Intelligent Tutoring System for Computer Science Education and the Use
of Artificial Intelligence: A Literature Review
Rodrigo Elias Francisco
1,2 a
and Flávio de Oliveira Silva
1 b
Faculty of Computer, Federal University of Uberlândia (UFU),
Av. João Naves de Ávila, 2121, Block 1A, Room 1A243 - Campus Santa Mônica, Uberlândia - MG, Brazil
Federal Institute Goiano (IF Goiano) - Campus Morrinhos, Rodovia BR153, KM633 Zona Rural, Morrinhos - GO, Brazil
Intelligent Tutoring System, Computer Science Education, Artificial Intelligence, Literature Review.
Education in computer science brings specific challenges to the teaching-learning process. Students spend
a lot of time dealing with the complexity of problems and learning to use existing technologies. Intelligent
Tutoring System (ITS) is a technology that can contribute to this scenario, automating and adapting teaching
to the student’s profile. This work presents a literature review on ITS’s for Computer Science Education,
focusing on Artificial Intelligence (AI) in this scenario. We analyze the development and use of ITS’s in
Computer Science Education and assess AI techniques, algorithms, and datasets. The results of this review
point to challenges in research on aspects such as the unavailability and difficulty of reproducing datasets, the
lack of in-depth explanations about the relationship between AI techniques and these ITS data, the need to
deepen these techniques of AI, and the need for more research about software engineering to ITS. This work
contributes to providing opportunities to this research area that can help the digital transformation of Computer
Science Education.
The challenges of teaching Computer Science for stu-
dents and teachers can be perceived when analyz-
ing the various disciplines in the area. The diffi-
culties that students experience during the teaching-
learning process in the disciplines of algorithms and
data structures are a significant concern for educators
around the world (Silva et al., 2019). About teaching
Software Engineering, the study (Ouhbi and Pombo,
2020) interviewed educators in the area and found dif-
ficulties in involving students in Software Engineer-
ing courses and in developing practical activities for
students. These two examples highlight some prob-
lems that are related to Computer Science Education.
Intelligent Tutoring System (ITS) is an educa-
tional software aimed at adapting teaching to the stu-
dent profile (Alkhatlan and Kalita, 2018). Using Ar-
tificial Intelligence (AI), these systems can contribute
to the automation of stages of the teaching-learning
process in this area. For example, supporting a stu-
dent in solving problems involving practice and ab-
stract reasoning can be made possible by these sys-
This scenario makes it relevant to have a broad
view of ITS’s for Computer Science Education. How-
ever, we cannot find many studies that review the
literature on ITS’s for Computer Science Education.
Moreover, the few studies found do not consider the
use of AI in this research area.
This literature review analyses the current sce-
nario of the development and use of ITS in Com-
puter Science Education, focusing on AI-based tech-
nics. This review contributes to the reflection on the
problems that hinder the advancement of this area of
research and the availability of these ITS’s for stu-
dents and teachers. The main contributions are:
An overview of the development and use of ITS’s
in Computer Science Education focusing on the
use of AI-based techniques in this area;
Discuss issues that hinder speed, alignment, and
collaboration in research on ITS’s for Computer
Science Education;
A Discussion of the need to go further in the in-
vestigation of AI in these studies due to the lack of
clarity about the relationship between data models
and AI algorithms and the unavailability of public
datasets to improve AI adoption and results;
Francisco, R. and Silva, F.
Intelligent Tutoring System for Computer Science Education and the Use of Artificial Intelligence: A Literature Review.
DOI: 10.5220/0011084400003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 338-345
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
An indication of possible strategies to deal with
these problems to advance the area.
We organized this work as follows: Section 2 de-
scribes the background on ITS. Section 3 describes
the planning to this review. Section 4 describes the
current scenario of ITS’s in Computer Science Edu-
cation. Section 5 focus on the use of the AI in ITS’s
for computer science. Section 6 describes challenges
and opportunities. Finally, Section 7 describes some
concluding remarks.
