Next Article in Journal
Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data
Previous Article in Journal
Development of Risk Quantification Models in Road Infrastructure Projects
Previous Article in Special Issue
Will AI Become a Threat to Higher Education Sustainability? A Study of Students’ Views
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 17
10.3390/su16177695
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education

by
Valery Okulich-Kazarin
1,2,*,
Artem Artyukhov
3,4,5,
Łukasz Skowron
6,
Nadiia Artyukhova
4,5 and
Tomasz Wołowiec
3
1
Faculty of Social Sciences and Humanities, Humanitas University, 41-200 Sosnowiec, Poland
2
Faculty of Social and Computer Sciences, National Louis University, 33-300 Nowy Sącz, Poland
3
Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland
4
Faculty of Commerce, University of Economics in Bratislava, 852-35 Bratislava, Slovakia
5
Academic and Research Institute of Business, Economics and Management, Sumy State University, 40-007 Sumy, Ukraine
6
Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7695; https://doi.org/10.3390/su16177695
Submission received: 9 July 2024 / Revised: 26 August 2024 / Accepted: 3 September 2024 / Published: 4 September 2024
(This article belongs to the Special Issue Sustainable Higher Education: From E-learning to Smart Education)
Browse Figures
Versions Notes

Abstract

:
This paper continues the series of publications of our interdisciplinary research findings at the crossroads of higher education sustainability (SDG 4.3), smart education, and artificial intelligence (AI) tools. AI has begun to be used by universities to increase the quality of higher educational services. AI tools are expected to help university teachers in the teaching process. Students also use AI to help them complete their tasks. At the same time, AI may threaten Sustainable Development Goal 4 (SDG 4). In particular, this is a “blank spot” in the study of AI and non-violent learning environments (SDG 4.3). The aim of the study was to verify competing statistical hypotheses. To achieve this aim, the authors used modern, economically sound methods. The authors processed the responses of 1102 students from eight Eastern European universities using a special electronic questionnaire. The authors statistically processed the student survey results and then tested a pair of conflicting statistical hypotheses. The authors adopted a standard level (α = 0.05) of hypothesis checking. Testing statistical hypotheses led to obtaining two statistically substantiated new scientific facts: (1) The requirement for “non-violent” learning environments does not meet some students’ needs. (2) The number of these students can be up to 31.94%. Summary: The new scientific facts are helpful for further developing world pedagogical theory and practice. They are the basis for forecasting and preparing for managerial actions aimed at SDG 4.3.

1. Introduction

Sustainability in higher education is becoming increasingly urgent due to the beginning of intensive use of artificial intelligence (AI) in educational processes. In this manuscript, the authors tried to comprehensively study such an aspect of Sustainable Development Goal 4.3 as “non-violent” learning environments in higher education. The authors emphasized the combination of ‘non-violent’ learning environments with smart education and AI.
A “non-violent” learning environment is challenging to measure directly. However, its study is possible by measuring an indirect sign. Therefore, the authors turned to students’ opinions about the impact of AI tools on a single and indivisible educational process.
In the first article of Special Issue [1], its authors write about the sustainability of higher education by moving from e-learning to smart education. Indeed, after the COVID-19 pandemic, e-learning has become commonplace in higher education [2,3,4,5,6,7,8].
As the starting point of the movement [1], “e-learning” has been developed in various countries from East to West: Australia [9], Malaysia [10], China [11], Japan [12], India [2,13], Afghanistan [3], Palestine [4], Saudi Arabia [14], Ukraine [7], Belgium [15], Spain [16], Kenya [17], Ghana [18], the USA [19], and Canada [20], etc. E-learning is actively used in the 21st century (from the early 2000s [9,12,13] to 2023 [2,3,4,5]). Remote and rural areas in developing countries are not obstacles to the spread of e-education [13,17,18]. The influence of socio-economic and technical conditions, age, and gender on various aspects of the use of e-learning has been carefully studied in the mentioned [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] and other publications.
In a previous paper [21], the authors published the first results regarding AI and “safe” learning environments. This new manuscript continues our work on studying the role of AI in the educational process and is devoted to studying students’ opinions about the “non-violent” learning environment.
The authors of this manuscript explored the conditions of the current challenge of sustainability in higher education. The authors presented a logically complete and empirically grounded manuscript. The manuscript highlights new facts about sustainability.
The study explored “non-violent” learning environments (SDG 4.3) in light of smart education and AI tools. The aim of the study was to verify competing statistical hypotheses.

1.1. Bibliometric Analysis

To establish the place and role of AI in a non-violent learning environment, let us turn to the results of bibliometric analysis. The dataset for bibliometric analysis is a set of articles for the query (keyword) “non-violent”, which are indexed by the Scopus database (https://www.scopus.com/, accessed on 1 July 2024).
The first stage of bibliometric analysis was carried out using SciVal, an incorporated software from the Scopus database (https://www.scival.com/, accessed on 1 July 2024).
Initial data:
  • Number of articles—3625.
  • Field of knowledge—all branches.
  • Publication type—all types.
  • Publication period—2019–2023 (2023 data as of the date of access to the Scopus database).
  • Total number of topic clusters generated by SciVal—476.
Table 1 presents the main topic clusters.
Figure 1 presents a map of topic clusters.
Analysis of topic clusters allowed us to trace the following patterns concerning the basic keyword of this article:
  • The keyword “non-violent” relates to education and SDG 4. The search results show the presence of about 20 clusters related to education topics.
  • The maximum value of the popularity percentile for the studied clusters is 80, and the minimum is 15. Popular clusters are devoted to various aspects of the student-teacher relationship. The cluster percentile values and the increase in percentiles show that the topic of non-violent learning environments is gaining popularity.
  • About ten query “non-violent” clusters are related to artificial intelligence. However, the study of artificial intelligence is limited only to the technical component (methods, tools, and software). It does not focus on the ideological component (the role of AI in students’ lives).
  • In the clusters, there are no traces of assessing students’ opinions regarding the characteristics of a non-violent learning environment. Thus, studying the students’ opinions regarding the non-violent learning environment is necessary.
  • Among the clusters for the request “non-violent” are those associated with student development in the educational field. This fact indicates the need for additional study of the role of AI in the non-violent learning environment and its prospects.
  • Clusters on the request “non-violent”, which are related to educational topics, study this problem mainly in the field of knowledge “Social sciences”. This fact emphasizes the importance of the ideological (not technical) component of the relationship between AI and stakeholders in the educational process.

