Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7

BACKGROUND: The rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers new opportunities to scale and personalize diabetes self-management education and support (DSMES). This study evaluated the quality, accuracy, and reproducibility of DSMES plans generated by ChatGPT-5 using the ADCES7 Self-Care Behaviors™ framework. METHODS: Eleven virtual patient profiles representing five key DSMES time points (diagnosis, annual review, complicating factors, transitions, and ongoing care) were analyzed using structured prompts with retrieval-augmented generation. Generated plans were assessed using a DSMES Process Evaluation Checklist integrating IMPACTS, QAMAI, PDQI-9, and Donabedian's frameworks across ten domains. Content validity was confirmed by five experts (S-CVI: 0.982 for appropriateness; 0.964 for clarity). Ten certified diabetes educators (mean age 47.9 years; mean experience 14 years) independently scored the plans. Internal consistency, inter-rater reliability, and reproducibility were examined. RESULTS: Mean domain scores ranged from 29.0 ± 2.0 (patient engagement; 58.0%) to 42.7 ± 2.0 (cultural and linguistic appropriateness; 85.5%). ChatGPT-5 demonstrated strengths in clarity (85.1%) and goal-directedness (81.1%), generating evidence-based plans within three to five minutes, but showed limitations in patient engagement. Moderate performance was observed for accuracy (74.9%), relevance (78.7%), individualization (79.6%), and feasibility (75.3%). Reliability was high (Cronbach's α = 0.889; intra-class correlation coefficient = 0.837). Reproducibility was moderate, with intra-assay coefficients of 0.04 to 0.15 and inter-assay coefficients of 0.10 to 0.27. CONCLUSIONS: The DSMES process evaluation checklist is valid and reliable. ChatGPT-5 shows promise as a scalable DSMES decision-support tool, though improvements in personalization, patient engagement, and reproducibility are needed before broader implementation.