11institutetext: Wrocław University of Economics and Business,
Komandorska 118/120 street, 53-345 Wrocław, Poland
11email<EMAIL_ADDRESS>
http://www.ue.wroc.pl/en/ 22institutetext: Gdańsk University of Technology,
Faculty of Electronics, Telecommunications and Informatics,
Department of Software Engineering,
11/12 Gabriela Narutowicza Street,
80-233 Gdańsk, Poland
22email<EMAIL_ADDRESS>
https://pg.edu.pl/en/home

# Artificial Intelligence Technologies in Education: Benefits, Challenges and
Strategies of Implementation

Mieczysław L. Owoc 11 0000-0003-1578-6934
Agnieszka Sawicka 11 0000-0002-8318-9931
Paweł Weichbroth 22 0000-0002-1645-0941

###### Abstract

Since the education sector is associated with highly dynamic business
environments which are controlled and maintained by information systems,
recent technological advancements and the increasing pace of adopting
artificial intelligence (AI) technologies constitute a need to identify and
analyze the issues regarding their implementation in education sector.
However, a study of the contemporary literature reveled that relatively little
research has been undertaken in this area. To fill this void, we have
identified the benefits and challenges of implementing artificial intelligence
in the education sector, preceded by a short discussion on the concepts of AI
and its evolution over time. Moreover, we have also reviewed modern AI
technologies for learners and educators, currently available on the software
market, evaluating their usefulness. Last but not least, we have developed a
strategy implementation model, described by a five-stage, generic process,
along with the corresponding configuration guide. To verify and validate their
design, we separately developed three implementation strategies for three
different higher education organizations. We believe that the obtained results
will contribute to better understanding the specificities of AI systems,
services and tools, and afterwards pave a smooth way in their implementation.

###### Keywords:

Artificial intelligence Benefit Challenge Strategy Implementation.

##  1 Introduction

Research into artificial intelligence (AI) has tended to increase for a number
of reasons. The desire and excitement to create intelligent systems was the
case in the past in the academic community, while now it seems inevitable that
their development and inception have been addressed by the majority of
organizations, representing almost all business sectors. It is not only the
effect of the far-reaching technological progress such as microprocessors,
data storage and global networking, but also the impact of changes in business
strategies. While the debate on how AI will change business is at the top of
the present-day agenda [1], education is already being challenged to
reconceptualize existing teaching and learning methods by putting AI
techniques and tools into service [2]–[4].

Indeed, Sameer Maskey, founder and CEO at Fusemachines, an AI Education and AI
Talent Solution provider based in New York City (USA), in an interview with
Forbes magazine published on 8th June 2020, said that [5]: “It will be
important for educators and policymakers to explore the intersection of
education and artificial intelligence. The application of machines in learning
environments is only one variable in a multifaceted equation. We have to
consider barriers that prevent an even distribution in technological resources
and how to overcome them. We must also ensure that teachers are prepared and
empowered to leverage artificial intelligence. Assuming these elements are
addressed, the possibilities of AI-powered learning are infinite”.

Inspired by his words, and apart from the financial costs and profits, the
question arises what are the specific benefits and challenges of implementing
artificial intelligence in education? Since little work has focused on this
area, the first research goal is to identify and analyze its possible benefits
and preceding challenges (see Sec. 2.2 and 2.3, respectively). In this case, a
qualitative generic thematic analysis was undertaken due to its flexibility to
collect descriptive data in the narrative form.

Our study is also driven by a concern for the implications of the human factor
due to the increasing evidence of the apprehension raised against the
proliferation of artificial intelligence since the variety of its applications
seems to be immeasurable, ranging from computer games [6,7], decision-making
[8]–[10], education [11]–[16], enterprise management [17]–[19], grid computing
[20]–[23], knowledge management [24]–[27], learning systems [28]–[31],
ontologies [32]–[34], smart cities [35]–[37], and software engineering
[38]–[40], to name just a few. On the contrary, we attempt to argue that AI
can amplify educational effectiveness, concerned with sharing, developing and
disseminating knowledge, at the same time preserving human autonomy, agency
and capabilities. Therefore, the second research goal is to analyze and
evaluate the usefulness of existing AI technologies (see Sec. 2.4).

To explore these two goals, we collect, analyze and review a plethora of
information sources. Their addresses were obtained from the Google web search
engine and Google Scholar website by using combinations of keywords such as:
“artificial intelligence”, “adoption”, “acceptance”, “advantages”,
“disadvantages”, “implementation”, “deployment”, “education”, “benefits”,
“challenges”, “risks”, and “technologies”.

What is much rarer, however, was to find applicable and relevant
implementation strategies. Nevertheless, based on our previous results, which
provide solid theoretical foundation, we also designed and created a generic
strategy, able to be applied in a strategic plan regarding any of the AI
systems, services or tools in all industries, and in organizations of a
variety of sizes. In particular, our strategy addresses the “what” and “why”
of the activities, embedded in a five-phase process model (see Sec. 3).