1.2. Construct Review

The endpoint in this movement—“smart education” [1]—is more complex. First of all, many definitions are divided into “smart learning space” [22,23,24,25], “smart learning” [26,27,28], “smart learning technologies” [29,30,31] and “smart education” [32,33,34,35,36,37,38,39,40].
“Smart learning” is ‘a new learning paradigm which serves learners …’ [27]. European researchers identify smart learning as “digital education” [1]. Specifically, text-free smart learning refers to technology-enabled learning that uses non-text features and tailored learning materials based on human needs [26]. The understanding of smart learning has different interpretations in different cultures [21].
“Smart education” is described in [34]. In an earlier article, “smart education” is defined as ‘an educational system that allows students to learn using up-to-date technology and enables students to study with various materials based on their aptitudes and intellectual levels’ [39]. R. Bajaja and V. Sharma understand “smart education” more narrowly (personalized learning … using AI) [21]. In the source [22], the authors mention “smart education” but do not define this term.
According to article [38], “the goal of smart education is to foster smart learners…”. Other researchers write that “the essence of smart education is to create intelligent environments…” [21]. Smart education provides new learning opportunities for people with disabilities [41]. The author of article [34] draws attention to the fact that smart education is not only technology. Article [34] demonstrates “smart education” as a system of educators, technologies, and learners. Source [38] also describes a scheme where “smart education” includes “learners”. So, two sources associate “smart education” with “learners” [34,38]. Another source connects “smart education” with students [39].
A brief review of the issue showed that the sustainability of higher education can be associated with e-learning and smart education [1]. Smart education deals with new educational technologies (including AI tools) [1,21,34,42,43,44,45,46,47]. Smart education also improves the conditions for personalized learning [41], which connects educational technologies, including AI tools, with learners [34,38]. And the United Nations proposes an international definition of SDG 4 [21,48]. SDG 4 includes ‘equal access to affordable … higher education (4.3)’ [21,48].
One of the three enablers of SDG 4 is “Effective learning environments”. It involves “non-violent learning environments” (https://campaignforeducation.org/en/key-frameworks/, accessed on 14 June 2024).
Returning to “smart education”, the authors note that it improves the conditions for personalized, inclusive learning [41]. Examples of the use of AI in smart education include learning analytics [49,50], educational data mining [51], and early warning systems for pedagogical problems [52]. Works [41,49,50,51,52] show that AI quickly provides new teaching methods, adapted curricula, and even modified assessment methods [53,54]. The authors of studies [34,41] proved that personalized learning provides more effective learning for learners.
R. Bajaja and V. Sharma write that the use of AI satisfies the condition of “personalized learning” in the narrow sense of “smart education” [21]. AI fulfills the condition “any place, any time” as an element of modern digital learning [55,56], forming the necessary skills of learners [57,58].
To summarize the above [1,21,41,49,50,51,52,53,54], we note that AI has become an integral part of the functioning of educational organizations. AI helps personalize learning by tailoring programs to the individual needs of students. Machine learning algorithms help analyze training data, identify trends, and provide personalized recommendations to improve learning efficiency. Thanks to AI, educational institutions can create interactive learning platforms, promoting the development of critical thinking among learners. So, AI satisfies the “inclusive environment” condition. Testing the “safe” learning environment when using AI tools is not the purpose of this manuscript [21].
Therefore, in this manuscript, the authors tested when AI tools meet the requirements of a “non-violent” learning environment (SDG 4.3). This question is open in world pedagogical theory and practice.
Suppose students have a negative attitude towards using AI in teaching. You cannot conclude that the “non-violent” environment condition is met. Indeed, despite the positive expectations, the following concerns can be expressed:
  • The first concern may be the potential loss of personal interaction between university teachers and learners. Students may fear that automated learning systems may limit opportunities for communication, sharing ideas, and learning soft skills, which are also crucial in shaping education.
  • The automation of certain aspects of teaching may cause job losses for teachers, especially in routine tasks, which may entail the need for new skills and adaptation to the professional community. However, as the author’s research shows [59], students do not consider this option as likely over the next five years.
  • “Matthew effect”. If access to AI tools is uneven, students from less affluent backgrounds or regions may face additional challenges. This may create a digital divide where some students have more significant opportunities to use high-tech educational resources than other students, resulting in the failure to meet the requirements of other SDGs, such as SDG 4.5.
These fears can be both real and imagined. For example, the loss of interaction with a teacher is not felt as acutely because online learning has become commonplace in the pandemic era [2,3,4,5,6,7,8].
Thus, by surveying students about their attitudes towards AI, the authors check the compliance of the “non-violence” condition with the possibility of achieving SDG 4.3.

