## 1 Introduction  
Computer programs utilizing NLP have emerged as pivotal tools for simulating human-like conversations in the evolving landscape of technology, chatbots, and virtual assistants [1]. These AI-driven entities have found widespread application in automating customer support and services [1]. The integration of artificial intelligence (AI) across various computing and technological domains [2] notably includes the field of NLP [3]. This branch of AI facilitates human-computer interaction and communication through natural language, enabling chatbots to converse with humans or other bots via text or speech [4]. The utility of chatbots extends across diverse sectors, including e-commerce [5] and education [6; 7; 8; 9; 10], with notable applications in customer service [5; 11; 12] and hospital patient counseling [13; 14; 15]. Institutions increasingly rely on corporate websites and social media for student services in the educational domain by utilizing web-based response systems [4]. However, these methods are resource-intensive and time-consuming, leading to delays in response times [5]. This issue has been further compounded by the shift towards online education during the pandemic, intensifying the demand for efficient onlineassistance and round-the-clock service availability [16]. The growing reliance on intelligent agents, such as chatbots, in daily life makes their integration into social media and instant messaging ecosystems increasingly commonplace and rational [17]. Conversational agents have garnered considerable interest in the education sector because of their capacity to fulfill several educational functions, such as acting as instructors, coaches, and learning companions [18]. These agents possess the ability to analyze and react to human language. These have been incorporated into educational computer systems to facilitate dynamic learning interactions with students [19; 20]. Implementing AI-powered chatbots in educational settings can significantly reduce administrative burden and enhance the student experience, potentially boosting student retention rates and satisfaction [6]. This study proposes the Tayseer architecture to explore this field by utilizing advanced technological innovations such as NLP, dialogue systems, and sentiment analysis, all finely tuned for the Arabic language. This study introduces a dual-mode conversational architecture that synergizes NLP capabilities with different interaction methods (write-based, click-based, or both), specifically catering to technical students' needs. Employing the RASA framework, the system adeptly handles open-ended dialogues, whereas the menu option paradigm offers structured guidance for various tasks. This dual approach aims to provide flexible self-service channels that accommodate user preferences, regardless of whether they favor structured or unstructured interactions. Developing effective chatbots requires a solid understanding of deep learning (DL), a subset of ML within AI [17]. DL, which is often associated with deep artificial neural networks, involves sophisticated algorithms that model data through nonlinear function transformations across multiple layers [21]. Deep learning chatbots are primarily categorized as retrieval-based and generative bots. Retrieval-based bots leverage classification models to discern user intent and retrieve suitable responses from a database, offering greater flexibility than rule-based systems. In contrast, generative bots generate responses based on both current and previous user interactions, displaying more human-like characteristics but often struggling with grammatical accuracy and consistency [22; 23; 24]. Focusing on the RASA framework, a key component in chatbot development comprises natural language understanding (NLU) and dialogue management elements. RASA NLU enables customizable NLP through various prebuilt components, including the Dual Intent and Entity Transformer (DIET) [7].