Conversation-Thread Condition and Temporal Reproducibility of AI-Generated Plant Images: A 30-Day Longitudinal Case Study with Implications for Sustainable Digital Education

Generative artificial intelligence (GenAI) can accelerate the production of digital teaching materials, but long-term use also requires provenance, reproducibility, and maintainable resource workflows. In a single-account 30-day longitudinal case study, we generated images of the moss Pleurozium schreberi with ChatGPT and Gemini under persistent-thread (P) and new-thread (N) conditions and assessed structural similarity (SSIM), perceptual distance (LPIPS), and CLIP embedding distance. The experimental contrast concerned visible persistent-thread continuity versus newly opened conversations; account-level memory, personalisation, model routing, and other provider-side state were not independently controlled. Under the primary preprocessing, within-day P-N divergence was greater in the observed ChatGPT series than in Gemini for perceptual and CLIP-embedding distance. The LPIPS contrast remained robust under padding-free preprocessing, whereas the structural SSIM cross-platform contrast was strongly preprocessing-sensitive; equivalent geometric-preprocessing sensitivity was not assessed for CLIP. Persistent-thread outputs showed greater observed day-to-day similarity for ChatGPT at structural and perceptual levels, while Gemini showed smaller but consistent P-N differences. Padding-free sensitivity analyses preserved the perceptual within-day contrast and the principal day-to-day thread-condition patterns but showed that the cross-platform within-day SSIM result depended strongly on image preprocessing. The study did not measure learning outcomes, botanical accuracy, energy use, carbon emissions, educator time, or monetary cost; the sustainability implications are therefore limited to provenance-aware digital-resource management and should not be interpreted as demonstrated environmental or economic benefits. Prompt-only documentation was insufficient for recovering a stable visual asset; AI-generated visuals intended for educational reuse should therefore be archived and versioned with their prompt, platform, date, and thread context, while content quality requires separate botanical and pedagogical validation.