Conversational AI tools have been rapidly adopted by students and are becoming part of their learning routines. To understand what drives this adoption, we draw on the Technology Acceptance Model (TAM) and examine how perceived usefulness and perceived ease of use relate to students' behavioral intention to use conversational AI that generates responses for learning tasks. We extend TAM by incorporating trust, perceived enjoyment, and subjective norms as additional factors that capture social and affective influences and uncertainty around AI outputs. Using partial least squares structural equation modeling, we find perceived usefulness remains the strongest predictor of students' intention to use conversational AI. However, perceived ease of use does not exert a significant direct effect on behavioral intention once other factors are considered, operating instead indirectly through perceived usefulness. Trust and subjective norms significantly influence perceptions of usefulness, while perceived enjoyment exerts both a direct and indirect effect on usage intentions. These findings suggest that adoption decisions for conversational AI systems are influenced less by effort-related considerations and more by confidence in system outputs, affective engagement, and social context. Future research is needed to further examine how these acceptance relationships generalize across different conversational systems and usage contexts.

翻译：对话式人工智能工具已被学生迅速采纳，并正成为其学习常规的一部分。为理解驱动这种采纳的因素，我们借鉴技术接受模型，考察感知有用性和感知易用性如何与学生使用为学习任务生成回复的对话式人工智能的行为意向相关联。我们通过引入信任、感知愉悦和主观规范作为额外因素来扩展技术接受模型，这些因素捕捉了社会与情感影响以及围绕AI输出的不确定性。使用偏最小二乘结构方程模型分析，我们发现感知有用性仍是预测学生使用对话式人工智能意向的最强因素。然而，当考虑其他因素后，感知易用性对行为意向并未产生显著的直接影响，而是通过感知有用性产生间接作用。信任和主观规范显著影响有用性感知，而感知愉悦对使用意向同时产生直接和间接影响。这些发现表明，对话式人工智能系统的采纳决策较少受努力相关考量影响，更多受系统输出可信度、情感参与和社会情境的影响。未来研究需进一步探讨这些接受关系在不同对话系统和使用情境中的普适性。