This section addresses the foundations for under-
standing this literature review. We will present the
ITS main modules and related work.
2.1 Intelligent Tutoring System (ITS)
According to (Alkhatlan and Kalita, 2018), ITS’s
are software capable of improving, adapting, and au-
tomating teaching. These systems offer the student
interaction with the content adapted to their profile to
improve the learning experience.
An ITS usually has the Modules: Tutor, Student,
Expert Knowledge, and User Interface. We consider
these module names to standardize and facilitate un-
derstanding in this work. The Tutor Module is re-
sponsible for making pedagogical decisions and ex-
ecuting pedagogical instruction. This module needs
algorithms to define the ITS intervention threshold
in the study process, operationalize this intervention,
and recommend content. The Student Module needs
to capture, update and retrieve the student’s profile.
The Expert Knowledge Module needs to represent
and manipulate the learning content. The Student and
Expert Knowledge Modules provide input for the Tu-
tor Module to perform its tasks. The User Interface
Module enables students to interact with the content
and with the behavior of an ITS.
ITS literature presents different nomenclatures
when describing the components of an ITS. A com-
mon term is model instead of module. The Expert
Knowledge Module (EKM) sometimes is called the
domain model.
2.2 Related Work
We searched on surveys and reviews in order to ver-
ify how these works approached the topic of ITS’s for
Computer Science Education. We found six surveys
about ITS, and only two of which (Crow et al., 2018;
Nesbit et al., 2015) addressed the domain of Com-
puter Science Education.
Surveys (Almasri et al., 2019; Alkhatlan and
Kalita, 2018; Mousavinasab et al., 2021; Kulik and
Fletcher, 2016) address general aspects of ITS’s.
They do not emphasize ITS’s for Computer Science
Education and therefore are unrelated works.
The work of (Crow et al., 2018) presented a Sys-
tematic Review on ITS’s for the computer program-
ming domain. This analysis showed the distinction
between these ITS’s concerning the topics covered
and the resources offered to students.
The study (Nesbit et al., 2015) reported on an on-
going Survey on ITS’s for Computer Science Edu-
cation and Software Engineering Education. Com-
puter programming was the discipline with the high-
est number of ITS proposals. They noticed a gradual
increase in publications on this subject from 1975 to
2014. This survey aimed to analyze aspects related to
the student model, content mastery, and the incorpo-
ration of technological resources ranging from games
to the analysis of the student’s affective state.
Despite the relevance of these surveys, we noticed
gaps in the research. The most recent survey (Crow
et al., 2018) only addressed the domain of computer
programming, and the survey (Nesbit et al., 2015)
mentioned that the most significant number of ITS’s
among those analyzed are also for the domain of pro-
gramming. Both analyzed ITS’s features, but neither
emphasized the AI techniques used in these features.
This present survey investigates this gap by analyzing
research on ITS’s for Computer Science Education,
focusing on the use of AI techniques.
This Literature Review aims to analyze the research
scenario on ITS’s for Computer Science Education.
The following research questions (RQ) were the basis
for this work.
RQ1 - What is the current scenario about the de-
velopment and the use of ITS’s in Computer Sci-
ence Education?
RQ2 - How is the use of AI in ITS’s for Computer
Science Education?
Some available researches propose educational
technologies whose didactic content to involved is
generic, making it possible to diversify the contents.
However, this research is interested in specific ITS’s
for Computer Science Education. These ITS’s have a
design planned concerning the content.
Intelligent Tutoring System for Computer Science Education and the Use of Artificial Intelligence: A Literature Review
This review relied on a Google Scholar search. We
chose Google Scholar because it indexes the leading
databases of scientific papers. Table 1 presents each
search string and its respective number of hits.
Table 1: Search strings and Number of Hits.
