The purpose of this study was to find some of the predominant factors that determine the intention of students to use eLearning in the future. Since eLearning is not just a technology acceptance decision but also involves cognition, this study extended its search beyond the normal technology acceptance variables into variables that could affect the cognition of an individual due to his or her unique characteristics. The variables in the literature of technology acceptance studies can be classified broadly into 5 categories. They are Individual Differences, Beliefs, Attitude, Behavioral Intention, and Actual Behavior. Since the Technology Acceptance Model (TAM) is the most widely used model to study the acceptance of technology, this study adopted TAM and further extended it based on the recommendations from the literature of information systems and information technology. It can be said that eLearning is in the "Early Adopters" stage in the academic sector and at the "Early Majority" stage in the corporate sector of the innovation diffusion process. Individual differences are among the external variables said to influence the belief variables in TAM. The research intent is to understand what individual differences can be observed as the characteristics of early adopters of eLearning. Personality traits are one of the predominant measures of individual differences. This study employs the big five personality trait measures to measure the personality types of individuals engaged in eLearning and how they influence their decision to further engaging in eLearning. The variables of the research model are categorized into individual differences, beliefs and behavior. Apart from the TAM variables, i.e., Perceived Ease of Use (PEU) and Perceived Usefulness (PU), this study has incorporated Perceived Enjoyment (PE) which is a measure of intrinsic motivation and Subjective Norms (SN) into the beliefs variables category. This research was conducted among the master degree students of eLearning programs at a University in Thailand. There are 750 students who have completed or are currently enrolled in these programs at the university. The students comprise both Thai national and international students. A total of 249 usable questionnaires were collected. The respondents studied in eLearning master degree programs from a total of 26 different countries with majority of the respondents (83 percent) residing in Thailand. Most of the respondents (67 percent) had no prior experience with eLearning and the majority (64 percent) of respondents were females. Compared to the females, the males enjoyed eLearning more and expressed stronger intentions to use eLearning in the future. Respondents with prior eLearning experience had stronger intentions to use eLearning in the future than those without prior experience. Analysis of the data shows that the significant predictors of Behavioral Intention (BI) to engage in eLearning are PU, SN, and PEU. Perceived Enjoyment seems to have no significant effect on BI or beliefs regarding eLearning use. Only three out of the five personality trait variables turned out to be significant in the final model, i.e., Extraversion, Conscientiousness, and Neuroticism. It was found that the effects of individual differences on Behavioral Intention were mediated solely through the belief variables in the final model. Hence it could be concluded that individual differences form beliefs which in turn manifest as the Behavioral Intention of an individual to engage in eLearning. (Contains 16 tables and 3 figures.)