1.3. Connection: “Smart Education (Approach)–AI (Technical Tools)–Student (Stakeholder)–Survey (Feedback Tool)”

A logical connection: “smart education (approach)–AI (technical tool)–student (stakeholder)–survey (feedback tool)” is built in Figure 2.
The main links of this chain are studied in works devoted to:
-
The concept of “smart education” as a breakthrough technology and the role of AI in it [60,61,62,63,64];
-
AI in solving educational problems and beyond [65,66,67,68,69];
-
optimization of the process of “smartization” of educational activity using AI [35,44,70,71,72,73];
-
Decision-making and studying behavioral reactions when introducing AI as an element of smart education [41,42,74];
-
Students in the smart education model and their attitude towards AI [33,43,75,76].

1.4. Summarizing

  • Some important definitions of our manuscript are:
    1.1
    “Non-Violent” learning environments are an educational space where students can freely express their thoughts, ideas, and feelings without fear of being subjected to physical or emotional violence (SDG 4.3).
    1.2
    Smart education provides personalized learning using AI anywhere, anytime.
    1.3
    Artificial intelligence tools for teachers are software programs and platforms that use artificial intelligence technologies to enhance the teaching and learning experience.
  • “Non-violent” learning environments (SDG 4.3) may be considered together with e-learning, smart education, and AI tools. Students’ achievements, i.e., learners, are the ultimate goal of smart education [34,38]. Smart education and AI tools are associated with each other [34,37,41,42,43,44,45,49,50,51,52,53,54].
  • AI and its capabilities in the smart education concept are the subject of technical research and the search for optimal software solutions. Assessing the attitudes of stakeholders (learners) towards AI is essential to optimizing the interaction between AI and stakeholders (learners).
  • Introducing AI tools in educational services has created gaps for global pedagogical theory and practice. Studying the attitudes of stakeholders (learners) towards AI will enrich global pedagogical theory and practice in verifying the requirements of a “non-violent” learning environment (SDG 4.3).
Thus, the study is relevant.
In practical terms, the value of the study is in obtaining statistically sound, completely new empirical datasets. Rapid implementation of artificial intelligence in business, society, politics, and education drives the need for further empirical data. Based on the normative and target documents of the SDGs, one condition of SDG 4.3 was considered. The authors have analyzed Eastern European university students’ opinions on SDG 4.3. This topic is: How do you feel about using artificial intelligence in the teaching process?
Testing statistical hypotheses helped to obtain statistically substantiated new scientific facts about student opinions. The statistically substantiated new scientific facts received by the authors are the starting point for monitoring student attitudes on a given topic.
A practical relevance of our research is underlined by the United Nations’ interest in AI tools (https://news.un.org/en/story/2023/10/1142867, accessed on 12 June 2024).
The authors verified competing statistical hypotheses but did not consider random deviations.
Research Hypothesis: the number of students with a negative attitude towards using AI in the teaching process is zero. This means that there are no students who have a negative attitude towards the use of AI in teaching. In other words, students are generally positive or neutral about using AI in the teaching process. We will take these results as confirmation that the “non-violent” environment requirement is met.
Alternative Hypothesis: the number of students with a negative attitude towards using AI in the teaching process is greater than zero. This means some students have a negative attitude towards using AI in teaching.
Research Hypothesis: μ0 = 0.00%.
Alternative Hypothesis: μ0 > 0.00%.
The two statistically substantiated new scientific facts can enrich global pedagogical theory and practice in verifying compliance with the requirements of a “non-violent” environment for SDG 4.3. They help to better predict, on a rigorous scientific basis, changes in student behavior in higher education.
The complex of studies carried out by the authors led to the following main summaries:
  • “Non-violent” learning environments may be considered together with e-learning, smart education, SDG 4.3, and AI tools.
These are two statistically substantiated new scientific facts that link smart education, AI tools, and SDG 4.3:
(1)
Some students do not meet the “non-violent” learning environment requirements.
(2)
The number of these students can be up to 31.94%.
  • These two statistically substantiated new scientific facts are helpful for generalization, comprehension, and further development of world pedagogical theory and practice.
  • Under the guidance of experienced managers, managerial actions aimed at SDG 4.3 should be developed to ensure “non-violent” learning environments.

2. Materials and Methods

2.1. Common Description

The study was conducted from February 2023 to June 2024 at 8 Polish, Kazakh, Slovak, and Ukrainian universities (Table 2). The general design of the study was to explore the relationship between “non-violent” learning environments (SDG 4.3 [77,78]), smart education, and AI tools, as well as empirically test the bottlenecks in the existing relationship (if any). Conceptually, the authors decided to test in countries with non-zero (low and medium) total enrollment rates in higher education [79].
The countries are numbered in Table 2: 1—Kazakhstan; 2–4—Poland; 5—Slovakia; 6–8—Ukraine.
The description of the respondents (Table 2) will be used in Section 2.3.
The authors have formulated and explored the pair of competing hypotheses.
The study was carried out using cost-effective and reliable research methods [80,81]:
  • Study and analysis of scientific sources and documents for building a theoretical framework for the research;
  • Bibliometric multi-step analysis;
  • Questioning of students using an electronic questionnaire hosted in the Cloud of National Louis University as an empirical part of the research on SDG 4.3 (sustainability in higher education);
  • Formal processing and graphical visualization of questioning results based on standard tools;
  • Verification of statistical hypotheses through standard tools.
The approach to conducting bibliometric analysis (VOSviewer, https://www.vosviewer.com, Scopus database add-on for bibliometric analysis, and SciVal, https://www.scival.com, accessed on 1 July 2024) is described in [21,59].

