College Enquiry Chatbot using Rasa Framework

Meshram, Siddhant; Naik, Namit; Vr, Megha; More, Tanmay; Kharche, Shubhangi

doi:10.1109/asiancon51346.2021.9544650

Cited by 22 publications

(9 citation statements)

References 3 publications

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“…Especially, there are various models to perform the intent classification task, such as K nearest neighbors, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, etc. Our proposed architecture adopts Neural network algorithm (NN) because of two main reasons claimed by [35] : (1) it needs less formal statistical training (2) it has a high capacity to detect complex nonlinear relationships between dependent and independent variables, in our case, independent variables refer to wh-questions and the intent class is the dependent variable. The first task is to vectorize whquestions by using word embedding techniques.…”

Section: ) Data Splittingmentioning

confidence: 99%

“…Hence, the learning strategy becomes dynamic, customized, and individualized to encourage learners. (2) Providing specific information about each learner"s progress, strengths, and weaknesses, and even attendance issues. Hence, data analysis on e-learning platforms enhances and improves learning experiences.…”

Section: Introductionmentioning

confidence: 99%

“…AI chatbots have been used in the e-learning domain to satisfy several needs such as (1) Promoting learners" interaction with online courses. (2) Providing information about administration [2], courses, and exam regulations (3) Sharing exercises and tips/hints to solve them and assist students to master the course knowledge (4) Answering repetitive and massive questions about the course knowledge (5) Recommending learning materials and educational resources according to the learner"s need (6) Assessing learners" knowledge by automating exams and assignments (Sreelakshmi et al, 2019) (5) Encourage collaborative learning between learners (El Azhari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, saving time, energy, and cost. (2) Capitalizing knowledge from several sources and storing them as the chatbot Knowledge Base (KB), Thus, assisting learners to quickly find the reliable information needed without wasting time searching in many information sources. (3) Encouraging learners to ask their questions (Moreno-Guerrero et al, 2023) without being afraid of attracting attention from others, being criticized, and having their opinions misinterpreted.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques

Azhari,

Hilal,

Daoudi

et al. 2023

IJACSA

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Intelligence chatbots have shown a growing interest in different domains including e-learning. They support learners by answering their repetitive and massive questions. In this paper, we develop a smart learning architecture for an inclusive chatbot handling both text and voice messages. Thus, disabled learners can easily use it. We automatically extract, preprocess, vectorize, and construct AskBot's Knowledge Base. The present work evaluates various vectorization techniques with similarity measures to answer learners' questions. The proposed architecture handles both Wh-Questions starting with Wh words and Non-Wh-Questions, beginning with unpredictable words. Regarding Wh-Questions, we develop a neural network model to classify intents. Our results show that the model's accuracy and the F1-Score are equal to 99,5%, and 97% respectively. With a similarity score of 0.6, our findings indicate that TF-IDF has performed well, correctly answering 90% of the tested Wh-Questions. Concerning No-Wh Questions, soft cosine measure, and fasttext successfully answered 72% of Non-Wh-Question.

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Section: ) Data Splittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques

Azhari,

Hilal,

Daoudi

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Reference [22] developed an AI-driven chatbot that allows students to enquire about college admission rules and policies. The chatbot is developed using the RASA framework.…”

Section: B Chatbots In Educationmentioning

confidence: 99%

Bilingual AI-Driven Chatbot for Academic Advising

Bilquise¹,

Ibrahim²,

Shaalan³

2022

IJACSA

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Conversational technologies are revolutionizing how organizations communicate with people, thereby raising quick responses and constant availability expectations. Students often have queries about the institutional and academic policies and procedures, academic progression, activities, and more in an academic environment. In reality, the student services team and the academic advisors are overwhelmed with several queries that they cannot provide instant responses to, resulting in dissatisfaction with services. Our study leverages Artificial Intelligence and Natural Language processing technologies to build a bilingual chatbot that interacts with students in the English and Arabic languages. The conversational agent is built in Python and designed for students to support advising-related queries. We use a purpose-built domain-specific corpus consisting of the common questions advisors receive from students and their responses as the chatbots knowledge base. The chatbot engine determines the user intent by processing the input and retrieves the most appropriate response that matches the intent with an accuracy of 80% in English and 75% in Arabic. We also evaluated the chatbot interface by conducting field experiments with students to test the accuracy of the chatbot with real-time input and test the application interface.

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A Systematic Review of Conversational AI Chatbots in Academic Advising

Assayed,

Alkhatib,

Shaalan

2024

Lecture Notes in Civil Engineering

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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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College Enquiry Chatbot using Rasa Framework

Cited by 22 publications

References 3 publications

An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques

An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques

Bilingual AI-Driven Chatbot for Academic Advising

A Systematic Review of Conversational AI Chatbots in Academic Advising

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