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 Chinese Traditional Medical Journal 

 

An Intelligent Tutoring System for Automata Theory: A 

Proposed Framework 

Luo Da, Den Hui, Zhang Youhu 

Department of Graduate School, Beijing University of Chinese Medicine, Beijing 

, Department of Ophthalmology, China-Japan Friendship Hospital, Beijing 

 

 

 

 

 

 

 

Introduction 

An concept from distant education that 

allowed students to study at their own 

speed and in any location came up with the 

notion of giving instruction online, which 

was a result of research in education. 

Education and Artificial Intelligence have 

been bolstered and advanced by scholars 

who have worked in both fields. 

Traditional computer-aided teaching 

systems have developed into intelligent 

tutoring systems (CAI). Intelligent tutoring 

systems (ITS) were originally known as 

Intelligent Computer Assisted Instruction 

(ICAI) when CAI was designed to act 

intelligently (ITS). [11] 

Computational modules on cognitive 

sciences, cognitive learning, computational 

linguistics and artificial intelligence, as 

well as mathematics and regular language, 

are used in computer-based learning 

environments to construct intelligent 

systems that are well-specified 

computationally. [1] 

Following are some of the original ITS 

needs outlined by researchers in [21]: 

 

(a) A working knowledge of the field 

(Expert model) 

 

learner self-awareness, or b) (Student 

model) 

 

b) An understanding of instructional 

methods (Tutor model) 

 

Although the overall structure has 

remained the same, a new module has been 

introduced. 

 

Abstract: Humans are being displaced in almost every aspect of their existence on 

this planet by computers and the advances they have made in technology. Since the 

discovery of Artificial computers that function on the same principle as the human 

brain and learn with changes in experience over time, the concept of intelligent 

computers attempting to emulate the human brain has developed. Learners benefit 

from intelligent tutors since it is hard for a human tutor to focus on every student in 

a classroom setting. As a result of a review of current intelligent tutoring systems 

for undergraduate courses in Automata Theory for computer science students, this 

article presents an improved approach. 

Keyword: FLAT, Automata Theory, and Intelligent Tutoring System. 

 

 

 

 

 
 



 

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Intelligent tutoring systems currently rely 

on the four-model design, which maintains 

the previous three models while adding a 

fourth. 

 

The following are four examples of each 

design type: 

 

(a) Database of knowledge 

 

a) Student's Representation 

 

b) A model for teaching 

 

Interface (d) 

 

The knowledge base is identical to the 

preceding designs' domain model. 

Consolidation of declarative, procedural, 

and metacognitive information is the goal 

of a tutor or coach. 

 

This is an internal model that describes the 

cognitive processes, metacognitive 

methods, and psychological traits of each 

learner. 

A comparable module exists in the other 

architectures as a pedagogical model for 

this one. It selects an efficient route 

through its knowledge representation 

based on a model of the learner's current 

understanding to produce expert behaviour 

by the learner. 

This module combines three categories of 

information: knowledge about the patterns 

of interpretations and actions inside 

conversations; domain knowledge required 

for presenting material, and knowledge 

needed for conveying intent. 

Student knowledge is 'actively constructed' 

rather than passively learned from 

textbooks and lectures, according to this 

theory. In this way, each student will 

create their own unique form of knowledge 

since the construction builds recursively 

on the learner's prior knowledge (facts, 

thoughts, and beliefs). Constructivist 

teaching methods are expected to be more 

effective than conventional methods 

because they openly address the 

unavoidable process of knowledge 

production. [24] 

 

Computer Science teaching is challenging 

to use constructivist theory because of 

these reasons. 

 

• Because sensory data from class must be 

integrated into a student's existing 

framework that is too superficial, CS 

concepts are constructed using 

constructivist theory. 

 

There is a lot of frustration and the idea 

that computer science is difficult since 

models have to be built from scratch. 

 

• Success in academic computer science 

courses is not always connected with 

autodidactic programming expertise. These 

pupils, like physics students, undoubtedly 

arrive to school with preconceived notions 

that aren't going to hold up under the 

scrutiny of a classroom environment. 

 

• Students who prefer a more introspective 

or social learning approach may be 

discouraged by the actual feedback they 

get while working on a computer. [24] 

 

FLAT is included in most Computer 

Science curricula because it provides 

students with an understanding of the 

mathematical foundations of computing, as 

well as its power and limits, as well as 

how to use it. the numbers 20, 10, and 19, 

respectively. 

