Browsing by Author "Alkhatib, Manar"

Now showing 1 - 4 of 4
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    A Systematic Review of Conversational AI Chatbots in Academic Advising
    (SpringerLink, 2024) Assayed, Suha Khalil; Alkhatib, Manar; Shaalan, Khaled
    Purpose – This paper aims to review several studies published between 2018 to 2022 about advising chatbots in schools and universities as well as evaluating the state-of-the-art machine learning models that are deployed into these models. Methodology – This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), it demonstrated the main phases of the systematic review, it starts with screening 128 articles and then including 11 articles for systematic review which focused on the current services of the advising chatbots in schools and universities, as well the artificial models that are embedded into the chatbots. Findings – Two main dimensions with other sub-dimensions are extracted from the 11 included studies as it shows the following: 1- Advising chatbots AI Architecture which includes other sub-dimensions on identifying the deep learning based chatbots, hybrid chatbots and other open-resources for customizing chatbots; 2- The goals of the advising chatbot as it includes both the admission advising and academic advising. Conclusion – Most of studies shows that advising chatbots are developed for admission and academic advising. Few researchers who study the chatbots in high schools, there is a lack of studies in developing chatbots for students advising in high schools. Limitations and future work – This study is constrained to review the studies from 2018–2022, and it is not exposed to the chatbots artifacts, even though, the human-chatbot interaction has an essential impact on students’ experiences. Future research should include the impact of chatbots interactive design and students’ experiences.
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    Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries
    (SpringerLink, 2024) Assayed, Suha Khalil; Alkhatib, Manar; Shaalan, Khaled
    Purpose - This paper aims to develop a novel chatbot to improve student services in high school by transferring students’ enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students’ requests. Methodology - In this study we selected the data from high school students, since high school is one of the most essential stages in students’ lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings - Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work - This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users’ services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.
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    Neural Machine Translation for Arabic Language
    (The British University in Dubai (BUiD), 2019-07) Alkhatib, Manar
    Translating the Arabic Language into other languages engenders multiple linguistic problems, as no two languages can match, either in the meaning given to the conforming symbols or in the ways in which such symbols are arranged in phrases and sentences. Lexical, syntactic and semantic problems arise when translating the meaning of Arabic words into English. Machine translation (MT) into morphologically rich languages (MRL) poses many challenges, from handling a complex and rich vocabulary, to designing adequate MT metrics that take morphology into consideration. The task of recognizing and generating paraphrases is an essential component in many Arabic natural language processing (NLP) applications. A well-established machine translation approach for automatically extracting paraphrases, leverages bilingual corpora to find the equivalent meaning of phrases in a single language, is performed by "pivoting" over a shared translation in another language. Neural machine translation has recently become a viable alternative approach to the more widely-used statistical machine translation. In this thesis, we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based mainly on neural networks. The thesis we present also, highlights the key challenges for Arabic language translation into English, and Arabic. Experimental results across datasets confirm that neural paraphrases significantly outperform those obtained with statistical machine translation, and indicate high similarity correlation between our model and human translation, making our model attractive for real-world deployment.
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    Towards Gulf Emirati Dialect Corpus from Social Media
    (SpringerLink, 2024) AlAzzam, Bayan A.; Alkhatib, Manar; Shaalan, Khaled
    Purpose: This paper discusses the need for a corpus of Emirati traditional phrases and idioms in natural language processing (NLP) for the Gulf Emirati dialect and its potential applications in fields like voice recognition, machine translation, and sentiment analysis. Methodology: The researchers collected a corpus of more than 3000 traditional Emirati words and idioms by gathering data from several social media platforms, such as forums, YouTube, and Emirati radio stations. In addition, the researchers used the website scraping technologies to collect suitable resources, subsequently cleansing and organising the gathered material to ensure accuracy and consistency. A pilot investigation was undertaken, including an individual who is a native speaker of Emirati, in order to verify the precision of the dataset. Findings: The researchers successfully compiled a substantial dataset of traditional Emirati phrases and idioms, so enabling potential future investigations in the realm of Arabic dialects, specifically focusing on Gulf Arabic dialects such as the Emirati dialect. Implications: The compilation of Emirati traditional idioms and words presented in this study has potential practical effects in several domains such as medical, education, and business. These implications mostly revolve around enhancing communication among and with individuals proficient in the Emirati language. Originality/Value: This study distinguishes itself by concentrating on the compilation of an NLP corpus comprising traditional Emirati phrases and idioms, with a specific emphasis on the Gulf Emirati dialect. The dataset generated as a result of this effort may prove indispensable for further studies into Arabic dialects.