To verify and validate its design, we separately developed three
implementation strategies (see Sec. 4), for three different non-public higher
education institutions, namely the “Copernicus” Wroclaw Computer Science and
Management University (WSZI), WSB University in Gdansk (WSB) and Jan
Wyżykowski University (UJW). By adopting a qualitative study design, as an
exploratory, descriptive approach, in the first step we collected all of the
necessary data, using a specific thematic approach, while in the second step
we analyzed the data in a fashion reflecting the aim of recognizing
circumstances and challenges related to the subject matter.

It is worth noting here that nowadays, non-public universities play an
essential role in higher education [41]–[43] and have to compete very hard in
order to gather potential students [44]–[46]. Their position increasingly
depends on the quality of the education and the managerial competencies of the
university governance [47]–[49]. In both cases, applying intelligent
technologies seems to be a must if one considers their competitiveness and
development. Yet, the level of its implementation is still relatively low in
comparison with the business sector.

On the other hand, there are a few cases documented, giving an idea in which
areas AI methods have been implemented within higher education institutions
(HEIs) [50]–[52]. In particular, intelligent technologies are gradually being
implemented in non-public universities [53], usually being part of the
strategy that sets up a framework of priorities [54]. However, to the best of
our knowledge, very few studies have considered the benefits and challenges
affecting the implementation of AI technologies within university emerging
set-ups.

The rest of the paper is structured as follows. Section 2 discusses artificial
intelligence (AI) in the education sector, introducing the basic concepts,
benefits and challenges related to its implementation, as well as a discussion
of modern AI systems and tools. Section 3 presents a strategy implementation
model, conceptualized by a five-stage generic process along with the
corresponding configuration guide. Section 4 describes and analyzes the case
studies, illustrating how AI implementations, based on underlying decision
processes, are conducted in practice. Finally, we conclude in Section 5.

##  2 AI in Education

By design, intelligent technology is a method which uses knowledge to achieve
concrete purpose in efficiency. At present, there are the following
intelligent technologies: multi-agent, machine learning, ontology, semantic
and knowledge grid, autonomic computing, cognitive informatics and neural
computing. The prompt advances in these fields have already brought
substantial changes in education, opening up new opportunities and challenges
to teach and learn anytime and anywhere by providing new methods and systems
that aim to stimulate innovative teaching and ultimately improve learning
outcomes.

###  2.1 2.1 AI concepts

The continuous progress of modern information technologies is strictly
connected with the presence of implemented artificial intelligence techniques.
During the over 60 years of development of artificial intelligence, several
intelligent approaches have appeared in almost all sectors of modern life.
Therefore, one can talk about the new generation of AI, including the
potential power of the current solutions and the variety of applied
techniques. The crucial components of such an understanding of AI 3.0 are
presented in Figure 1.

Figure 1: Artificial Intelligence 3.0 [55].

Particular categories of AI can be combined in the final applications; some of
them seem to be obligatory (knowledge, reasoning, processing) while others are
employed for the specific solutions where knowledge should be permanently
updated (machine learning) or requires cooperation between specialized agents
(multi-agent systems). Either way, the current solutions are not quite
satisfactory – the level of the obtained progress is less than human
intelligence. The next imaginable stages are still before us – we are heading
toward Artificial Super Intelligence passing in the meantime through
Artificial General Intelligence (see Fig. 2).

Figure 2: Future evolution of Artificial Intelligence [56].

To sum up, the landscape of Artificial Intelligence in terms of using the main
categories is rather stable. Yet, learning-based techniques are playing an
increasingly significant role. However, the specialty of the application areas
can determine the shape of implementation of artificial intelligence methods.
The educational sector and the specifics of non-public universities are real
determinants in defining the implementation of intelligent technologies.

###  2.2 AI technologies

With the advent of AI in the mid-1950s to the present day, the proliferation
of its methods and techniques has made it possible to develop intelligent
systems which are increasingly relevant in education and training. For
instance, Nuance, the high-tech company from Burlington (Massachusetts) [57],
has implemented speech recognition software that can be used both by students
and faculty. The application can transcribe up to 160 words per minute and is
particularly useful for students who have limited mobility or struggle with
writing. The available features also enhance word recognition and spelling.
Teachers can apply the software to prepare homework and assemble and schedule
recurrent tasks such as sending notifications or emails.

Another company, Knewton, is promoting its newest product, Alta [58], as a
complete courseware solution that combines expertly designed adaptive learning
technology with high quality openly available content. In other words, Alta
helps identify drawbacks in a student’s knowledge by providing relevant
coursework, and supports teaching activities at different educational levels.

Cognii is another provider of artificial intelligence by virtue of virtual
assistants (VAs) that combine the powers of conversational pedagogy with the
conversational AI technology [59]. Interestingly, the open-format applied to
the VAs’ responses is claimed to improve critical-thinking skills. The VAs
also provide real-time feedback, and individual tutoring, customized to the
particular student’s requirements.