Search String Nº Hits
String"intelligent tutoring systems" computer science 59600
"intelligent tutoring systems" "computer science" 31600
"intelligent tutoring system" computer science 30600
"intelligent tutoring system" "computer science" 17200
"intelligent tutoring systems" "software engineering" 7270
"intelligent tutoring system" "software engineering" 4350
"intelligent tutoring systems" "software maintenance" 321
"intelligent tutoring system" "software maintenance" 170
We defined these search strings due to the interest
in analyzing the context of ITS’s for Computer Sci-
ence Education in a conceptual and practical perspec-
tive. The term ITS has been combined with computer
science to reflect the conceptual perspective and has
been combined with software engineering and soft-
ware maintenance to reflect the practical view.
The software engineering and software mainte-
nance contents are potentially subsets of the computer
science content set, which makes this set of search
strings a reinforcement for the search as a whole. The
interest in analyzing works with a practical perspec-
tive related to this area also occurred due to the im-
portance of these valuable skills for professionals in
the industry.
We chose to analyze the first ten resulting pages,
as the results are ordered by relevance, and these
searches brought a vast number of results. All the
search results had the title, and abstract analyzed to
verify their use in this work. This process generated
an initial set of 40 papers, and after further reading,
we reduced this initial set to the 26 articles used in
this literature review.
To understand the current scenario of use and de-
velopment of ITS’s in Computer Science Education
(RQ1), we made the Table 2. Table 2 lists the
works found that fall under tutor systems and ITS’s
with application domains aligned with the Associa-
tion for Computing Machinery (ACM) (ACM Com-
puting Curricula Task Force, 2013) curriculum. We
also describe whether the work presents and details
the three main modules of the ITS architecture.
According to the works found, we noticed the ex-
istence of two categories of works. The first category
clarifies that the work presents an ITS according to its
conceptual definition. However, the second category
does not explain its ITS, so it only describes a tutor
Among the works, some of them presented a ITS’s
for Computer Science Education (Jeremic et al., 2009;
Jeremic et al., 2012; Carter and Blank, 2013; Hars-
ley et al., 2016; Abd Rahman et al., 2016; Verdú
et al., 2017; Price et al., 2017; Hooshyar et al., 2018;
Figueiredo and García-Peñalvo, 2020; Galafassi et al.,
2020; Alshaikh et al., 2021). Modules found in these
works are aligned with the conceptual definition of
ITS modules. They present dynamics based on algo-
rithms and data representations that enable the execu-
tion of the ITS’s functionalities.
As described in Table 2, several computer science
related contents were addressed in the ITS’s. Among
these contents, it is possible to exemplify: Computer
Network Design (Verdú et al., 2017) and Program-
ming (Figueiredo and García-Peñalvo, 2020).
We found five works (Alshaikh et al., 2021;
Figueiredo and García-Peñalvo, 2020; Hooshyar
et al., 2018; Price et al., 2017; Carter and Blank,
2013) whose application domains of ITS’s are aligned
to the ACM Body of Knowledge described as Soft-
ware Development Fundamentals.
Within these ve works within the domain of
Software Development Fundamentals, we found that
three systems (Figueiredo and García-Peñalvo, 2020;
Hooshyar et al., 2018; Price et al., 2017) that ad-
dress the field of computer programming. The ITS
presented by the study by (Figueiredo and García-
Peñalvo, 2020), which was built as a teacher support
tool, can detect the student’s situation, needs, skills,
and learning style from a predictive model to con-
tribute to the work of the teacher who needs to decide
the next steps of instruction. The solution-based ITS
proposed by (Hooshyar et al., 2018) seeks to improve
students’ programming problem-solving strategy. It
offers solutions that help students with the automatic
generation of flowcharts that represent the algorithms
related to the exercises provided by the tool and sup-
port the navigation of topics and activities. The study
on the programming environment for newbies iSnap
(Price et al., 2017) presented the incorporation of the
idea of offering tips during task resolution, from the
ITS, in its proposal.
The other studies found for Software Develop-
ment Fundamentals address Software Understanding
(Alshaikh et al., 2021), and Software Debugging
(Carter and Blank, 2013). The work by (Alshaikh
et al., 2021) presented a Socratic ITS, its author-
CSEDU 2022 - 14th International Conference on Computer Supported Education
Table 2: Papers on ITS for Computer Science Education.