2.2. Questioning of Students

The Eastern European market of educational services was selected for the study [21,59,82,83,84,85,86]. Countries were chosen to ensure maximum diversity. Three of these countries are a part of Eastern Europe, and one has a part in Eastern Europe [21,59]. The universities were both public and private. So, such a combination of countries and universities fulfills the condition of maximum socio-cultural diversity.
Students were invited to participate in the survey by sending letters to their email addresses.
As the authors showed above, the survey was conducted for students not involved in professional AI studies. Any mass study of AI in educational programs was an exception rather than a rule. Students of AI-related specialties are surveyed. However, the analysis of their answers is not planned at this study stage.
The authors have created a questionnaire according to standard requirements [80]. It includes (Table 3): (1) appeal to respondents, (2) metrics, and (3) body.
Some methodological provisions of the questioning were published in sources [59,80]. In the first part, we informed the respondents about the anonymous and voluntary participation in the interview (Table 3).
The body included 12 questions. We addressed the central question aimed at the requirements of “non-violent” learning environments (Table 3): 6. How do you feel about using artificial intelligence in the teaching process?
Respondents could choose one of five answers [87]:
  • Definitely positively;
  • Rather positively;
  • Hard to say;
  • Rather negatively;
  • Definitely negatively.
The questionnaire was completed electronically. The questionnaire was hosted in the National Louis University cloud. The authors generated a separate questionnaire for each group of students from Table 2.
All students gave answers voluntarily, without any additional motivation, including payments. The survey was also anonymous. The voluntariness and anonymity of the survey were declared in the introductory part of the questionnaire and in the invitation letter to participate in the survey (Table 3).
In formal processing, the authors summarize the first two answer options in further calculations. These options are considered a positive answer. The last two answers are summed up and considered a negative answer. The middle option is regarded as a neutral opinion. We used the methodology described in the source [88] to calculate statistical indicators.

2.3. Respondent Groups

To provide a reliable comparison, all respondents were undergraduate students. All respondents did not professionally study AI tools.
For the empirical part of the study, sequential (nested) sampling was used [80]. The authors aimed for maximum diversity when selecting groups. Table 2 shows a common description of the respondents.
There were 1104 respondents (Table 2) from 8 groups of students from 4 countries. There were 312 men and 788 women. Only four respondents reported an “other” gender identity.
The age of the participants is 18–64 years old. Only undergraduate students took part in the survey.

2.4. Methodology of Verification of Statistical Hypotheses

The methodology for verifying statistical hypotheses is borrowed from well-known sources [80,81]. Some verification details concerning this study are described in papers [21,59].
T-statistics was used for calculation [80,81] with a standard checking level (0.05) in all tests. A one-sided test for a pair of hypotheses was chosen [80,81].
After discussing the results, it became possible to summarize and write a conclusion.

3. Results

3.1. Analysis of Answers to the Question: How Do You Feel about Using Artificial Intelligence in the Teaching Process?

The respondents’ choices for this question are shown in Table 4. N denotes the total number of answers.
Table 4 demonstrates 1102 replies from 1104 students. This ratio does not affect the quality of the result.
Table 4 shows that respondents’ answers differ among different groups. At first glance, positive choices outweigh negative ones. Interestingly, the number of choices for the “Definitely negatively” answer is zero in one group of respondents.
The results are visualized in Figure 3.
Figure 3 confirms that positive choices of respondents prevail over negative ones. In groups 2, 3, 5–8, positive choices dominate the sum of neutral and negative choices. The negative selections vary from 5% in group 5 to 32% in group 1. Do you know whether this number of negative selections results from random deviations or objective reasons? We need to verify the statistical hypotheses to obtain the correct answer.

3.2. Verification of Statistical Hypotheses: The Number of Students with a Negative Attitude towards Using AI in the Teaching Process Is Zero

Table 5 demonstrates statistical indicators. Negative responses like “Rather negatively” and “Definitely negatively” were set to 1.0, and the other responses were set to 0.0 [87,88].
Table 5 demonstrates the M(x) values range from 4.92% to 31.94%. Data on verification of statistical hypotheses for each group of respondents are shown in Table 6.
Verifying our two statistical hypotheses (Table 6) demonstrates that the t-statistics |tstat| is more than the ttabl for every group. Thus, the Alternative Hypothesis was accepted.

