.io/Parametric_Literacy_Learning_Tool/enhanced_parametric_app.html

Repository

https://github.com/gk1966/Parametric_Literacy_Learning_Tool

Author

Georgios Korakakis
Assistant Professor
Department of Graphic Design and Visual Communication
School of Applied Arts and Culture
University of West Attica (UniWA), Greece

Archived Releases

Current corrected release:

v3.0.1: https://doi.org/10.5281/zenodo.20582977

Concept DOI for the full Zenodo version family:

https://doi.org/10.5281/zenodo.19451350

Previous archived releases:

v3.0.1: https://doi.org/10.5281/zenodo.20582977
v3.0.0: https://doi.org/10.5281/zenodo.20366944
v2.0.0: https://doi.org/10.5281/zenodo.20112617
v1.0.0: https://doi.org/10.5281/zenodo.19451351

For publication or formal citation, please cite the Zenodo DOI corresponding to the exact version used.

Key Features

• Client-side image processing using HTML5 Canvas and JavaScript.
• Built-in AI-generated landscape demo image for quick testing.
• Pixel-level parameter manipulation through Canvas/ImageData workflows.
• Real-time RGB/luminance histograms and simple quantitative metrics.
• Diagnostic clipping overlay and clipping percentage indicators.
• Before/after split view for comparing original and edited images.
• Point zoom inspection for examining local detail, noise, blur, sharpening, and clipping.
• Parameter search, presets, undo/redo, and reset-by-group controls.
• JSON metadata export/import for reproducible parameter states.
• Structured prompt generation from active parameter states.
• Optional AI Tutor requiring a user-provided API key and explicit privacy confirmation.
• Scientific tooltip explanations with DOI-based further reading.

Requirements

• A modern desktop web browser with HTML5 Canvas and JavaScript support.
• No server, build step, package installation, or backend is required for the core application.
• Optional AI Tutor functions require a user-provided Gemini API key.

Privacy and API Notes

The core editing workflow runs locally in the user's browser. No image is uploaded by the application during ordinary slider-based editing, histogram rendering, clipping visualization, image export, JSON metadata export, or JSON import.

The optional AI Tutor sends a compressed canvas snapshot and active parameter context to an external AI API only when the user explicitly invokes the AI analysis function. API keys are stored locally in the browser.

JSON metadata exports do not include image pixels or API keys.

Getting Started

1. Open index.html.
2. Launch enhanced_parametric_app.html.
3. Load the built-in demo image or upload a non-sensitive image.
4. Adjust parameters and inspect the live preview, histogram, metrics, clipping warnings, and prompt output.
5. Use Export JSON to save a reproducible parameter record.
6. Optional: enter a Gemini API key in the AI Tutor tab for parameter-aware AI feedback.

Educational Use

Example learning activities are provided in EXAMPLES.md. They cover tonal diagnostics, reproducible editing, and AI-prompt translation from explicit image parameters.

The tool is intended for introductory digital imaging, design education, visual communication, photography, media production, and AI literacy activities.

Testing

Manual reviewer testing steps are provided in TESTING.md. The repository is a static HTML/CSS/JavaScript application, so the main checks are browser launch, demo-image loading, parameter manipulation, histogram updates, clipping overlay behavior, JSON export/import, prompt generation, and optional AI Tutor privacy confirmation.

Community and Support

Contribution, issue-reporting, and support guidelines are provided in CONTRIBUTING.md.

Documentation

Technical and educational manual:

Parametric_Literacy_Technical_Manual.html

Image attribution:

IMAGE_ATTRIBUTION.md

Tooltip bibliography:

TOOLTIP_REFERENCES.md

Suggested Citation

Korakakis, G. (2026). Parametric Literacy Tool: Enhanced Educational Edition (v3.0.1). Zenodo. https://doi.org/10.5281/zenodo.20582977

See CITATION.cff for machine-readable citation metadata.

License

The software source code is released under the MIT License. See LICENSE for details. Documentation, repository text, and the built-in demo asset are described in CONTENT_LICENSE.md.