 

Theoretical computer science, formal 

languages and automata theory (FLAT), 

and other similar terms are taught in many 

computer science curricula. Designing 

Finite Automata (FAs), Push-Down 

Automata (PDA) and Turing machines to 

identify languages of the appropriate class 

is a common requirement in these courses. 

 



 

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It's common for students to struggle with 

these FLAT ideas since they're so abstract 

and difficult to grasp (as there is a 

perception that the field is not only dated, 

but that it has little current applicability in 

the real world). [4] In order of appearance: 

[7] [28] [30]... [34] As a result, students 

may get demotivated and disappointed, 

and they may not remember much of what 

they've studied. It has been shown that a 

lack of problem-solving abilities is a key 

contributor to the challenges connected 

with the development of FAs. The logic 

mistakes that were shown to be common 

may be traced back to a lack of problem-

solving skills. It is possible that pupils are 

relying on "plug and chug" procedures 

without a clear knowledge of how they 

work. Many teaching methods have been 

developed in response to these issues, as 

well as to aid in the development of 

stronger mental models of FAs [5]. [38] 

 

• Active learning and constructivist 

teaching strategies: Students require quick 

feedback at each level of the process of 

developing FAs to better improve their 

problem-solving abilities (something that 

is very difficult to implement in a distance 

learning environment) 

 

By offering an alternate perspective and 

letting students to interact with the ideas 

by testing them on multiple input strings 

with quick feedback, visualisation tools 

aid students in grasping FAs and how they 

function. Such methods are based on the 

idea that students can make abstract 

models more tangible when they are able 

to engage with them. [9] 

Link to current computer science 

applications: This might include 

leveraging programming expertise as 

incentive for FA investigation and usage of 

its application to real-world issues. • Link 

to current computer science applications. 

 

These systems give students with 

personalised guidance, adjusting the path 

through the needed content depending on 

the student's progress. 

Students put off taking the course until 

their final semesters of an undergraduate 

course because they have only rudimentary 

or no prior understanding of computer 

science theory and arithmetic. The pupils 

become bogged down in the language if 

the educational content isn't motivating 

enough [14]. The issue here is that 

students may resort to rote learning if they 

do not grasp the topics [37]. 

A variety of hardware and software 

applications benefit greatly from automata 

theory's insights. Because they are 

abstract, these topics are often taught using 

a conventional lecture format, which 

works well for students who want to think 

things out for themselves. 

Computer Engineering students, on the 

other hand, exhibit a significant 

predilection for active and sensory 

learning. 

RELATED WORK: 

Formal Languages and Automata Theory 

have been supported by a few famous 

instances of e-learning systems. 

 

Since it was launched in 1998, FLUTE 

(Formal Languages and Automata 

Education) [42] has been educating 

students about automata theory via 

examples and dependency graphs 

connecting related subjects. However, this 

system is out of date and is no longer in 

service. 

 

For automata theory, a smartphone-based 

multimedia learning system was created. 

Simulating an automaton, reading course 

notes, seeing presentations, taking a quiz 

and even having a help section that acts as 

a handbook are just some of the features 

available. 

 

The difficulties of online education and the 

static acquisition of information have been 

addressed in certain ways in [41] [32] [2] 

[13]. They have been conceived and 



 

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developed so that students may learn via 

exploration, rather than relying just on 

textbooks. This kind of method is often 

used in learning activities where students 

answer practical issues, so that the 

information acquisition involves the 

completion of an acceptable number of 

exercises.. 

 

As a result of these systems, students are 

given exercises from a repository, given 

the opportunity to contribute their own 

answers, given comments on their work, 

and even given the opportunity to create 

new challenges depending on their 

progress. There is no way for students to 

create their own exercises using these 

systems. They are limited to working with 

a collection of activities that are already in 

a repository. [18] 

 

Motivated teachers seek for or develop 

visual aids to aid their pupils in their 

learning processes. As long as kids are 

interested in what they are learning, they 

will find a way to succeed. Animations 

have been suggested for the DFA, NFA 

and Turing machines. In addition, the 

recommended remedy has not been 

implemented, and no explanation is 

offered as to how to create these 

storyboards 

 