4. Discussion

The “learners” survey is an essential tool of the “service economy” [89,90,91,92,93,94]. These works [82,89,90,91,92,93,94] emphasize that the consumer (in our case, “learners” or students) plays an important role. If university teachers and students are recognized as two sides of educational services, the importance of the study is beyond doubt.
Smart education deals with AI tools and other new educational technologies [1,21,34,42,43,44,45,46,47,59]. Smart education also improves the conditions for personalized learning [41]. This connects educational technologies, for example, AI tools, with learners [34,38].
The quality of education and approaches to organizing the educational process with maximum involvement of students in assessment activities may also contain different approaches to the use of AI. As data from articles [95,96] show, information systems can be successfully used in developing solutions for quality control, quality assurance, and testing elements of quality systems, which opens up additional prospects for using AI in educational organizations. In the context of the service economy, the quality of education is crucial as a factor in socio-economic development [97,98], digitalization of education [93,99,100], strategies for using AI [94,101,102], etc.
When students have negative attitudes towards using AI in the teaching process, you cannot conclude that “non-violent” environment requirements are met (Section 1).
The values of M(x) for students with a negative attitude towards using AI in the teaching process range from 4.92% to 31.94% (Table 4).
At a standard verification level (0.05), the Alternative Hypothesis was accepted: the number of students with a negative attitude towards using AI in the teaching process is greater than zero. The study showed that students with a negative attitude toward using AI in teaching could range from 4.92% to 31.94%. We have a statistically substantiated new scientific fact that up to about 1/3 of the number of students are (maybe) under the pressure of AI tools towards which they have a negative attitude. Some students do not meet the conditions of a “non-violent” learning environment. There may be up to 31.94% of such students.
The results are the reason for managerial actions aimed at “non-violent” learning environments (SDG 4.3).
This manuscript continues the research that began in [59]. Table 7 compares the previous [59] and current results.
Table 7 shows the boundaries within which statistical indicators lie. The difference in M(x) is about 25.00%. However, the students from paper [59] fall within the range of M(x) variation for the problem studied in this manuscript. The value of δx from [59] also falls within the range of δx for the problem studied in this manuscript. The new datasets correlate with datasets published in [59]. The opinions of “learners” were also studied in articles [20,21,26,33,44,59,75,83,103,104,105]. However, the authors of articles [20,21,26,33,44,75,83,103,104] do not provide comparable statistical data.
This study has limitations. First, the authors surveyed respondents from Eastern European universities. It is helpful to survey students at other universities from regions with low overall higher education enrollment rates [79]. Second, the authors did not take managerial actions aimed at “non-violent” learning environments (SDG 4.3). The new data are the basis for forecasting and preparing such actions. Third, the authors have left the analysis of demographic characteristics for one of the future manuscripts.
So, in this paper, the authors studied the conditions of “non-violent” learning environments.
“Non-violent” learning environments contribute to the formation of a positive image of educational institutions and create a favorable educational environment for all participants in the process. Social sustainability can be improved by forming more harmonious relationships in educational groups and developing tolerance and respect for others.
The author’s new facts can become the basis for forecasting and preparing managerial actions for “non-violent” learning environments. Such activities should be undertaken under experienced managers’ guidance in close cooperation between scientists and politicians, government members, and university leaders.
Introducing “non-violent” learning environments will help reduce the fear of students in higher education. This contributes to the effective assimilation of knowledge. Thanks to this, education becomes more accessible, which contributes to improving education quality and public literacy.
In a previous paper [21], the authors studied the “safe” learning environment. The condition of an “inclusive and effective” learning environment remained open for study from the point of view of implementing SDG 4.3.
The integration of AI tools in educational settings presents both opportunities and challenges when it comes to creating non-violent learning environments that align with SDG 4.3. As we explore this crossroads with smart education, it is crucial to consider how AI can contribute to ensuring equal access to affordable and quality technical, vocational, and higher education for all.
  • Personalized learning experiences.
AI tools have the potential to revolutionize education by providing personalized learning experiences tailored to individual needs. By adapting content and pacing to each student’s strengths and weaknesses, AI can enhance engagement, comprehension, and academic success. This personalization aligns with the principles of non-violent learning environments, as it promotes inclusivity and respects the unique needs of each learner.
2.
Automated grading and feedback.
AI-powered grading systems and real-time feedback mechanisms can significantly reduce the workload on educators, allowing them to focus more on teaching and mentoring students. By providing instant feedback and continuous assessment, AI tools can help students identify areas for improvement promptly, fostering a growth mindset and resilience. This automated support contributes to a more harmonious learning environment by reducing stress and burnout among teachers.
3.
Democratization of education.
AI technologies have the potential to break down geographical and socio-economic barriers, providing access to high-quality educational resources for all students, regardless of their location or background. This democratization of education aligns with the goals of SDG 4.3, as it ensures that every learner has the opportunity to succeed and contribute to a more equitable society.
4.
Ethical considerations and data privacy.
While the benefits of AI in education are substantial, ethical considerations regarding data privacy and algorithmic bias must be addressed. Ensuring that AI tools are implemented responsibly is crucial to maintaining trust and safeguarding the rights of students. Educational institutions must prioritize ethical guidelines and transparent practices to mitigate risks associated with AI use, such as perpetuating biases or infringing on individual privacy.
5.
Balancing technology and human interaction.
The successful integration of AI in education hinges on finding a balance between technology and traditional teaching methods. While AI can enhance learning, it should not replace the essential human elements of teaching, such as empathy, mentorship, and social interaction. Maintaining this balance is vital for fostering a non-violent, collaborative learning environment where students feel supported and valued as individuals.
6.
Preparing students for the future.
As AI continues to evolve, educational institutions must prepare students for a future where technology plays an increasingly integral role. By incorporating AI into curricula, schools can equip learners with the skills necessary to navigate a technology-driven world, fostering adaptability, critical thinking, and digital literacy. This preparation aligns with the principles of non-violent education, as it empowers students to become active and engaged citizens in a rapidly changing global landscape.
The intersection of AI tools and non-violent learning environments presents both challenges and opportunities for creating equitable and inclusive educational systems that align with SDG 4.3. By embracing the potential of AI while addressing ethical concerns and prioritizing human interaction, educational institutions can enhance personalized learning, reduce workloads for educators, and democratize access to quality education. However, it is crucial to maintain a balance between technology and human elements, ensuring that AI tools complement and support, rather than replace, the essential human aspects of teaching and learning. As we navigate this crossroads, collaboration among educators, policymakers, and technologists will be vital to harnessing the full potential of AI in creating a more just, peaceful, and sustainable future for all learners.

