An open-source Dental Chatbot
Tutor system, trained on
academic books pertaining to
Dental Surgery using AI models.
The Chatbot asks short
questions and evaluates
responses from medical
students to assess their
academic level, while providing
hints to them as well in order to
help them connect concepts
and theories during attempts.
Lack of Physical Correspondence and Accessibility to educational stakeholders,
resources has rocketed due to the on-going pandemic,
Remote Learning is an exponentially-growing success story, where everyone
has a chance to learn and grow,
Social Constructs, Barriers no longer as massive an issue for potential students,
Theory-reliant practice – changes to underlying content on the occasional side,
Often free or lower-priced, in comparison to traditional education,
The medical field in general appears to be more reliant on traditional didactic
approaches when it comes to teaching,
….and many other benefits!
Why is this needed?
Leveraging Innovative Self-paced Learning in
Dental Education has become highly desired.
Virtualization of Education using TutorBots
trained on Dental Education content can
help bridge this gap.
A Conglomeration of Problems
Data Ingestion –
Flow and Integration
A high-grade replicating implementation such as the following is essentially needed:
An Intelligent TutorBot’s Reasoning
Help / Hint / Question
The same sample text utilized for the QA Generation task was fed to a Latent
Dirichlet Allocation (LDA) model implementation
LDA – a generative statistical method that allows sets of observations to be
explained in terms of groups which explain why some parts of the data are
similar. For example, if observations are words collected into documents, it
operates on the theory that:
1. Each document is a mixture of a small number of topics.
2. Each word's presence is attributable to one of the document's topics.
Topic Extraction Pipeline
Perform Lemmatization, Stemming and Stopword Pre-Processing
Split text to create corpora
Using a generated dictionary, each ‘document’ is converted into a BoW model
Based on BoW Histograms, LDA computes Dirichlet prior probability of each word with
class parameters reliant on document-wise and word-wise distributions
This computation allows weighting on each word occurrence relative to its count, thus
the frequently occurring words begin to appear as natural topic selections
Topic Extraction Pipeline
We address the “hint” as a semantic chunk in an input passage that will be
included (or rephrased) in the target question and the accompanied answer.
Based on this definition, we perform syntactic parsing and chunking on input text,
and select chunks which are the most relevant to the target question as the clue
Leverage two sub-systems for the task (inclusive of Word Embedding):
A: Lexical Simplification using WordNet, BabelNet and Rake reliant on Text
Summarization, Expansion, Keyword Similarity and Rephrasing
B: GPT-2-small model trained on Semantic Clues learned from SQuAD using
Hint Generation Pipeline
The following tables were maintained in order to fulfill each User - Bot paradigm:
User, Bot Interactions
Improvised Hint Generation,
Question Rating Generation,
Human Evaluation (Students, Dentists, Teachers),
Learning and Enabling Objectives,
Authentic Response Control – Instructor vs. TutorBot,
Extensive Deployment and Correlative Study,
Real-time Training and Testing,
in the Pipeline
….and many other possibilities!
A reliable platform advocating
and positively exploiting the uses
and benefits of virtual education
while capsizing the disadvantages
of physical education, that can
influence many other sub-and-
super systems to follow suit.
• Reddy, S., Chen, D., & Manning, C. D. (2019). Coqa: A conversational
question answering challenge. Transactions of the Association for
Computational Linguistics, 7, 249-266
• Mishra, A., & Jain, S. K. (2016). A survey on question answering
systems with classification. Journal of King Saud University-Computer
and Information Sciences, 28(3), 345-361
• Dialogue-based tutoring at scale: Design and Challenges
• Developing a Large-scale Feedback System to Train Dialogue-based
Tutors using Student Annotations
• Preliminary Evaluations of a Dialogue-Based Digital Tutor
• The Personality of AI Systems in Education: Experiences with the
Watson Tutor, a one-on-one virtual tutoring system
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