Querium, a successful start-up from Austin (Texas) [60], helps students master
critical STEM skills by delivering a customizable STEM tutoring program of
personalized and effective lessons which works on desktop computers and
smartphones. Querium’s AI provides teachers with insights into the student’s
learning habits and highlights areas in which the student should improve.

Century is another successful start-up, established in 2013 in London by Priya
Lakhani [61]. Behind its success is a diverse team of teachers, technologists,
neuroscientists and parents. Their platform utilizes data analytics and
cognitive neuroscience to create personalized learning plans and reduce
workloads for teachers. Moreover, the AI platform tracks student progress,
identifies knowledge gaps and delivers personal learning recommendations and
feedback. The teacher dashboard allows them to monitor individual student and
whole-class performance.

These examples show only some of the possible applications of AI in the
education sector. Nevertheless, we argue that the above allows us to claim
that at a general level, the constellation of AI methods and tools leverage
both learning and teaching. With time constraints, limited resources and the
abundance of new, incoming knowledge, the engagement of artificial
intelligence seems to be a must to deliver a competitive environment,
facilitating both the learning and teaching processes.

According to the latest literature concerning the application of artificial
intelligence in the education sector, computer-assisted tutoring represents
the major applied field of AI [62]. At present, we can observe that the rate
of development of intelligent educational software (IES) has increased due to
the high and dynamic students demands [63].

The main problems are related to content flexibility and adaptability, and for
reusability, sharing and collaborative development of the learning objects and
structures [64]. Moreover, providing user-oriented content depends on three
factors, namely the domain model, user model and instructional task model
[65].

###  2.3 Benefits

The advantages of artificial intelligence applications in education are vast
and varied. Here, everything can be considered to be beneficial if we are
thinking of anything, for example a computer program, that can efficiently
perform any task that would normally rely on the intelligence of a human.
Based on the state-of-the art research in this area, we outline nine areas in
which AI methods can bring added value for both learning and teaching
activities [66].

The first benefit concerns automated grading which simulates the behavior of a
teacher to assign grades to the answer sheets submitted by the students. It
can assess their knowledge by processing and analyzing their answers, giving
feedback and recommending personalized teaching plans.

Secondly, intermediate spaced repetition aims at knowledge revision when
someone is just about to forget. It is worth noting that Polish inventor Peter
Wozniak [67] introduced the SuperMemo application, which is based on the
effect of spaced repetition. The app keeps track of what a user is learning,
and when he/she is doing it. By applying AI techniques, the application can
discover when a user is most likely about to forget something and recommend
revising it.

Thirdly, feedback loops for teachers, aided by machine learning and natural
language processing techniques, improves the quality of student evaluations.
For example, a chatbot can collect opinions via a dialog interface similarly
to a real interviewer but with a small amount of work required by the user.
Moreover, each conversation can be adapted according to the student’s
personality and provided answers. A chatbot can even formulate the reasons for
particular opinions.

Fourthly, to support teachers in their classroom work, one can put into use
virtual facilitators. For instance, at the Georgia Institute of Technology on
Knowledge-Based Artificial Intelligence (KBAI) class, students were introduced
to a new teacher’s assistant named Jill Watson (JW) [68], who has been
operating on the online discussion forums of different offerings of the KBAI
class since Spring 2016. JW autonomously responded to student introductions,
answered routine, frequently asked questions, and posted announcements on a
weekly basis.

In the fifth place, Watts introduced chat campus based on the IBM Watson
cognitive computing technologies [69]. In brief, students at Deakin University
have asked IBM Watson 1600 questions a week to learn the ins and outs of life
on campus and studying in the cloud. Within 12 months of implementing Watson,
due to the enhanced quality of the student know-how at Deakin, this ground-
breaking solution has handled more than 55,000 questions from students.
Furthermore, the school is progressing its use of Watson, broadening its
capabilities and teaching the system to understand new sources of information.

Personalized learning is the sixth example of AI applications in the education
sector. In general, it refers to a variety of educational programs in which
the pace of learning and the instructional approach are customized and
eventually optimized for the needs of each learner [70]. In particular, the
content is tailored to the learning preferences and specific interests of each
student.

The seventh example—one of the most promising—is adaptive learning (AL). While
the traditional model of classroom education, continues to be very much one-
size-fits-all, on the contrary, AI-powered AL systems are designed to optimize
learning efficiency. For example, Yixue Squirrel AI (Yixue) collects and
analyses students’ behavior data, updates learner profiles, then accordingly
provides timely individualized feedback to each student [71].

Since cheating is a concern for all teachers, AI-powered anti-cheating systems
have been presented as another (eighth) application of AI in the education
sector. Proctoring is software which secures the authenticity of the test
taker and prevent him/her from cheating as a proctor is always present during
the test [72].