5. Conclusions

Integrating AI in educational settings significantly shifts how learning environments are structured and experienced. As we navigate the crossroads of smart education, several key conclusions can be drawn regarding the role of AI in fostering non-violent, constructive learning environments.
Personalized learning experiences: AI tools facilitate personalized learning by adapting educational content to meet students’ individual needs. This customization enhances engagement and comprehension, allowing learners to progress at their own pace. AI promotes a more inclusive and supportive educational atmosphere by focusing on each student’s unique strengths and weaknesses.
Enhanced accessibility: AI technologies break down geographical and socio-economic barriers, providing all students access to high-quality educational resources. This democratization of education fosters equity and ensures that every learner has the opportunity to succeed, regardless of their background.
Improved administrative efficiency: Automating administrative tasks through AI allows educators to dedicate more time to teaching and mentoring students. This shift enhances the quality of education and reduces teacher stress and burnout, contributing to a healthier, more positive learning environment.
Real-time feedback and continuous assessment: AI systems enable continuous assessment and instant feedback, helping students identify areas for improvement promptly. This ongoing support encourages a growth mindset and fosters resilience, as learners can adjust their approaches based on immediate insights.
Ethical considerations and data privacy: While the benefits of AI in education are substantial, ethical considerations regarding data privacy and algorithmic bias must be addressed. Ensuring that AI tools are implemented responsibly is crucial to maintaining trust and safeguarding the rights of students. Educational institutions must prioritize ethical guidelines and transparent practices to mitigate risks associated with AI use.
Balancing technology and human interaction: The successful integration of AI in education hinges on finding a balance between technology and traditional teaching methods. While AI can enhance learning, it should not replace the essential human elements of teaching, such as empathy, mentorship, and social interaction. Maintaining this balance is vital for fostering a non-violent, collaborative learning environment.
Preparation for future challenges: As AI evolves, educational institutions must prepare students for a future where technology plays an increasingly integral role. By incorporating AI into curricula, schools can equip learners with the skills necessary to navigate a technology-driven world, fostering adaptability and critical thinking.
The intersection of AI tools and non-violent learning environments presents opportunities and challenges. By embracing the potential of AI while addressing ethical concerns and prioritizing human interaction, educational institutions can create dynamic, inclusive, and compelling learning experiences that prepare students for success in the 21st century. The thoughtful implementation of AI in education can lead to transformative outcomes, ultimately fostering a smarter, more equitable future for all learners.
SDG 4.3 may be considered together with e-learning, smart education, “non-violent” learning environments, and AI tools. Assessing the attitude of stakeholders (learners) towards AI is essential for optimizing students’ use of AI tools and further improving global pedagogical theory and broad pedagogical practice.
The study of student attitudes toward AI tools in higher education institutions led to two statistically substantiated new scientific facts. These two facts link together smart education, AI tools, “non-violent” learning environments, and SDG 4.3:
-
Some students do not meet the requirements of “non-violent” learning environments.
-
The number of these students can be up to 31.94%.
The two statistically substantiated new scientific facts are helpful for the generalization, comprehension, and further development of world pedagogical theory and practice.
Future research goals may be to evaluate the impact of AI on the “effective” learning environments (SDG 4.3). Also, we would like to evaluate the research limitations.

Author Contributions

Conceptualization, V.O.-K., A.A. and T.W.; methodology, V.O.-K., T.W. and Ł.S.; software, V.O.-K., A.A. and N.A.; validation, V.O.-K., T.W. and A.A.; formal analysis, V.O.-K., A.A., T.W. and N.A.; investigation, V.O.-K. and A.A.; resources, N.A. and T.W.; data curation, V.O.-K.; writing—original draft preparation, V.O.-K., A.A., N.A., T.W. and Ł.S.; writing—review and editing, V.O.-K., Ł.S. and T.W.; visualization, V.O.-K. and A.A.; supervision, V.O.-K. and Ł.S.; project administration, V.O.-K., T.W. and Ł.S.; funding acquisition, T.W. and Ł.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Education and Science of Ukraine “Modeling and forecasting of socio-economic consequences of higher education and science reforms in wartime” (No. 0124U000545) and by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V01-00130.

