## 1 Introduction  
Artificial intelligence (AI) has transformed numerous industries. offering innovative, accessible, and sustainable solutions to longstanding challenges [1]. In education, AI is recognized for its potential to transform teaching and learning methodologies by enabling personalized instruction, automating routine tasks, and improving accessibility. Among AI-based tools, chatbots have emerged as a particularly valuable technology for providing real-time support, streamlining administrative processes, and enhancing user engagement [2, 3]. These systems are increasingly utilized in fields such as e-commerce, healthcare, finance, and education, where they are valued for their ability to deliver efficient, user-centered services [4, 5]. In educational contexts, chatbots have gained significant traction by facilitating access to academic resources, offering continuous information, and supporting administrative operations. For example, universities in North America and Europe have adopted chatbots to manage tasks such as answering admission queries, assisting with course registration, and enhancing student services [6; 7]. Despite their growing adoption, there is a need to rigorously evaluate chatbots in educational settings, particularly in terms of usability and UX. Usability, as defined by ISO 9241-11 [8], is a crucial dimension of user experience (UX) but does not encompass all aspects of it [9; 10]. Usability comprises effectiveness, efficiency, and satisfaction, focusing on how successfully and efficiently users achieve their goals. Effectiveness, a core component of usability, emphasizes task success and error reduction, which are essential for evaluating system performance. In contrast, UX includes broader dimensions, such as user satisfaction, engagement, and emotional responses, providing a more comprehensive evaluation of the overall user interaction with the system. This study presents a theoretical framework grounded in ISO 9241-11 [8] standards to evaluate educational chatbots systematically. The framework includes three core usability components: effectiveness (error reduction), efficiency (response times), and usability (CUQ scores, reflecting interaction quality and user perception) [11; 12; 13]. Analyzing these components separately offers a nuanced understanding of their individual contributions to overall usability and UX. To complement this focus on usability, cognitive load theory is also incorporated into the framework to ensure that chatbot interaction modes are designed to minimize cognitive demands. Menu-based systems employ hierarchical structuring to reduce working memory load, while hybrid modes balance user control with system guidance using progressive disclosure strategies, thereby promoting cognitive efficiency [14; 15]. User engagement theory further informs this framework by addressing both the technical and emotional dimensions of user interaction. This study employs the UES-SF to assess key dimensions of engagement, encompassing focused attention, perceived usability, aesthetic appeal, and reward. These dimensions align with [PERSON] engagement framework, which emphasizes the importance of emotional and perceptual factors in sustained user interaction [16]. Both the CUQ and UES-SF were chosen for their strong theoretical foundations and empirical validation in educational contexts. The CUQ has demonstrated high reliability (\(\alpha>0.80\)) and comprehensive coverage of usability dimensions, while the UES-SF is tailored to digital interactions and has undergone robust cross-cultural validation [17]. Chatbot interaction modes, including text-based, menu-based, and hybrid-based approaches, play a crucial role in shaping user engagement. Menu-based systems are effective for handling straightforward queries due to their structured navigation, while hybrid systems integrate text and menu options to meet diverse user needs [15; 18].