The last solution argued by Watts is data accumulation and personalization.
For instance, learning grammatical rules can be aided by examples only from
the domain being the subject of personal interest [73].

###  2.4 Challenges

There are several approaches to planning and organizing the implementation of
AI methods in the education domain [74]–[81], but discussion about the
essential challenges for decision-makers is still ongoing. To the best of our
knowledge, the list of potential challenges that influence the implementation
of intelligent technologies concern:

  * •

strategy refers to a general plan of implementation to achieve one or more
specific long-term goals accordingly to a schedule established and agreed with
all interested stakeholders;

  * •

organizational maturity refers to its employees, processes and technology
readiness and capability with respect to the adoption of artificial
intelligence technologies;

  * •

data governance, refers to data principles, quality, meta-data, access
requirements and data life cycle; since machines learn on the basis of data,
data governance is a crucial facet of the implementation and further
maintenance of AI;

  * •

infrastructure, being the combination of hardware and software systems, is
particularly acute due to compatibility and integration issues.

As one would expect, it is important to establish a strategy that defines the
goals with regard to the AI implementation and provides a means to manage
them. The strategy itself might take the form of a mix of qualitative and
quantitative approaches. The former aims to describe how the goals will be
fulfilled, while the latter aims to decide if the goals are fulfilled and
which goals are fulfilled. The fulfilment of the goals cab be expressed in
quantitative numbers, or/and in qualitative terms.

In general, maturity is a synonym of “full development” or “perfected
condition,” and since any organization is a living entity, it grows over a
period of time and learn from its decisions and outcomes. Therefore, all
organizations seem to be at some stage of maturity, striving forward to
development and perfection. From a strategic point of view, we stress the
importance of the high level of organizational maturity due to the changes
spanning across core dimensions of strategic management such as: alignment,
performance measurement and management, process improvement and
sustainability. In the context of our study, maturity assessment should
encompass external and internal benchmarks, describing the organization
readiness and capability to adopt AI technologies.

Another challenge is data governance, which is related to the system of data
organization, collection, control, storage, usage, archival and destruction.
The path of setting up data governance is driven by a specific program,
supported by particular policies and procedures, and communicated by
organizational leadership and management. In general, the regulations must
provide all of the necessary means to preserve the following generic
requirements: accessibility, availability, completeness, accuracy, integrity,
consistency, auditability, and security.

The last, but not least significant, challenge concerns the infrastructure
which encompasses all of the hardware installations and the software entities.
Recent advancements in the artificial intelligence technology landscape have
introduced specific requirements toward hardware capacity and software
capabilities. In an effort to integrate these cutting-edge technologies with
the existing systems, one has to incorporate solutions that underpin a
flexible and scalable end-to-end integration. Enabling on-the-fly software
asset configurations and reconfigurations (in the case of enabling/disabling
particular services) facilitates an “assembly-from-parts” model for
implementing new and updating existing AI applications from a catalog of
services.

We argue that the above-mentioned challenges are essential to take into
consideration while preparing the discussed scenarios (see Section 4). Our
research in essence attempts to identify and analyze the issues related to
implementing AI in education, however it still lacks the empirical evidence
which might at least confirm our perceptions of the studied phenomenon.

##  3 Strategy implementation model

Undoubtedly, artificial intelligence has had significant influence on various
industries, leveraging their effectiveness, productivity and profitability.
This also applies to the education sector which has been committed to several
reforms, addressing key sectoral issues and including incorporating artificial
intelligence tools and methods.

It is believed that such a shift will bring a new perspective to many facets
of existing learning and teaching techniques. Toumi, in his report from 2018,
claims that [67]: “(…) in the domain of educational policy, it is important
for educators and policymakers to understand AI in the broader context of the
future of learning. (…) As AI will be used to automate productive processes,
we may need to reinvent current educational institutions. (…).”

This opinion and many others thereafter argue the need to better comprehend
the impact of artificial intelligence on education. However, first we need to
develop a model that will drive the process of implementation and result in
the smooth deployment of AI systems. At this point, we argue that the below
model is suitable under general contexts, and is thus applicable to any
organization.

The five-stage process shown in Figure 3 organizes, arranges and systematizes
the tasks into consequent groups. The particular tasks employed in each stage
might cross other phases, to an extent which depends on the context of the
implementation (e.g. strategy, organizational maturity, data governance).
Therefore, the five stages are interdependent with each other, whereas the
duration and labor intensity may significantly vary.

Figure 3: General strategy implementation model.

The model of the implementation process consists of five stages:

  1. 1.

Plan and analyze stage concerns all activities associated with the creation
and maintenance of a plan which describes a list of steps with details of the
timing and resources required to achieve the desired goals, along with the
budget and general time frames.

  2. 2.

Design and specify stage aims to prepare and establish the structure and
organization of the system as well as to define the functional and non-
functional requirements. In other words, a specification should address all
enumerated goals in the first stage.

