## 1 Introduction  
The disruption and development of artificial intelligence (AI) technology have rapidly transformed the educational system. AI is expected to transform education and revolutionize the teaching and learning process [1]. AI-driven tools such as chatbots and language models, including ChatGPT, are poised to enhance the educational experience [2]. The application of AI in higher education, particularly in promoting students' self-directed learning (SDL), has demonstrated considerable promise [3]. However, this increased adoption also poses challenges, particularly in maintaining students' motivation to continue effectively using these tools [4]. Although AI tools provide significant benefits for SDL, it has been observed that students often have difficulty in maintaining their continuance intentions in the long run. This suggests that while the initial acceptance of AI tools for learning is a critical step toward success, the continued use of technology and the continued intention are the real determinants of final success. In recent years, research on SDL has garnered extensive attention. Autonomy refers to the capability of managing one's own learning process [5]. [PERSON] [6] posits that factorsinfluencing SDL encompass metacognition, self-efficacy, learning strategies, goal setting, learning environments, and teachers. [PERSON] [7] argues that learners' autonomy is hindered by a lack of learning strategies, attitudes, motivation, and access to resources. Regarding the relationship between SDL and AI, [PERSON] et al. [3] aimed to delve into students' perceptions of how AI-driven applications, as learning aids, facilitate their autonomy in learning. [PERSON] et al. [8] examined how teacher support based on self-determination theory (SDT), along with student attributes such as gender and achievement levels, impact AI-based learning in secondary school settings. Furthermore, [PERSON] et al. [9], utilizing SDT as a theoretical framework, explored how teacher support moderates the influence of students' domain-specific knowledge on their need satisfaction and intrinsic motivation through information technology. While scholars have extensively studied the relationship between SDL and AI, encompassing specific factors like self-efficacy, satisfaction, and intrinsic motivation within SDL, the majority of the existing research focuses primarily on the direct role of AI in areas akin to learning assistance. There remains a dearth of comprehensive analyses on the full spectrum of factors influencing sustained intention and their configuration within this context. Firstly, previous studies have often centered on the prerequisites influencing students' initial acceptance of AI tools in SDL environments [10; 11]. However, the adoption of AI tools encompasses not merely an acceptance process but, more importantly, a process of sustained usage by students [12]. Consequently, this study aimed to delve into the factors driving students' willingness to continuously engage in SDL using AI tools, with a focus on the continuance intention. Secondly, there is a persistent lack of attention paid to intrinsic motivation when examining the intention for continued usage. For instance, [PERSON] argues [13] that the Technology Acceptance Model (TAM) proposed by [PERSON] overly emphasizes the influence of external motivation, neglecting the intrinsic motivations that shape technology acceptance. Furthermore, due to this oversight of intrinsic motivation, [PERSON] and [PERSON] [14] found that the Expectation Confirmation Model (ECM), originating from research on continued usage behavior in workplace information systems, struggles to explain the continued usage behavior of e-learning users. Therefore, it is imperative to further explore, within the framework of SDT, the intricate interplay between intrinsic and extrinsic motivational factors and their impact on the intention to continuously use AI tools.