Reference ACM Body of Knowledge ACM Discipline
Detail All
ITS Modules?
ITS Module
Tutor? Student?
(Nakhal and Bashhar, 2017) Algorithms and Complexity
Basic Automata, Computability and
Complexity; Advanced Automata
Theory and Computability
No Yes No No
(Harsley et al., 2016) Algorithms and Complexity
Fundamental Data Structures and
No No Yes No
(Albatish et al., 2018) Architecture and Organization Digital Logic and Digital Systems No Yes No No
(Galafassi et al., 2020) Discrete Structures Proof Techniques No No Yes No
(AbuEl-Reesh and Abu-Naser, 2018)
Information Assurance and
Cryptography No Yes No No
(Elreesh and Abu-Naser, 2019)
Information Assurance and
Cryptography No Yes No No
(Mahdi et al., 2016)
Information Assurance and
Threats and Attacks; Cryptography No No No No
(Al-Hanjori et al., 2017)
Networking and
Introduction; Local Area Networks No No No No
(Alshawwa et al., 2019)
Networking and
Introduction; Local Area Networks No Yes No No
(Verdú et al., 2017)
Networking and
Introduction; Local Area Networks Yes Yes Yes Yes
(Marouf and Abu-Naser, 2019) Operating Systems Overview of Operating Systems No Yes No No
(Hasanein and Naser, 2017)
Parallel and Distributed
Cloud Computing No Yes No No
(Oberhauser, 2017)
Software Development
Development Methods No No No No
(Haddad and Naser, 2017)
Software Development
Development Methods No Yes No No
(Carter and Blank, 2013)
Software Development
Development Methods Yes Yes Yes Yes
(Paaßen et al., 2016)
Software Development
Fundamental Programming
No No No No
(Al-Bastami and Naser, 2017)
Software Development
Fundamental Programming
No Yes No No
(Price et al., 2017)
Software Development
Fundamental Programming
No Yes No No
(Mosa et al., 2018)
Software Development
Fundamental Programming
No Yes No No
(Hooshyar et al., 2018)
Software Development
Fundamental Programming
No Yes No Yes
(Al-Shawwa et al., 2019)
Software Development
Fundamental Programming
No Yes No No
(Figueiredo and García-Peñalvo, 2020)
Software Development
Fundamental Programming
No No Yes No
(Alshaikh et al., 2021)
Software Development
Fundamental Programming
No Yes No Yes
(Jeremic et al., 2009) Software Engineering Software Design Yes Yes Yes Yes
(Jeremic et al., 2012) Software Engineering Software Design Yes Yes Yes Yes
(Abd Rahman et al., 2016) Software Engineering Software Project Management Yes Yes Yes Yes
ing tool and its mechanism of generation and under-
standing dialogues for the domain of software com-
prehension. The ITS for software debugging (Carter
and Blank, 2013) supports individually the student in
problem solving.
For the Software Engineering domain, the ITS’s
(Abd Rahman et al., 2016; Jeremic et al., 2012;
Jeremic et al., 2009) were found. The ITS ABITS-
FPM (Abd Rahman et al., 2016) was proposed for
teaching and learning Metrics for Function Points.
This ITS provides visualization, immediate feedback,
recommendation, interactive help, and guided help.
The paper presented: a student model composed of
student personal facts, learning levels, presentation
styles, and assessment results; and a domain model
aligned with knowledge about Metrics for Function
Points, which includes theoretical content, practice
questions, question tips, and supporting information
related to student practical performance.
The ITS DEPTHS (Jeremic et al., 2012; Jeremic
et al., 2009) was proposed for the topic of software
design patterns. The study by (Jeremic et al., 2009)
presented the use of a dependency graph to model
the domain of software design patterns, proposed a
strategy for the tutor module that involves facts, rules,
queries, and the production of concept plans and plans
of class and the accomplishment of tests to evaluate
the student that collects data of difficulty, time and
result. However, the (Jeremic et al., 2012) study em-
phasized the student model at ITS DEPTHS with the
use of personal data, static and dynamic performance
data, and teaching histories and their updating.
