Development of An Arabic Conversational Intelligent Tutoring System for Education of Children with ASD

This paper presents a novel Arabic Conversational Intelligent Tutoring System (CITS) that adapts the learning styles VAK for autistic children to enhance their learning. The proposed CITS architecture uses a combination of Arabic Pattern Matching and Arabic Short Text Similarity to extract the responses from the resources. The new Arabic CITS, known as LANA, is aimed at children with autism (10 to 16 years old) who have reached a basic competency with the mechanics of Arabic writing. This paper describes the architecture of LANA and its components. The experimental methodology is explained in order to conduct a pilot study in future

Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)

The book is a timely report on advanced methods and applications of computational intelligence systems

SEEKER: A Conversational Agent as a Natural Language Interface to a relational Database

Managers of companies are typically not SQL (Structured Query Language) experts but require information 24/7. Therefore, a growing need for Natural Language Interfaces to Databases (NLIDs) has been identified, with a vast amount of research being undertaken in the area. The existing approaches to NLIDs present many weaknesses including the inability to deal with grammatical mistakes in user input, the inability to communicate with the user to correct mistakes and the inability to allow refinement of query results. This paper proposes a system, SEEKER, which uses a Conversational Agent (CA) as the Natural Language Interface (NLI) in a NLID. The CA is used to capture key words in the user's utterance. Once these key words have been identified, the most appropriate SQL template is selected by the expert system using rule based reasoning. The identified variables are mapped to the SQL template in order to create an SQL query. SEEKER allows for refinement of query results. SEEKER was evaluated in terms of user satisfaction and task completion. The results of the evaluation were promising

Adaptive tutoring in an intelligent conversational agent system

This paper describes an adaptive online conversational intelligent tu-toring system (CITS) called Oscar that delivers a personalised natural language tutorial. During the tutoring conversation, Oscar CITS dynamically predicts and adapts to a student’s learning style. Oscar CITS aims to mimic a human tutor by using knowledge of learning styles to adapt its tutoring style and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic and boost confidence. An initial study into the adaptation to learn-ing styles is reported which produced encouraging results and positive test score improvements. The results show that students experiencing a tutorial adapted to suit their learning styles performed significantly better than those experiencing an unsuited tutorial

Development of an Arabic conversational intelligent tutoring system for education of children with autism spectrum disorder

Children with Autism Spectrum Disorder (ASD) are affected in different degrees in terms of their level of intellectual ability. Some people with Asperger syndrome or high functioning autism are very intelligent academically but they still have difficulties in social and communication skills. In recent years, many of these pupils are taught within mainstream schools. However, the process of facilitating their learning and participation remains a complex and poorly understood area of education. Although many teachers in mainstream schools are firmly committed to the principles of inclusive education, they do not feel that they have the necessary training and support to provide adequately for pupils with ASD. One solution for this problem is to use a virtual tutor to supplement the education of pupils with ASD in mainstream schools. This thesis describes research to develop a Novel Arabic Conversational Intelligent Tutoring System (CITS), called LANA, for children with ASD, which delivers topics related to the science subject by engaging with the user in Arabic language. The Visual, Auditory, and Kinaesthetic (VAK) learning style model is used in LANA to adapt to the children’s learning style by personalising the tutoring session. Development of an Arabic Conversational Agent has many challenges. Part of the challenge in building such a system is the requirement to deal with the grammatical features and the morphological nature of the Arabic language. The proposed novel architecture for LANA uses both pattern matching (PM) and a new Arabic short text similarity (STS) measure to extract facts from user’s responses to match rules in scripted conversation in a particular domain (Science). In this research, two prototypes of an Arabic CITS were developed (LANA-I) and (LANA-II). LANA-I was developed and evaluated with 24 neurotypical children to evaluate the effectiveness and robustness of the system engine. LANA-II was developed to enhance LANA-I by addressing spelling mistakes and words variation with prefix and suffix. Also in LANA-II, TEACCH method was added to the user interface to adapt the tutorial environment to the autistic students learning, and the knowledge base was expanded by adding a new tutorial. An evaluation methodology and experiment were designed to evaluate the enhanced components of LANA-II architecture. The results illustrated a statistically significant impact on the effectiveness of LANA-II engine when compared to LANA-I. In addition, the results indicated a statistically significant improvement on the autistic students learning gain with adapting to their learning styles indicating that LANA-II can be adapted to autistic children’s learning styles and enhance their learning

