Internal and External Factors Shaping Motivation in AI-Based Language Education

The rapid growth of AI has led to the integration of AI-powered educational tools, such as intelligent tutors and chatbots, enhancing student engagement through personalized learning experiences. These tools automate tasks like grading and feedback, adapt content to individual needs, and improve both motivation and academic performance. By addressing learning motivations, AI-based applications promote sustainable learning outcomes. Personal factors, such as arousal, beliefs, goals, and needs, significantly shape motivation, influencing both internal and external drivers. In foreign language learning, AI tools provide personalized feedback, interactive content, and real-time conversation opportunities to improve language acquisition, while AI-supported role-play exercises enhance fluency and pronunciation. Despite progress in AI-based education, a gap remains in understanding how various factors influence motivation within these tools. Existing literature lacks exploration of how factors like arousal, beliefs, goals, and needs shape motivation, particularly in AI-driven learning applications. This study aims to fill this gap by examining the internal and external factors that influence users' learning motivations in AI-based apps. Semi-structured interviews with 29 users were conducted, and data were analyzed descriptively, with key findings presented in tables and significant statements highlighted. The research results showed that both internal and external factors significantly influenced motivation. Participants were driven by the desire to improve their language skills, and the AI apps’ personalized feedback and features like role-playing supported their motivation. Clear goals, the freedom to progress at their own pace, and a sense of competence were key motivators, while autonomy, competence, and relatedness were reinforced by the apps’ positive feedback and tailored content