However, we found only one work for each of
the other domains, i.e., Algorithms and Complexity
(Harsley et al., 2016), Discrete Structures (Galafassi
et al., 2020) and Networking and Communication
(Verdú et al., 2017).
The Collaborative ITS Collab-ChiQat Tutor was
proposed for the teaching-learning of algorithms, and
basic data structures (Harsley et al., 2016). This ITS,
in addition to including a very intuitive User Interface
(UI) that integrates programming and visualization of
Intelligent Tutoring System for Computer Science Education and the Use of Artificial Intelligence: A Literature Review
data structures, presented a student model that con-
siders the individual and collaborative behavior of the
student during the use of the system.
The study on the ITS EvoLogic (Galafassi et al.,
2020) addressed the teaching-learning of Natural De-
duction in Propositional Logic. They presented model
tracing as a resource capable of tracking the individ-
ual steps of each student to provide real-time feed-
back and the student model, which represents these
steps performed by students, categorizing the quality
of their efforts and their line of reasoning during the
task resolution.
The INTUITEL (Verdú et al., 2017) approach,
which includes an ITS adaptable to the Learning
Management System (LMS) and performs content
recommendation, was applied to a Computer Net-
work Design course through integration with Moo-
dle. This approach offers non-intrusive recommen-
dations and feedback on the best learning path con-
sidering profile, progress, context, pedagogical strate-
gies, and environmental influences. Teachers need to
model the learning process in INTUITEL concern-
ing their teaching materials and strategies, and IN-
TUITEL handles this modeling in an ontology-based
approach. Although the system has been applied in
the Computer Network Design domain and integrated
into the LMS Moodle, it can be used in other courses
and integrated into other LMS’s.
We found in the literature on ITS’s for Computer
Science Education that describe Tutor Systems that,
despite claiming that they are ITS’s, approach the
characteristics of ITS’s in a very simplified way, e.g.
without presenting computational techniques such as
Artificial Intelligence algorithms or structures for rep-
resentation of models. The papers (Marouf and Abu-
Naser, 2019; Elreesh and Abu-Naser, 2019; Mosa
et al., 2018; AbuEl-Reesh and Abu-Naser, 2018; Al-
batish et al., 2018; Hasanein and Naser, 2017; Haddad
and Naser, 2017; Al-Hanjori et al., 2017; Nakhal and
Bashhar, 2017; Mahdi et al., 2016), which were based
on the authoring tool Intelligent Tutoring System
Builder (ITSB), and work (Alshawwa et al., 2019; Al-
Shawwa et al., 2019; Paaßen et al., 2016; Oberhauser,
2017) fit into this perspective.
In this sense, for this Tutor Systems category, only
the works (Oberhauser, 2017; Paaßen et al., 2016)
will be presented as they present future possibilities to
contribute to the construction of ITS’s for Computer
Science Education.
A source code recommendation, navigation, and
3D visualization approach (Oberhauser, 2017) was
proposed to contribute to the understanding of soft-
ware in a perspective of exploratory, analytical, and
descriptive cognitive processes. This Tutor System
has a recommendation service based on a theoretical
model of program comprehension and information re-
lated to the MethodRank metric for source code, filter
processing, calculations of distances, and points of in-
terest in the source code.
The strategy presented by the study by (Paaßen
et al., 2016), which addressed the tracking of pro-
gram execution, can be used in ITS’s for the teaching-
learning of computer programming. In this strategy,
the execution traces of the programs are captured and
compared using the Edit Distance algorithm to obtain
information about the functionality that the program
implements, regardless of syntactic differences.
Table 3 aims to help understand the scenario of the
use of AI in ITS’s for Computer Science Education
(RQ2). Table 3 presents the subset of works from
Table 2 that addressed AI in the ITS’s. Information
about the AI technique used, the existence of the al-
gorithm description, the availability or possibility of
reproducing the datasets, and the availability of the
ITS for use are presented.
