## 3 Method
### Write-Based (RASA-Based)
Intent and entity recognition are handled quite well by the **DIETClassifier,** which leverages transformer technology to produce iteratively updated context-sensitive predictions, as shown in Figure 7. Subsequent levels include the **EntitySynonymMapper**, which aligns terms of entities for overall consistency, and the **ResponseSelector**, which is adept at sourcing suitable replies to common queries. Figure 5: [PERSON] supervised embedding pipeline. Figure 6: Illustration of the agent training process executed in RASA. In instances where predictions may falter with low confidence, a **RulePolicy** steps in to maintain conversational coherence, and there is a unique assistant ID for every part of the pipeline to make it possible to trace any part. As indicated in Table 1, the RASA configuration is designed conscientiously, especially for Arabic conversational AI, with each unit serving a separate function. Designed with educational dialogues to be precisely tuned, the system is engineered to offer an unbeatable, knowledgeable virtual aid for technical and vocational students. In general, RASA's flexible pipeline combines state-of-the-art NLP methodologies that are sensitive to producing next-generation conversational AI abilities in Arabic. * The intent is specified in the \"nlu.yml\" and \"domain.yml\" files. The \"nlu.yml\" file will encompass the intent data that will be utilized for subsequent training purposes. Table 2 displays the various contexts and quantities of intent within each. For this research, we created 75 Arabic intents. Figure 9 illustrates the distribution of Tayseer's intents by context. * To manage diverse inquiries, a strategy is established in the \"domain.yml\" file under \"utter\", specifying conversational responses and their intents. The templates in this file address intent-specific queries. Tayseer encompasses more than 40 functions, with Table 3 providing samples from its diverse \"utter\" (user expressions or inputs). * The training data was annotated with the corresponding intents and entities using the RASA NLU framework, which provides a structured format for training the chatbot's natural language understanding component. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Context** & **Description** \\ \hline Greeting & Respond to various forms of greetings from users. \\ College admission and registration & Handle queries related to the college admissions and registration process. \\ Trainee services & Address queries or requests related to services provided to trainees. \\ Initiatives & Responding to user inquiries about initiatives for trainee career guidance and college goal achievement. \\ Activity participation and volunteering & Responding to user inquiries or interests in extracurricular activities and volunteering opportunities. \\ Guidance and counseling & Address user queries related to advice, support, or information on personal, educational, or career matters. \\ Rewards and financial aid & Respond to queries about financial support. \\ Global academies & Inquiries on Adobe, Cisco, and Microsoft academies’ programs and courses. \\ Complaints and suggestions & For handling user feedback and suggestions. \\ Access key site/system links & Assist users in finding and navigating to essential website or system links. \\ Faculty staff contact information & Assist users in obtaining faculty members’ contact details. \\ Trainee conduct regulations & Information on trainee behavior rules. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of intents. Figure 8: A set of example user expressions. * Dialogue Management:
* The dialogue management component of the chatbot was implemented using the RASA framework. * The conversation flow was defined by mapping intents to appropriate responses and actions using rules and stories. * Rules specify the triggering intents and the corresponding actions or responses to be executed.