## 3 Method
### Write-Based (RASA-Based)  
The choice of RASA for the conversational framework was motivated by its open-source nature, flexibility, and comprehensive support for Arabic. The architecture of RASA, comprising RASA NLU for natural language understanding and RASA Core for managing conversation flows, is particularly adept at handling the complexities of Arabic, including dialectal variations and script nuances. This framework facilitated the development of a chatbot capable of engaging in meaningful Arabic conversations and addressing the specific needs of technical and vocational college inquiries. The key components of the RASA framework are as follows:  
* RASA NLU: processes and interprets user inputs in Arabic, accommodating linguistic complexities. * RASA Core: guides conversation flow using machine learning to provide contextually relevant responses. * Domain: defines the chatbot's scope, including intents, entities, and actions tailored to college-related queries. * Rules and Stories: establish conversational guidelines and example paths to train chatbots for diverse interactions. * Actions: both predefined text messages and custom actions allow for dynamic responses and functionalities such as API calls or database queries. * Data Collection  
The data collection process for the write-based approach followed the same methodology described in the \"Data Collection\" sub-section for the menu-based approach. The same sources, including existing college documentation, frequently asked questions (FAQs), and expert knowledge from college staff and administrators, were utilized to gather a diverse and comprehensive dataset. The collected data were preprocessed and prepared for training the write-based chatbot model, ensuring consistency and compatibility with the write-based approach. * Data Preprocessing:
* The collected data underwent preprocessing to prepare it for training the chatbot model. * Text normalization techniques were applied to standardize the text's format, including converting characters to lowercase and removing irrelevant characters or information. * Tokenization was performed to split the text into individual words or tokens, enabling accurate processing and analysis. * Arabic-specific challenges, such as discretization and morphological variations, were handled using specialized libraries and techniques to ensure proper processing of the Arabic language. * Choosing the RASA NLU Pipeline  
The fundamental role of RASA NLU is to interpret the data provided by the user to the chatbot, which includes identifying necessary intents and entities. Within the RASA Open Source, a series of components sequentially process incoming messages. This ordered execution of components, known as the processing pipeline, is established in the config.yml file [75]. The supervised embedding pipeline allows for training in any global language, as it is designed to learn from the ground up. This type of pipeline is similar to that of the Tayseer pipeline, as shown in Figure 5.  
The open-source RASA platform provides users with a powerful toolkit for building AI-driven agents that can chat, fine-tuned with a natural language processing pipeline. Figure 6 illustrates the agent training process executed in RASA. Within the scope of the Arabic language, RASA supports comprehensibility and conversational participation through a set of customizable components specified as follows. First, the preprocessing phase uses the **WhitespaceTokenizer** for simple text segmentation. This is complemented by the **RegexFeaturizer,** which extracts features based on the patterns. The **LexicalSyntacticFeaturizer** performs deep syntactic parsing to ease navigation through the complexities of Arabic syntax. It uses two **CountVectorsFeaturizer** components: the first to produce token-level analysis using the bag of words and the second to analyze the Arabic language's linguistic subtleties using character models ranging from 1- to 4-g.