  3. 3.

Implement and configure stage can be interpreted twofold; the system
implementation is the process of creating its source code by the software
developers, or the system implementation is installing and configuring the
software applications.

  4. 4.

Test and evaluate stage aims to ensure that the actual software is free of
defects, and to check and determine whether it matches the expected
requirements, as defined in the second phase.

  5. 5.

Monitor and support stage concerns the surveillance process on measurable
events regarding the performance of the system, as well as providing
assistance and help to the users of the system required due to any issues and
incidents encountered.

Moreover, it should be emphasized that it is necessary to identify the
specificities of AI software and evaluate existing resources, including
hardware capacity, software compatibility and IT personnel. However,
anticipating all of the relevant resources is only possible under exceptional
circumstances exceptionally possible due to uncertainty regarding especially
the human factor and data quality (used to teach AI algorithms).

###  3.1 Implementation strategy configuration

One should keep in mind that the strategy is a “how” the goals (objectives)
will be achieved by the means (resources). In the context of this study, the
strategy consists of the desired objectives and corresponding actions in order
to implement an AI solution. Having said that, the structure of the
implementation configuration is as follows:

  * •

general purpose and vision,

  * •

scope, specific objectives with priorities and within time frames,

  * •

reference to the procedures used, regarding all five stages of the
implementation model (see Fig. 3),

  * •

reference to the AI systems, services and tools which are existing and able to
be included in the strategic plan, or in the case of developing a new system,
a description of the possible software vendors, trusted advisors and
consultancy agencies, and individuals experts, all with designated roles and
responsibilities.

This perspective conceives the know-how as a highly deliberate, descriptive,
and logical one, involving a sequential, rational and analytical collaborative
work of all interested stakeholders. The configuration strategy is
conceptually aimed at achieving the master plan by precisely following the
implementation process.

However, this form of strategy configuration can be mostly applied in
organizations which at least manage projects in accordance with a policy that
aims to identify and monitor the progress towards project performance
objectives (level two).

##  4 Exploratory Multiple Case Study

Respecting the autonomy of higher education institutions, we would expect very
individual approaches to deploying a single or suite of AI techniques for the
university. The selected non-public universities that participated in the
research are different in terms of geneses, formal statements as well as
levels of computer infrastructure and areas of education. Before presenting
the proposals for implementing artificial intelligence solutions in the
inquired HEIs, short descriptions of the universities are delivered, focusing
on their areas and levels of education. The state-of-the-art regarding the
maturity of the implemented IT solutions are also discussed.

###  4.1 Case settings

####  4.1.1 Case 1. WSZI in Wroclaw

The university which is the subject of research of this article, namely The
"Copernicus" University of Information Technology and Management, has been
operating on the educational services market for nearly 20 years and is one of
384 non-public universities in Poland.

Since 2001, it has educated 4,000 graduates, mainly in the area of new
technologies. Further observations and conclusions presented in the article
will be based only on their own observations of business practice and the
environment and competition due to the lack of data from external actors
evaluating the quality, innovation and conditions for studying and living for
students. Starting from 2019, the university has been conducting studies
solely in the field of Computer Science in a full-time and part-time system at
two levels, including post-graduate studies and courses.

The organizational structure of the university is a simplified structure with
one department and full decision-making ability of the Chancellor, who is also
the founder. His decisions are binding on financial, program and student
matters. For the analysis of the organizational structure, one can state that
WSIZ "Copernicus" omits the very important role of Rector. Usually, the rector
in a non-public university is the person who deals with the organization of
education and training, but his decisions are not binding without the
authorization of the Chancellor. In addition, the described university is a
unit educating a total of about 700 students in a full-time and part-time
systems.

Among others: the department of the Rector’s office, where there are too few
employees and students. It is worth noting that if there were changes in the
functioning of the organizational structure, it could be possible to
reorganize the work and introduce intelligent technologies into these
structures, introducing the innovation which would lead to the beginning of
changes.

The financing of a non-public university, which clearly affects the
development and functioning of educational institutions, departs from the
possibility of financing public entities. Obtaining funds or research grants
is difficult due to the small number of students.

There is also no obligation to conduct research, which makes it more difficult
to win in competitions. All retrofitting of the university halls are covered
only from the profit earned, which consists of tuition payment and fees for
program differences, conditions. When it comes to the issue of payment for
tuition, the student has the privilege of financing science on the basis of a
scholarships obtained, which are largely similar in terms of the procedures
for obtaining funds in public universities.

A non-public university, even though it is a university, is also an
enterprise. And just as a company will not exist without clients, a private
university will not be able to break out of the educational market without the
influence and opinion of its potential future students.

If we focus our conclusions on the differences in the functioning of these two
types of universities, we should also mention the change in the law on higher
education, which imposed student limits per lecturer on public universities.
In WSIZ "Copernicus", the current number of students per lecturer is 25–30
students, which definitely departs from the provisions of law for public
entities.