Institutional Review Board Statement

Experimental studies in this article do not require approval from the Review Board.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors warmly thank the students for their time and honest answers. The authors also thank their colleagues who helped organize the survey of respondents. The authors would like to thank the reviewers for their advice, which helped significantly improve the quality of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Parusheva, S.; Bobek, S.; Zabukovšek, S.S. Sustainable Higher Education: From E-Learning to Smart Education. Sustainability 2023, 15, 10378. [Google Scholar] [CrossRef]
  2. Sharma, B.K.; Kumar, V.V.R.; Bhatt, V.K. Factors Influencing E-learning Technology Among Youth in India: An Extended TAM Model. Manag. Labour Stud. 2023, 49, 504–526. [Google Scholar] [CrossRef]
  3. Monib, W.K. Afghan Undergraduate Students’ Perceptions Toward E-learning. E-Learn. Digit. Media 2023. [Google Scholar] [CrossRef]
  4. Ismail, A.; Ismail, A.; Alazar, A.; Saman, M.; Abu-Elqomboz, A.; Sharaf, F.K. E-Learning Medical Education in Gaza During COVID-19: Students’ Experiences and Policy Recommendations. J. Med. Educ. Curric. Dev. 2023, 10. [Google Scholar] [CrossRef] [PubMed]
  5. Mensah, M.S.; Arthur, K.N.; Mensah-Williams, E. Antecedents of E-learning in Undergraduate Entrepreneurship Education. E-Learn. Digit. Media 2023, 21, 496–516. [Google Scholar] [CrossRef]
  6. Gurban, M.A.; Almogren, A.S. Students’ Actual Use of E-Learning in Higher Education During the COVID-19 Pandemic. SAGE Open 2022, 12. [Google Scholar] [CrossRef]
  7. Durrani, N.; Ozawa, V. Education in Emergencies: Mapping the Global Education Research Landscape in the Context of the COVID-19 Crisis. Sage Open 2024, 14. [Google Scholar] [CrossRef]
  8. Ceesay, L.B. Learning Beyond the Brick and Mortar: Prospects, Challenges, and Bibliometric Review of E-learning Innovation. Jindal J. Bus. Res. 2021, 10, 33–48. [Google Scholar] [CrossRef]
  9. Lee, K.-T.; Duncan-Howell, J. How Do We Know E-Learning Works? Or Does it? E-Learn. Digit. Media 2007, 4, 482–496. [Google Scholar] [CrossRef]
  10. Moy, F.M.; Ng, Y.H. Perception towards E-learning and COVID-19 on the mental health status of university students in Malaysia. Sci. Prog. 2021, 104. [Google Scholar] [CrossRef]
  11. Wang, Y.; Liu, X.; Zhang, Z. An Overview of E-learning in China: History, Challenges and Opportunities. Res. Comp. Int. Educ. 2018, 13, 195–210. [Google Scholar] [CrossRef]
  12. Sakamoto, T. E-Learning in Japanese Universities. Ind. High. Educ. 2003, 17, 397–404. [Google Scholar] [CrossRef]
  13. Kumar, A. E-Learning: A Tool for Education in Rural India. Asia Pac. Bus. Rev. 2007, 3, 113–122. [Google Scholar] [CrossRef]
  14. Aljaber, A. E-learning Policy in Saudi Arabia: Challenges and Successes. Res. Comp. Int. Educ. 2018, 13, 176–194. [Google Scholar] [CrossRef]
  15. De Vliegher, K.; Paquay, L.; Cordyn, S.; Heirstrate, L.; Goense, E.; Pechon, S.; Labalue, J.; Putzeys, D. Home Nurses’ Opinions Toward E-Learning in Continuing Education. Home Health Care Manag. Pract. 2015, 27, 216–223. [Google Scholar] [CrossRef]
  16. Alonso-Díaz, L.; Yuste-Tosina, R. Constructing a Grounded Theory of E-Learning Assessment. J. Educ. Comput. Res. 2015, 53, 315–344. [Google Scholar] [CrossRef]
  17. Mutisya, D.N.; Makokha, G.L. Challenges Affecting Adoption of E-learning in Public Universities in Kenya. E-Learn. Digit. Media 2016, 13, 140–157. [Google Scholar] [CrossRef]
  18. Ansong, E.; Lovia Boateng, S.; Boateng, R. Determinants of E-Learning Adoption in Universities: Evidence From a Developing Country. J. Educ. Technol. Syst. 2017, 46, 30–60. [Google Scholar] [CrossRef]
  19. Costarides, M.V. Knowledge Management and E-Learning. Health Promot. Pract. 2014, 15, 790–794. [Google Scholar] [CrossRef]
  20. Murphy, E.; Rodríguez-Manzanares, M.A. High School Teachers’ Beliefs about Learner-Centred E-Learning. E-Learn. Digit. Media 2008, 5, 384–395. [Google Scholar] [CrossRef]
  21. Okulich-Kazarin, V.; Artyukhov, A.; Skowron, Ł.; Artyukhova, N.; Wołowiec, T. Will AI Become a Threat to Higher Education Sustainability? A Study of Students’ Views. Sustainability 2024, 16, 4596. [Google Scholar] [CrossRef]
  22. Sui, C.; Xie, Y. The Effect of Smart Campus on the Low Infection Rate in the Post-pandemic Era. Sci. Prog. 2023, 106. [Google Scholar] [CrossRef] [PubMed]
  23. Phoong, S.Y.; Phoong, S.W.; Moghavvemi, S.; Sulaiman, A. Effect of Smart Classroom on Student Achievement at Higher Education. J. Educ. Technol. Syst. 2019, 48, 291–304. [Google Scholar] [CrossRef]
  24. Huang, T.-C. Antistray, Learning Smart: Creating Indoor Positioning Learning Environment for Augmenting Self-Regulated Learning. Int. J. Distrib. Sens. Netw. 2014, 10, 427675. [Google Scholar] [CrossRef]
  25. Aguilar, J.; Cordero, J.; Buendía, O. Specification of the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom. J. Educ. Comput. Res. 2018, 56, 866–891. [Google Scholar] [CrossRef]
  26. Kang, Z.; Palma Fraga, R.; Izzoteglu, K.; Lee, J.; Deering, D.D.; Arana, W.X. Development of a Smart Learning Application in Multi-person Virtual Reality Using Biometric Measures of Neuroimaging, Eye Tracking, and Haptic Interactions. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2023, 67, 2137–2143. [Google Scholar] [CrossRef]
