## 5 Discussion  
This study examined how different chatbot interaction modes impact students' chatbot usability, engagement, efficiency, and effectiveness in Arabic education. We compared these modes within varying levels of question complexity, where the findings provide evidence of optimal configurations for educational chatbots. This study's findings suggest that the hybrid-based mode is a flexible, user-friendly solution that effectively manages diverse educational queries, which could enhance productivity [3]. The findings suggest four core evaluation criteria for future comparisons across educational chatbots in academic settings: usability (CUQ scores), engagement (UES-SF scores), efficiency (response time), and effectiveness (error rate). Notably, the variation in [PERSON]'s alpha values between the UES-SF and CUQ measures reflects the different natures of engagement and usability (i.e., satisfaction). Statistically, the Bonferroni-corrected pairwise comparisons show that the hybrid-based mode consistently outperformed the text- and menu-based modes, particularly in high-complexity tasks, by enhancing engagement, usability, and response accuracy. Therefore, the statistical findings reveal that the hybrid-based mode is adaptable, practical, sustainable, engaging, and efficient in educational chatbot interactions in Arabic-language settings. Our proposed chatbot evaluation framework is a foundation for consistent, objective benchmarking across various chatbot implementations. This framework benchmarks future chatbot designs in educational contexts. The comparison of the chatbot's quality differs due to different aspects discussed in the literature, such as understanding the user, evaluating chatbot utterances, performance and efficiency, task success, usability, and user experience [78]. Moreover, a holistic evaluation might not be the best approach, as it might depend on the chatbot's scope, objectives, and domain. For example, the literature reports 108 constructs measuring chatbot quality; however, the proposed metrics align with the two primary dimensions: intrinsic evaluation, and system in context [78]. Specifically, the proposed framework provides a comprehensive and focused approach to chatbot evaluation with state-of-the-art evaluation models such as the Bot Usability Scale (BUS). Each criterion in our framework is designed to refine and expand on core elements from these models, specifically tailoring them to the needs of educational contexts. Efficiency, for example, aligns with the BUS-15's emphasis on response time but integrates an accuracy component essential for educational scenarios, where timely and precise responses  
Figure 6: The overall relationships between the proposed chatbot metrics. support learning outcomes [51]. Effectiveness was similarly adapted from the BUS-15's task completion metric, emphasizing accuracy in response content and underscoring the importance of reliable information in academic contexts. For engagement and usability, the framework incorporates user-centered measures from the BUS-15 and chatbot user experience models [2], emphasizing clarity, ease of use, and sustained engagement as critical to maintaining student motivation. Thus, this framework positions itself as a targeted, rigorous extension of these established models, ensuring consistent, relevant benchmarking while directly addressing the specific demands of educational chatbot applications. 1. Influence of Interaction Modes on Student Experience (RQ1)  
This study's findings reveal significant insights into how different interaction modes impact the student experience with educational chatbots. This highlights the significance of interaction modes in shaping a holistic user experience, encompassing usability, engagement, efficiency, and effectiveness, which aligns with this study's aim of establishing a comprehensive evaluation framework for educational chatbots.