Agent-based architecture was an AI technique
used in the (Hasanein and Naser, 2017; Abd Rahman
et al., 2016) studies. Despite using this type of ar-
chitecture, these studies did not mention which algo-
rithms were used in their agents.
Multiagent System and Bayesian Network were
other AI techniques found in one study. The ITS
based on solutions for the teaching-learning of com-
puter programming (Hooshyar et al., 2018) used a
Multiagent System for the automatic generation of
flowcharts from an approach that works with the text
of the problem specification and a Bayesian Network
to model the programming domain content and pre-
The Case-based reasoning (CBR) was used in an
ITS for software debugging (Carter and Blank, 2013).
The perception of the relationship between debugging
and CBR motivated the authors to propose CBR in the
design of this ITS. The proposal includes the CBR
cycle and uses representations of the software and er-
rors, e.g., the generated abstract syntax tree, compila-
tion, execution, and static analysis data.
Fuzzy was used by the (Jeremic et al., 2012;
Jeremic et al., 2009) studies, which are about the ITS
DEPTHS. This ITS works with the updating of the
student model through Fuzzy rules (Jeremic et al.,
2012), which involves production rules and Fuzzy
logic, and with Fuzzy sets and theories of certainty,
CSEDU 2022 - 14th International Conference on Computer Supported Education
Table 3: Papers that addressed AI.
Reference AI Based Technique Algorithm
(Hasanein and Naser, 2017) Agent-based architecture No No No No
(Abd Rahman et al., 2016) Agent-based architecture No No No No
(Hooshyar et al., 2018)
Bayesian networks;
Multi-agent system
No No No
(Carter and Blank, 2013) Case-based reasoning No No Yes No
(Jeremic et al., 2009) Fuzzy sets No No No No
(Jeremic et al., 2012) Fuzzy rules Yes No Yes No
(Galafassi et al., 2020) Genetic Algorithm Yes No Yes No
(Figueiredo and García-Peñalvo, 2020) Neural network Yes No Yes No
(Verdú et al., 2017) Ontology-based reasoning No No No Yes
factors to calculate the student’s knowledge status
(Jeremic et al., 2012; Jeremic et al., 2009).
Genetic Algorithm was used by ITS EvoLogic
(Galafassi et al., 2020). This study proposes model
tracing as a resource for the expert agent, which uses
an adapted GA to solve Natural Deduction problems
in the Propositional Logic to accompany students in
real-time. Neural Network was used in the ITS for the
domain of introduction to programming (Figueiredo
and García-Peñalvo, 2020). The neural network-
based model predict student failure.
The Ontology-based reasoning approach was used
by the INTUITEL (Verdú et al., 2017) project. This
project, which includes an ITS adaptable to the
LMS and performs content recommendation, uses
ontology-based reasoning from various ontologies or-
ganized in layers. This reasoning is made possible by
the INTUITEL Reasoning Engine and the Learning
Progress Model (LPM). The LPM deduces the stu-
dent’s learning state so that the INTUITEL Reason-
ing Engine can use it to recommend the next learning
As a result of this study, this section presents some re-
search challenges and opportunities identified in this
6.1 Low use of AI Techniques
There is low usage of AI techniques in ITS’s for Com-
puter Science Education. Among the 26 articles in-
cluded in this review, only nine addressed some AI
techniques, equivalent to 34.6 %.
In addition to the few articles that addressed AI
techniques, we noticed that 45.5% of the articles that
used AI did not even present details of the algorithms.
The works (Hasanein and Naser, 2017; Abd Rahman
et al., 2016), which used Agent-based architecture,
did not even mention which algorithms were used in
their agents.
AI techniques are impacting several areas and we
can clearly state that there are many different oppor-
tunities to explore its use in ITS for Computer Sci-
ence Education, then improving and strengthening
this area.
6.2 ITS’s not Available Publicly
Computer Science Education has geographic particu-
larities, which requires investigating the effects these
ITS and their AI-based functionalities bring to teach-
ing in different contexts. Only one of the AI-based
ITS’s was publicly available for use (Verdú et al.,
2017). This fact hampers the popularization of ITS’s
in real educational scenarios, partnerships between re-
searchers, and research advances.