An introduction to FSMs is provided by 

these first two components. In one 

component, learners are given a brief 

movie-style introduction to the subject 

matter. In addition, there is a more in-

depth hypertext explanation of the 

fundamental principles. Students may use 

this component to get a general 

understanding of the material. Finite 

automation and a Turing machine 

simulator are included in the course, which 

also includes two games for students to 

apply what they've learned. [25] 

 

It is composed of several components, 

including an animated (movie-like) 

welcome component, a hypertext 

introduction to computation theory topics, 

a finite state machine (FSM) simulator, a 

Turing machine (TM) simulator, a self-

assessment component for online 

collaborative learning, and three other 

components showing visual examples of 

automata, such as video, of Hamada's e-

learning system for automata and the 

theory of computation These detailed 

resources are tough for novice automata 

students to understand since they were 

developed to accommodate a wide range 

of learning styles. Students are stumped as 

to where to begin their studies. [27] 

 

To make the learning experience more fun, 

[27] adds an additional component that 

helps the student discover their preferred 

method of learning. 

In the same way as other IT systems, 

SELFA-Pro is made of a number of 

modules that work together to achieve the 

overall system goal. The FLAT problem 

solver module, the linker module, and the 

interface module make up the proposed 

system. Each module has been 

meticulously crafted to do a particular set 

of activities using just the data that is 

required. It is the goal of the issue solver 

module to take the student's exercise 

(declarative knowledge) and assess the 

problem or circumstance needs before 

using the knowledge to solve the problem 

(i.e. execute procedural operations). The 

linker module will provide advice on how 

to repair an issue after it has identified one 

(i.e. attempts to place conditional 

knowledge into specific execution). In 

accordance with the linker module's 

recommendations, the interface module 

gives students mechanisms for sending 

exercises and for creating the most suitable 

view of system outputs. [18] 

PROBLEM IDENTIFICATION: 

It is assumed that the student has a basic 

understanding of formal languages and 

automata theory in order to benefit from 

the teaching aids currently available. Since 



 

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no mechanism has been created to help a 

pupil who does not comprehend the 

essential ideas. 

Simulators and emulation seem to be the 

offered answers in the current systems, 

which aid students in putting the ideas and 

information they've acquired to use. There 

isn't a single system that makes it easier 

for beginners to learn about automata 

theory topics. 

Mathematical foundations of computer 

science are assumed to have been taught in 

the past, but students tend to forget these 

concepts or rely on rote learning to get a 

passing grade, which prevents them from 

progressing to a higher level course or 

semester in the traditional classroom. 

 

Assuming that all students have the same 

level of comprehension and that a teacher 

is unable to spend enough time with each 

one, most E-learning systems for this 

purpose use recorded lectures, scanned 

textbooks as study materials, and scanned 

handwritten notes, which are considered to 

be standard for the acquisition of 

knowledge in the traditional classroom-

based scenario. 

 

For the most part, ITS built in computer 

science focus on offering individualised 

answers to issue questions relevant to 

information learned by students, rather 

than on the learning module itself. For 

many students, however, this isn't a viable 

option since they are frustrated by the fact 

that they must go back to their textbooks 

or notes for notes and study material while 

using the system the conventional way. 

PROPOSED SOLUTION: 

An ITS for formal languages and automata 

theory may be developed by using the four 

current model ITS designs [16] [wing 

components: 

For further information, see the17] [39], 

which are endorsed by numerous scholars 

[16] [17] [39]. It is composed of the follo 

following resources: 

 

Theoretical framework for instruction 

 

• The User Interface 

 

 

Fig: Proposed Solution of ITS for 

Automata Theory 

 

Knowledge base: The ITS for automata 

theory will include a knowledge base made 

up of learning objects that may be mapped 

to the preferences and requirements of 

learners based on their many intelligences. 

When creating the parts of the knowledge 

base, attention must be made since it 

contains domain knowledge, restrictions, 

rules, and difficulties linked to the topic. 

When the learner model is modified, the 

knowledge base should be updated 

accordingly. 

 

The various needs, preferences and 

strengths of the eight Intelligences can be 

seen below which is taken from [12] 

 
 

Intelligence Area 

 
 

Strengths 

 
 

Preferences 

 
 

Needs 

 

 
Verbal / 

Linguistic 

 

 
Writing, reading, memorizing dates, 

thinking in words, telling stories 

 
 

Write, read, tell stories, talk, 

memorize, work at solving 

puzzles 

 
 

Books, tapes, paper diaries, writing 

tools, dialogue, discussion, debated, 

stories, etc. 

