## 5 Discussion
Quantitative measures, including CUQ and UES-SF scores, along with qualitative feedback, indicate that the hybrid-based mode is flexible in allowing students to alternate between text- and menu-based interactions, which positively impacts their user experience. This modality supports smoother task completion and lowers cognitive demand, which can be particularly beneficial for managing simple and complex queries. Given its adaptability, the hybrid-based mode emerges as an optimal interaction style in educational settings, where diverse query types are common, accommodating students' varying needs more efficiently than text-based or menu-based designs. However, while the hybrid-based mode showed high levels of engagement, its lower perceived usability scores suggest the potential for enhancing its intuitiveness, especially in simplifying the transitions between interaction types to minimize cognitive demands across various educational tasks. Our findings are consistent with those of previous studies showing that dual-mode interaction significantly enhances user satisfaction and engagement by accommodating diverse interaction preferences [33]. 2. Impact of Question Complexity on Performance and Chatbot Usability (RQ2)  
The analysis demonstrated that question complexity plays a crucial role in chatbot performance and usability across different interaction modes. Increased question complexity generally worsened the user experience across all modes by increasing response time and error rates, while decreasing satisfaction and engagement. Complex queries demand more cognitive resources, which impacts performance, as evidenced by the lower CUQ and UES-SF scores. However, the hybrid-based mode demonstrated resilience to the impacts of complexity, as students could shift between interaction types to navigate challenging questions. This flexibility likely helps maintain the user experience by offering a workaround for high-complexity demands, highlighting hybrid-based interaction as a potentially robust solution for managing diverse query types in educational contexts. This variation in the perceived usability scores across question complexities suggests refining usability to accommodate more straightforward interactions. This may further support user satisfaction and ensure a seamless educational query-level experience. Our findings are consistent with those of previous research, which indicated that users exhibited more engagement when inquiries were presented straightforwardly [41]. 3. Suitability of Interaction Modes for Varying Levels of Question Complexity (RQ3)  
Our study revealed significant variations in the performance of different interaction modes across question complexity levels. These data indicate that text-based interactions are better suited for handling complex queries. This allows students to provide nuanced responses and enhances their accuracy and satisfaction. For straightforward questions,the menu-based mode supports faster task completion and ease of use, although it can become limited when students attempt to expand or clarify their responses. Therefore, the hybrid-based mode offers a balanced solution across complexity levels by integrating the strengths of both text-based and menu-based interactions, underscoring its utility for various question types in educational chatbots. Regardless of question complexity, our proposed chatbot evaluation is consistent with previous research that showed average prescribed standards of task completion, with an average below 1.6 s [64]. * Criteria for Guiding Future Comparisons Among Educational Chatbots (RQ4)  
This study developed a comprehensive framework to evaluate educational chatbots. The proposed framework, grounded in the educational setting, aligns well with state-of-the-art models, such as the BUS-15, and complements user-centered experience metrics commonly used in chatbot assessment [2; 16].