In this sense, it is welcome that the ITS’s could
be available to other researchers and teachers, which
could improve the research and development of this
area. At least, the research community could have at
least this goal for new systems and techniques.
6.3 Lack of Public Datasets
None of these studies with the ITS’s that addressed
AI presented the datasets used in the AI algorithms.
Therefore, researchers need to reconstruct scenarios
to collect data that make it possible to analyze the re-
sult of new AI techniques or even improvements in
existing methods.
This lack of public data hinders the comparison
between different AI techniques. A welcomed op-
portunity here is to capture and make public datasets
obtained from real scenarios of the use of ITS’s for
Computer Science Education.
Intelligent Tutoring System for Computer Science Education and the Use of Artificial Intelligence: A Literature Review
6.4 Difficulty to Reproduce Datasets
Another challenge is to reproduce datasets using
ITS’s presented in the literature. In the set of stud-
ies on ITS’s for this domain using AI, only 44.4% of
these works described data or text that could help in
this reproduction. In addition, there is the difficulty
of accessing educational scenarios and modifying the
teaching-learning process to carry out this data col-
lection from the use of ITS’s by students.
Reproducing scenarios could provide a systematic
opportunity to compare different ITS’s, indicating the
right direction to improvements and finding learned
lessons that the following research should avoid.
6.5 Low Coverage of Topics
The surveyed ITS’s covers only 44.4% (8 out of 18) of
the ACM Body of Knowledge several topics. Further-
more, the group of disciplines ACM Body of Knowl-
edge named Software Development Fundamentals oc-
cupies 30.7% of these surveys.
This fact brings the opportunity to expand the de-
sign, development, and deployment into other groups
of disciplines in the Computer Science area.
In this work, we conducted a literature review about
the AI-based techniques used in ITS for Computer
Science Education, considering the undergraduate
courses. Our work contributes to this area by pre-
senting an analysis of this research area with the AI
perspective. This research found 26 different articles
in the review scope.
Our findings of the current scenario of develop-
ment and use of ITS’s for Computer Science Educa-
tion show that these systems’ set of disciplines are
small. Only 19.2 % of the analyzed ITS’s detail the
three main modules of its architecture. Only 26.9 %
of the papers present the EKM, which represents and
manages the educational content. The review consid-
ered not only ITS’s but also tutor systems that follow
a different concept compared to the ITS.
We noticed some AI methods in these studies,
such as Genetic Algorithms, Bayesian Networks, and
Neural Networks. In general, the works lack descrip-
tions of the used techniques in ITS’s needing further
explanation. None of the articles available the dataset.
The papers presented little information about AI algo-
rithms. We found only one ITS that used AI and was
publicly available. There are several opportunities for
the use of AI in ITS.
Our assessment of this research area brought a set
of challenges and opportunities. There is room to im-
prove the use of the AI techniques on ITS’s improving
presented and using these techniques in researching
new systems.
Another opportunity that the research community
can explore is to publish ITS’s, make them pub-
lic, and foster their use in different educational con-
texts. This usage can contribute to the lack of pub-
lic datasets that could promote research on new AI
techniques and create reproducible experiments using
public datasets. These opportunities could also con-
tribute to improving experimental research in this re-
search area.
Finally, a clear opportunity in this area is to ex-
pand the coverage of the ITS to other topics of Com-
puter Science Education. This expansion would im-
prove the teaching-learning process in several areas of
knowledge that encompass computer science, helping
the digital transformation of Computer Science Edu-
This work is inside UFU-CAPES.Print Program. This
study was financed in part by the Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior
Brasil (CAPES) Finance Code 001. This research
also received the support from PROPP/UFU.
Abd Rahman, A., Abdullah, M., and Alias, S. H. (2016).
The architecture of agent-based intelligent tutoring
system for the learning of software engineering func-
tion point metrics. In 2016 2nd International Sym-
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