In addition, due to the lack of sufficiently qualified teaching staff and the
Chancellor’s desire to reduce employee costs, the university introduced the
possibility of writing diploma theses to teachers with a master’s degree,
provided that formal care over diploma theses will be carried out by a
promoter with a doctorate degree. However, he leads the student through the
whole process of writing the work, advises him, gives his opinion on his
progress and is listed in the diploma documentation. The content supervisor,
who is subject to the promoter, is a person with a doctorate degree or above.
Such a solution is allowed by the applicable law and applied in WSIZ
"Copernicus", however, there is no information whether and to what extent
public HEIs use it.

Looking at the above information about the structure, method of financing and
processes taking place in the area of education and work organization of the
described non-public university, it can be stated that although it is an
entity educating students similarly to state universities, it is significantly
different from them.

Obviously, it is not disputed that issues such as legal regulations, the shape
and form of the graduate and student documents, requirements regarding ECTS
points, and reporting or deadlines, are almost identical. However, they are so
individual in their approach to the quality of education and the candidate
that each of them should be considered separately. In the same way, when
designing and implementing business analytics solutions, it should be taken
into account that what works in a given non-public unit does not necessarily
have to serve another organization, especially a state one.

####  4.1.2 Case 2. WSB University in Gdansk

The WSB Universities is the largest group of university schools of business in
Poland. All of them are found in top positions in rankings of higher education
institutions. Founded in 1994 by TEB Akademia, WSB launched its first field of
study: “finance and banking”. Presently, the WSB Universities are located in
10 cities across Poland: Gdańsk and Gdynia, Bydgoszcz and Toruń, Wrocław,
Chorzów, Opole, Poznań, Szczecin, and Warszawa.

The organization of this particular University is typical for HEIs in Poland.
The faculties and departments are oriented on educational sectors and there
are many supporting divisions mentioned in the characteristics of the previous
University, but particular units cooperate strongly, including by utilizing
common platforms (webpages) for students and academic staff.

The WSB educational offer covers both bachelor’s and master’s programs. The
duration of the former is 3 years (6 semesters) while the latter is 2 years
long. It is worth noting here that a student can pursue second-cycle programs
in a field that is not directly related to that of their first-cycle degree.
In that way, one can extend their area of professional expertise and hence
enhance their employability. Each program is focused on developing practical
competencies and soft skills.

Each university is governed by their own statutes and regulations, but are
integral to the make-up of the ownership authorities. Moreover, each
individual university has its own internal procedures, regulations, and local
authorities. The university is governed by the rector along with the
chancellor. At the head of each faculty are the dean and vice-deans, supported
by the proxies.

University Faculties organize teaching and research into individual domains,
or groups of subjects. Their work is normally organized into subdivisions
called departments. The University’s administrative and support departments
support the running of the University and contribute to research, teaching and
international cooperation with other universities worldwide.

The organization claims to maintain a high performance culture that is
inspirational and motivating, providing internal and external funds to support
faculty staff development. The aim of each subsidiary is to provide an
attractive, sustainable, vibrant, and accessible campus, upheld by a
contemporary virtual environment in which students, staff, and engaged
stakeholders can interact, and share information and knowledge.

####  4.1.3 Case 3. Jan Wyżykowski University in Polkowice.

Jan Wyżykowski University was created thanks to the cooperation of Polkowice
Commune and Polkowice County. The founder of the University is the ZAMPOL
company, which Polkowice Commune and Polkowice County own all of the shares
of. Currently, UJW runs studies within the Bachelor’s, Engineer’s, Master’s
programmes and postgraduate studies and courses of Education offerings:
Bachelor’s studies (3-year studies) in the fields of: Administration, Pedagogy
and Management. Engineer’s studies (3.5-year studies) in the fields of:
Information Technology, Mechatronics, Logistics, Mining and Geology,
Production Management and Engineering. Master’s studies in the fields of
Management (2-year), Mechatronics (1.5-year) and a uniform (5-year studies
Law) are the newest in the offer.

All educational majors are offered as extramural studies, so basically,
students gain practical experience working for different companies. The second
important feature of the university is that it acts in a very specific region
known from its copper industry. Most of the students are employed in companies
connected with this industry so their professional expectations are strictly
tied with copper infrastructure or reflect more general positions typical for
medium-sized cities (administration and management).

Nowadays, the information systems present in UJW support typical transaction
processes: student enrollment, planning and evidence of classes and some
functions offering by online education. It is quite a well-functioning
university information systems but without functionality specific for AI
methods.

###  4.2 Implementation strategies settings

The solutions in Figure 4 use the latest technologies, which confirms that
many repetitive tasks and procedures could benefit from the support of AI
systems, thus offering new development opportunities and fields of study for
higher education.

Figure 4: Scenarios of implementation of selected AI methods for WSIZ, WSB and
UJW.