 
 

Mathematical/ 

Logical 

 
 

Math, logic, problem-solving, 
reasoning, patterns 

 
Question, work with 
numbers, experiment, solve 

problems 

 
 

Things to think about and explore, 
science materials, manipulative, 



 

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Visual / Spatial 

 
 

Maps, reading charts, drawing, mazes, 
puzzles, imagining things, visualization 

 
 

Draw, build, design, create, 
daydream, look at pictures 

 
video, movies, slides, art, imagination 
games, mazes, puzzles, illustrated 

book 

 
 

Bodily / 

Kinesthetic 

 
 

Athletics, dancing, crafts, using tools, 

acting 

 
 

Move around, touch and talk, 

body language 

 
 

Things to build, movement, tactile 

experiences, hands-on learning, etc. 

 

 

Musical 

 
 

Picking up sounds, remembering 
melodies, rhythms, singing 

 
 

Sing, play an instrument, 
listen to music, hum 

 
 

Sing-along time, musical instruments, 
etc. 

 

 

 

Interpersonal 

 
 

Leading, organizing, understanding 
people, communicating, resolving 

conflicts, selling 

 

 
Talk to people, have friends, 
join groups 

 

 

 

group games, 

 

 

Intrapersonal 

 
 

Recognizing strengths and weaknesses, 

setting goals, understanding self 

 
 

Work alone, reflect pursue 

interests 

 

 
self-paced projects, choices 

 

 

Naturalistic 

 
 

Understanding nature, making 

distinctions, identifying flora and fauna 

 
 

Be involved with nature, 

make distinctions 

 
 

Order, same/different, connections to 

real life and science issues, patterns 

Table: Strengths, preferences and needs of the Eight Intelligences 

 

Many of these intelligences can still be 

used in automata theory education, even if 

they can't be applied to all of them. 

Identifying learning objects for each form 

of intelligence in automata theory ideas 

would be a future project in this field. 

Student Model: 

The student model is the internal model 

that will keep track of the following 

information about the learner: 

• The current degree of understanding of 

the subject by asking questions regarding 

the subject's preparatory themes. 

Assuming a clean slate would be a far cry 

from the current state of affairs, though. 

 

A questionnaire based on Gardner's theory 

of multiple intelligence [15] is used to 

determine a person's intelligence type, 

which may then be used to design learning 

routes appropriately. 

 

In order for the knowledge base to update 

the learner model and make expert 

judgements, it needs the ability to retain 

and remember information. 

It is important to note that the Bloom's 

learning goals [22] have not been met, and 

a 100 shows that the principles of the 

constructivist theory [6] have been 

fulfilled, where the learner develops new 

knowledge. 

Pedagogical model: 

Learner profiles are used to match several 

learning routes and discover the most 

efficient one, so that a learner advances in 

such a way that produces expert behaviour. 

User interface: 

Integration of the three models into a 

unified user interface will ensure that it 

does not overload the student with too 

much information, while at the same time 

helping to maintain the learner's pace and 

redirecting them to other links utilising 

adaptive hypermedia [33]. 

Evaluation module: 

These milestones for the learner may be 

referred to as ITS assessment modules that 

test the student's knowledge on a regular 



 

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basis to ensure that the learning objectives 

have been met. From simple multiple-

choice quizzes to more advanced methods 

like employing simulators and emulators 

to solve issues, there are a wide range of 

assessment methods available. The next 

level of assessment would be to see how 

well students can apply what they've 

learned by designing their own games and 

puzzles. In order to make the principles 

more understandable, they should be 

applied to real-world scenarios. 

In this case, the knowledge base would 

assist students in solving issues by 

highlighting incorrect answers and 

providing clues to the right ones, much as 

a human tutor would. 

Regularly, the student model will update 

the profiles of students and their potential 

learning routes based on assessment 

findings. 

Conclusion and future work: 

Problems in the current intelligent tutoring 

systems for automata theory have been 

discovered and a solution has been offered 

to address these issues. 

In the future, this system will be designed 

and developed, and the results of its 

assessment will be reported by comparing 

its usage with those of the conventional 

classroom method. These findings may be 

used to other theoretical areas, such as the 

Mathematical Foundation of computer 

science, Digital Logic, Compiler Design 

etc. if results are sufficient. 

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