Aneesah: a novel methodology and algorithms for sustained dialogues and query refinement in natural language interfaces to databases

This thesis presents the research undertaken to develop a novel approach towards the development of a text-based Conversational Natural Language Interface to Databases, known as ANEESAH. Natural Language Interfaces to Databases (NLIDBs) are computer applications, which replace the requirement for an end user to commission a skilled programmer to query a database by using natural language. The aim of the proposed research is to investigate the use of a Natural Language Interface to Database (NLIDB) capable of conversing with users to automate the query formulation process for database information retrieval. Historical challenges and limitations have prevented the wider use of NLIDB applications in real-life environments. The challenges relevant to the scope of proposed research include the absence of flexible conversation between NLIDB applications and users, automated database query building from multiple dialogues and flexibility to sustain dialogues for information refinement. The areas of research explored include; NLIDBs, conversational agents (CAs), natural language processing (NLP) techniques, artificial intelligence (AI), knowledge engineering, and relational databases. Current NLIDBs do not have conversational abilities to sustain dialogues, especially with regards to information required for dynamic query formulation. A novel approach, ANEESAH is introduced to deal with these challenges. ANEESAH was developed to allow users to communicate using natural language to retrieve information from a relational database. ANEESAH can interact with the users conversationally and sustain dialogues to automate the query formulation and information refinement process. The research and development of ANEESAH steered the engineering of several novel NLIDB components such as a CA implemented NLIDB framework, a rule-based CA that combines pattern matching and sentence similarity techniques, algorithms to engage users in conversation and support sustained dialogues for information refinement. Additional components of the proposed framework include a novel SQL query engine for the dynamic formulation of queries to extract database information and perform querying the query operations to support the information refinement. Furthermore, a generic evaluation methodology combining subjective and objective measures was introduced to evaluate the implemented conversational NLIDB framework. Empirical end user evaluation was also used to validate the components of the implemented framework. The evaluation results demonstrated ANEESAH produced the desired database information for users over a set of test scenarios. The evaluation results also revealed that the proposed framework components can overcome the challenges of sustaining dialogues, information refinement and querying the query operations

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance

Arabic conversational agent for modern Islamic education

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), Arabic conversational agents (CA) and learning theories by constructing a novel Arabic conversational intelligent tutoring system (CITS) called Abdullah. Abdullah CITS is a software program intended to deliver a tutorial to students aged between 10 and 12 years old, that covers the essential topics in Islam using natural language. The CITS aims to mimic a human Arabic tutor by engaging the students in dialogue using Modern standard Arabic language (MSA), whilst also allowing conversation and discussion in classical Arabic language (CAL). Developing a CITS for the Arabic language faces many challenges due to the complexity of the morphological system, non-standardization of the written text, ambiguity, and lack of resources. However, the main challenge for the developed Arabic CITS is how the user utterances are recognized and responded to by the CA, as well as how the domain is scripted and maintained. This research presents a novel Arabic CA and accompanying a scripting language that use a form of pattern matching, to handle users’ conversations when the user converse in MSA. A short text similarity measure is used within Abdullah CITS to extract the responses from CAL resources such as the Quran, Hadith, and Tafsir if there are no matching patterns with the Arabic conversation agent’s scripts. Abdullah CITS is able to capture the user’s level of knowledge and adapt the tutoring session and tutoring style to suit that particular learner’s level of knowledge. This is achieved through the inclusion of several learning theories and methods such as Gagne’s learning theory, Piaget learning theory, and storytelling method. These learning theories and methods implemented within Abdullah’s CITS architecture, are applied to personalise a tutorial to an individual learner. This research presents the first Arabic CITS, which utilises established learning typically employed in a classroom environment. The system was evaluated through end user testing with the target age group in schools both in Jordan and in the UK. Empirical experimentation has produced some positive results, indicating that Abdullah CITS is gauging the individual learner’s knowledge level and adapting the tutoring session to ensure learning gain is achieved