It is important to remember that there are many often more complex and non-
standard procedures at universities. The processes of obtaining the intended
effects from the implementation of the proposed solutions based on AI should
be in accordance with applicable standards and the law regarding higher
education and also take into account the capabilities of the university. Each
replacement or support through the implementation of technological innovations
is intended to shorten the time of document circulation, reduce administrative
costs and improve the quality of education, which is certainly worthy of
attention.

Naturally, introducing such a solution based on new technology solutions is
not without its own inherent risks. Non-public universities would need to
exercise extreme caution in protecting students’ personal data and would need
some level of human oversight to monitor every AI method.

####  4.2.1 Case 1. WSIZ university

Building creative solutions into the work of a non-public university is also
helpful in creating new products and services. The implementation of smart
technology solutions at a private university will foster the building of
scientific and educational progress, which will translate into satisfactory
financial results and will strengthen the position on the non-public
university market and introduce the element of innovation to the current
activities of the unit. As part of the implementation work envisaged at the
"Copernicus" University of Information Technology and Management in Wroclaw,
it is planned to create a chatbot—an intelligent application for managing
responses and relations with students and candidates for studies.

This solution, in connection with the personnel problems of the non-public
university described would answer simple questions about the dates of the
sessions, exams, classes and inform about the recruitment schedule and the
recruitment documents needed. As part of the planned implementation, it is
also planned to reorganize the signing of learning contracts, which would
consist in enabling their signing in electronic form via a link sent,
generated by the internal university system, which is consistent with the
provision in art. 60 cc. Support for such a contract would take place in the
following steps:

  1. 1.

A link would be sent to the student’s e-mail address to the page about the
learning contract with a request to read its contents.

  2. 2.

After learning the terms of the contract, the student would be asked to accept
it by clicking the “I accept the terms of the learning contract” button.

  3. 3.

After choosing this option, the contract is concluded
electronically.Afterwards, the student accepts the presented conditions, the
confirmation along with the contract is sent automatically in a pdf file to
the dean’s employee. It is then printed and archived to the student’s personal
file.

In addition, the plans of AI implementation in WSIZ also include the
preparation and full implementation of a voice guide for students and
candidates with impaired vision. The voice guide presents an educational offer
and discusses payment, calls the phone number for the department, and checks
the plan for that academic year.

The introduction of intelligent solutions for the work of the "Copernicus"
University of Information Technology and Management in Wroclaw creates
opportunities to facilitate procedures in the recruitment process, and reduce
the use of office machines, which also affects the improvement of the
environment. It relieves the work of the dean’s office, which is the most
important area in contact with students and candidates.

Of course, apart from the positive aspects, one may ask the question whether
intelligent technologies strive to improve human work and make it easier for
them to complete certain processes to become more attractive on the market, or
whether it is simply interference in human work and increasing unemployment.
However, believing only in the good aspects of the case, we believe that the
solutions and techniques of business intelligence will significantly improve
the quality of the education process and assist in acquiring new students at
the "Copernicus" University of Information Technology and Management in
Wroclaw.

####  4.2.2 Case 2. WSB Universities

We have identified three administrative areas which are planned to be fully or
partially automated by implementing artificial intelligence tools. The list is
given below (not in order of priority).

  1. 1.

Grouping, sorting and responding to emails. From our employees, we know that
replying to emails is a time-consuming job. Moreover, repetitive email
conversations are also frustrating and can be demotivating in the long-term.
There are a number of tools which are being considered to be included in a
pilot study, namely: AI Email Smart Answer [82], OMQ Reply [83], and Notion
[84]. All of these tools are able to automate responses to emails and
eventually replace the manual work of employees. Recurrent students’ requests
are automatically recognized and answered by the system. Obviously, the list
of features of the AI-powered solutions doesn’t end here.

  2. 2.

Scheduling appointments. Scheduling meetings with multiple students is a
labor-intensive task. We believe that instead of manually responding via email
to schedule an appointment and check the calendars of everyone involved, we
could implement intelligent agents that detect and recognize certain phases in
incoming emails, eventually proposing appointment times according to
individual availability, and schedule appointments based on the attendees’
responses. Again, we have selected a few tools which are planned to be tested
in a pilot study, namely: Julie Desk [85], ArtiBot [86], and Hendrix.ai [87].
Meetings are still a crucial method of organizing and planning work, but they
are a waste of time unless one accurately captures what was discussed and
agreed upon. By design, the agents require access to individual calendars,
email accounts, social media profiles and location data to provide the
necessary data to the AI-based inference engine. In return, they simply serve
as virtual assistants, capable of preparing meetings, dialing into conference
calls, turning on a video projectors, loading presentations, removing outdated
and duplicate contacts, and many more otherwise tedious tasks.

  3. 3.

