With a substantial increase projected in the global diabetes population, the urgent need for effective self-management strategies becomes evident. Diabetes self-management education and support (DSMES) has proven critical for improving outcomes in individuals with type 2 diabetes mellitus (T2DM). Despite its benefits, many patients fail to meet key health targets, highlighting the potential of innovative solutions like chatbots to enhance DSMES delivery. Leveraging artificial intelligence, chatbots provide real-time feedback, personalized guidance, and continuous support, facilitating behavioral changes and improving self-management. However, it remains to be explored how patients perceive and adopt chatbots for T2DM management and identifies design strategies to optimize chatbot effectiveness within the DSMES framework. This study applies behavioral reasoning theory (BRT) to examine the adoption intention (AI) of chatbots for DSMES, offering a nuanced analysis of reasons for (RF) and against (RA) adoption. A survey of 144 respondents assessed DSMES status using the Chinese Diabetes Management Self-Efficacy Scale (C-DMSES) and explored chatbot adoption intentions through constructs from BRT. Partial least squares structural equation modeling (PLS-SEM) evaluated the relationships among health consciousness, RF, RA, attitudes, and adoption intentions. Results indicate high acceptance of DSMES chatbots among Chinese T2DM patients. RF factors such as relative advantage, compatibility, and complexity positively influence adoption attitudes, while RA factors, including usage, risk, and tradition barriers, hinder adoption. Health consciousness mediates these effects through RF and RA. This study advances the theoretical understanding of chatbot adoption intentions and offers practical insights to optimize chatbot design and implementation within DSMES frameworks.