||A study of chatbot users’ attitudes & perceptions in Thailand is related to a contemporary topic in applied marketing, which emphasizes technology opportunity. The first objective is to explore user’s attitudes and perceptions of using chatbots considering four factors that affect satisfactions including, performance expectancy, effort expectancy, facilitating conditions, and technology anxiety. The second objective is to identify user segments by demographics. The third objective is to identify the type of services that are acceptable for chatbots. The research method consists of 3 steps: 1) Study and review of literature and related research to the educational context for acquisition factor and framework. 2) Primary research for In-depth interviews and online survey to find insights and different aspects of the users’ intention to use the chatbot, and lastly 3) Analysis and conclusion of research results. A chatbot is a computer program that interacts like a human and simulates human conversation through text command by using artificial intelligence (AI) to communicate with users. It is generally integrated with messaging platforms such as Facebook and LINE. Chatbots have been widely used as marketing services tools in several industries such as banking, e-commerce, insurance, and restaurants. Nevertheless, the big challenge is that Thai language is more complex than English. It is an unsegmented language, ambiguous, and unknown words. Therefore, the training process is a very important process to build an intelligent chatbot, however it spends a lot of time and money. The users are more familiar with humans than chatbots because they think humans understand better than chatbots or robots.Both qualitative and quantitative analysis method were used in the research. The techniques include secondary research, in-depth interview, and an online survey. Sampling selection was a convenience sampling method targeting those who have ever used chatbots, aged 20-45 years and live in Thailand. A qualitative approach was conducted at the beginning in order to understand users’ attitudes, get some insightful information and develop the questionnaire later on in a quantitative approach. In-depth interviews were conducted with 11 respondents, aged 28-44, and live in Thailand. The data from survey were collected by online surveys. A total of 281 responses were collected and used in analysis. The data from the online survey were analyzed in order to explore the specific character of chatbot users in Thailand, using Statistical Package for the Social Sciences (SPSS) for analysis. The respondents were asked 4 sets of attitude questions (independent variables) including: performance expectancy, effort expectancy, facilitating conditions, and technology anxiety to see the attitude that influence their satisfaction (dependent variable). The 4 sets of attitude questions were analyzed by using factor analysis to reduce the number of variables themselves and group similar characteristics together. As a result, there are 6 factors left from 4 sets of attitude questions, each factor was labeled with the name as following: 1.) Chatbot Performance, 2.) Ease & Speed of use, 3.) Effort, 4.) Facilitating, 5.) Tech Savvy, and 6.) Online Users. Lastly, all 6 factors were composite variables and added to the SPSS data set and then ran regression to find the correlation between the factors that influence user’s satisfaction. The user segments are grouped by age and find their behavior. Lastly, the users were asked about the service type that they agreed to use chatbots by comparing means. The key findings have indicated that factors that influence customer satisfaction are chatbot performance meaning that chatbots understand their messages, giving them the right answers and results that they want, understanding complex conversations, and responding to them anytime. They can save more time when using chatbot and chatbot performance is equal to humans. For the segmentations, the user that uses chatbots the most is the user age 40 years and above. They use chatbot more than 3 times/month while the lower age uses 2-3 times per month or 1 time per month. Regarding the type of service that users accept for using chatbots in this research, the users agree to use chatbots on the service that are simple cognitive/ analytical and simple emotional/social tasks which are “asking” general information about products, and asking for promotion. This research will be beneficial for the readers who are considering adopting chatbots in their business or who plan to improve chatbot performances in the future. The recommendations are training more data and keywords to the chatbot, integrated chatbots with human to handle the conversations, increase security level to chatbots to align with the regulations, and communicate chatbot benefits to the young generation.