Customer service AI chatbot. A chatbot is simply a software agent that can
simulate a conversation with a user in natural language in a real time trough
messaging applications, websites, mobile applications, or even over the
telephone. The requests reported by the students dramatically increase before
particular events, such as bachelor and master diploma exams, the beginning
and the end of the academic year, as well as from the candidates who intend to
become students. Therefore, customer service employees are regularly inundated
with follow-up calls, support requests, frequently and repetitively asked
questions, confirmation emails, complaints, and many more. To face these
issues, IBM tells us that “chatbots can help businesses save on customer
service costs by speeding up response times, freeing up agents for more
challenging work, and answering up to 80% of routine questions” [88].

Moreover, unlike live agents (employees), software agents don’t need lunch
hours or coffee breaks, and are not absent due to holidays, or illness, or any
other natural disasters which can put human lives at risk. At the moment, our
pilot study includes the following AI chatbot software to be tested:
ActiveChat [89], Respond.io [80], and ChatBot [91]. We expect to uncover
sizeable advantages by implementing chatbots across the organization.

On the other hand, we are aware that it will not be an easy and effortless
task. On the contrary, based on our learnings, we have identified and listed
the following three major challenges, namely: security and privacy, obstacles
and burdens due to polish language complexity, data input for machine learning
algorithms. Undoubtedly, chatbots are one of the most promising enterprise AI
technologies, however, achieving maximum business value from them requires
from us extensive work and persevering determination.

####  4.2.3 Case 3. UJW University

Nowadays, the UJW educational unit is trying to be competitive as a local
experimental HEI. Therefore, the educational offering relates to regional
needs especially strongly connected with the copper industry and other
companies located in the region. UJW was a leader of Erasmus grants devoted to
improving the educational level through applying new teaching methods using AI
(e.g. project DIMBI [92] and related [93]–[96]). Therefore all potential
solutions in this HEI are strictly connected with the results of these
projects extended by discovered niches in education. Three directions of AI
techniques to embrace are:

  1. 1.

Using innovative methods of teaching selected courses. At the beginning,
practical abilities and skills refer to the data science and mechatronic
majors.

  2. 2.

Smart agents (SAs). SAs are used to automate administrative procedures with
the aim to simplify daily tasks.

  3. 3.

Chatbots. Implementation of chatbots for communication with actual and
potential students. This project is comparable to that presented in the
previous universities.

Educating staff for the constantly changing trends and creating innovations
both in the area of education and the functioning of a private educational
unit translates into profit and brand visibility and the prestige of
education. The search for creative solutions is the equation between the
known, acceptable order and the chaos that innovation can bring. However, if
innovation is treated as a course of action, as a way of managing, without
worrying about disturbing relative stability, after a period of time, it can
be seen that it has marked out new directions and possibilities of creating a
competitive advantage.

To sum up, we think that the automation of a universities’ administrative
tasks and customization of their student-oriented activities are not only
possible, but imminent. The goal of AI technologies is to make human-like
judgments and perform tasks in order to downsize employees’ workloads. Today,
academic communities are intensively developing and studying this field of AI
applications. Indeed, the findings from research commissioned by the Microsoft
show almost complete acceptance among educators that AI is important for their
future— “99.4 percent said AI would be instrumental to their institution’s
competitiveness within the next three years, with 15 per cent calling it a
game-changer” [97].

However, in this paper, including the assumptions of the first pilot study, we
“only” considered selected administrative areas, performed by the employees
not directly responsible for education. Nevertheless, we believe that AI has
far-reaching potential to change the way of teaching and learning. Undeniably,
the incoming shift has its advocates and opponents whose proofs and claims
should be always carefully judged.

##  5 Conclusions

As the technologies of artificial intelligence evolved, so did the domains and
practices of their implementation in education. Current trends have imposed
new requirements on the organization and management of both teaching and
learning. There are three interrelated aspects of this, one of which arises
from the recent advancements and innovations of the cutting-edge machine
learning methods and Internet-of-Things devices.

Secondly, the focus of both teachers and learners concern very large volumes
of information and knowledge resources, freely available on the Web. Their
growth in size and number seems to be an endless road to discover, but there
is more and more evidence to help us pick the right direction. Thirdly,
currently the competitiveness of higher educational institutions depends
strongly on the increase in the effectiveness of learning methods, strongly
supported by AI technologies and tools [98]–[100].

At a time when many education entities are stretched to capacity, and learners
experience long wait times for on-site counseling, AI solutions could provide
some facilities. It is therefore recommended that organizations should use
solutions that are supported by the latest technological solutions, in order
to improve the quality of education, and to minimize errors in the circulation
of administrative documentation and the course of study.

To sum up, we argue that the role and impact of artificial intelligence has
increased in the education and learning contexts. The academic sphere is
becoming more effective and personalized on the one hand, as well as global,
context-intensive (multi-cultural) and asynchronous on the other. The
intersection of three areas, namely data, computation and education has set
far-reaching consequences, raising fundamental question about the nature of
teaching: what is taught, when it is taught, and how it is taught.

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