<p>Personalized learning has received notable attention due to its effectiveness, and intelligent tutoring systems (ITS) serve as a representative example. A commercially available ITS, Assessment and Learning in Knowledge Spaces (ALEKS) has particularly gained popularity in K-12 and higher education. The purposes of this study are to explore students’ learning experience and satisfaction with ALEKS and to examine the influential factors of learning achievement in ALEKS. The participants were 100 Korean undergraduates enrolled in blended learning courses using ALEKS. We measured the participants’ perceived satisfaction with ALEKS, as well as their self-efficacy and resource management. Additionally, ALEKS log data were used to evaluate learning achievement. For data analysis, we investigated why students were satisfied with learning with ALEKS and the influence of learning time, prior knowledge, self-efficacy, and resource management (e.g., time and learning environment management and effort regulation) on ALEKS learning achievement using a multiple regression model. The participants were satisfied with ALEKS because of the personalized learning features, learning effectiveness, learning motivation, and flexibility. Prior knowledge and learning time were statistically significant to predict ALEKS learning achievement. Self-efficacy, resource management, and effort regulation accounted for about 76.0% of ALEKS learning achievement. Practical implications are that instructors should systematically assess students’ entry-level skills and provide an appropriate timeframe for learning. To enhance self-efficacy, instructors should provide vicarious experience (e.g., opportunities to compare learning progress with others on the dashboard menu) or social persuasion (e.g., encouraging comments on learning progress). A short orientation should also be given to teach applicable resource management and effort regulation strategies for studying with ALEKS.</p>