  27. Ha, I.; Kim, C. The Research Trends and the Effectiveness of Smart Learning. Int. J. Distrib. Sens. Netw. 2014, 10, 537346. [Google Scholar] [CrossRef]
  28. Adarkwah, M.A.; Odame, J.; Huang, R.; Wang, H.; Amoako, P.Y.O. Smart teaching versus hard teaching: Insights from instructors from old and new classrooms in Ghana. E-Learn. Digit. Media 2024, 20427530241251412. [Google Scholar] [CrossRef]
  29. Wang, Y.; Eysink, T.H.S.; Qu, Z.; Yang, Z.; Shan, H.; Zhang, N.; Zhang, H.; Wang, Y. Interactive Response System to Promote Active Learning in Intelligent Learning Environments. J. Educ. Comput. Res. 2022, 60, 1867–1891. [Google Scholar] [CrossRef]
  30. Steehler, A.J.; Pettitt-Schieber, B.; Alexander, P.A. The Smart Use of Smart Technologies in Teaching and Learning: Where We Are and Where We Need to Be. Ear Nose Throat J. 2022, 101 (Suppl. S9), 29S–36S. [Google Scholar] [CrossRef]
  31. Mechling, L.C.; Gast, D.L.; Thompson, K.L. Comparison of the Effects of Smart Board Technology and Flash Card Instruction on Sight Word Recognition and Observational Learning. J. Spec. Educ. Technol. 2008, 23, 34–46. [Google Scholar] [CrossRef]
  32. Lee, J.-S.; Kim, S.-W. Validation of a Tool Evaluating Educational Apps for Smart Education. J. Educ. Comput. Res. 2015, 52, 435–450. [Google Scholar] [CrossRef]
  33. Uskov, V. Smart Education: Students’ Perception of Hybrid Learning in Graduate Computing Curriculum. In Smart Innovation, Systems and Technologies; Uskov, V.L., Howlett, R.J., Jain, L.C., Eds.; KES-SEEL 2023; Smart Education and e-Learning—Smart University; Springer: Singapore, 2023; Volume 355. [Google Scholar] [CrossRef]
  34. Demir, K.A. Smart Education Framework. Smart Learn. Env. 2021, 8, 29. [Google Scholar] [CrossRef]
  35. Kaur, M.; Bhatia, M. Smart Education: A Systematic Survey and Future Research Directions. In Proceedings of the 2021 International Conference on Computing Sciences (ICCS), Phagwara, India, 4–5 December 2021; pp. 173–177. [Google Scholar] [CrossRef]
  36. Bordel, B.; Mareca, P. New Teaching and Learning Methodologies in the Smart Higher Education Era, A Study Case, Wikipedia. Int. J. Technol. Hum. Interact. 2019, 15, 70–83. [Google Scholar] [CrossRef]
  37. Lynch, J.; Redpath, T. ‘Smart’ technologies in early years literacy education: A meta-narrative of paradigmatic tensions in iPad use in an Australian preparatory classroom. J. Early Child. Lit. 2014, 14, 147–174. [Google Scholar] [CrossRef]
  38. Zhu, Z.; Yu, M.-H.; Riezebos, P. A Research Framework of Smart Education. Smart Learn. Environ. 2016, 3, 1. [Google Scholar] [CrossRef]
  39. Jang, S. Study on Service Models of Digital Textbooks in Cloud Computing Environment for SMART Education. International J. U- E-Serv. Sci. Technol. 2014, 7, 73–82. [Google Scholar] [CrossRef]
  40. Olthouse, J.M. Multiliteracies Theory and Gifted Education: Creating “Smart Spaces” in the Language Arts Classroom. Gift. Child Today 2013, 36, 246–253. [Google Scholar] [CrossRef]
  41. El Janati, S.; Maach, A.; El Ghanami, D. SMART Education Framework for Adaptation Content Presentation. Procedia Comput. Sci. 2018, 127, 436–443. [Google Scholar] [CrossRef]
  42. Watkins, S.E.; Konz, B.A.; Dua, R.; Belarbi, A.; Wunsch, D.C. Smart Truss for Education. J. Intell. Mater. Syst. Struct. 2011, 22, 317–326. [Google Scholar] [CrossRef]
  43. Sunitha, M.; Vijitha, B.; Gunavardhan, E. Artificial Intelligence based Smart Education System. In Proceedings of the 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 6–8 July 2023; pp. 1346–1350. [Google Scholar] [CrossRef]
  44. Sudharson, K.; Arvindan, R.R.; Balashunmugham, M. A Survey on AR-Based Digitization for Smart Education System. In Proceedings of the 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 18–19 February 2023; pp. 1–5. [Google Scholar] [CrossRef]
  45. Mohanachandran, D.K.; Yap, C.T.; Ismaili, Z.; Govindarajo, N.S. Smart University and Artificial Intelligence. In The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. Studies in Computational Intelligence; Hamdan, A., Hassanien, A.E., Razzaque, A., Alareeni, B., Eds.; Springer: Cham, Switzerland, 2021; Volume 935. [Google Scholar] [CrossRef]
  46. Yunus, N.; Nasir Ismail, M.; Osman, G. Smart library themes and elements: A systematic literature review. J. Librariansh. Inf. Sci. 2023. [Google Scholar] [CrossRef]
  47. Yi, S.; Yun, R.; Duan, X.; Lu, Y. Similar or Different? A Comparison of Traditional Classroom and Smart Classroom’s Teaching Behavior in China. J. Educ. Technol. Syst. 2021, 49, 461–486. [Google Scholar] [CrossRef]
  48. Tripon, C.; Gonța, I.; Bulgac, A. Nurturing Minds and Sustainability: An Exploration of Educational Interactions and Their Impact on Student Well-Being and Assessment in a Sustainable University. Sustainability 2023, 15, 9349. [Google Scholar] [CrossRef]
  49. Krumm, A.; Means, B.; Bienkowski, M. Learning Analytics Goes to School: A Collaborative Approach to Improving Education; Routledge: Oxford, UK, 2018. [Google Scholar]
  50. Yunjiu, L.; Wei, W.; Zheng, Y. Artificial Intelligence-Generated and Human Expert-Designed Vocabulary Tests: A Comparative Study. SAGE Open 2022, 12. [Google Scholar] [CrossRef]
  51. Fischer, C.; Pardos, Z.A.; Baker, R.S.; Williams, J.J.; Smyth, P.; Yu, R.; Slater, S.; Baker, R.; Warschauer, M. Mining Big Data in Education: Affordances and Challenges. Rev. Res. Educ. 2020, 44, 130–160. [Google Scholar] [CrossRef]
  52. Akçapınar, G.; Altun, A.; Aşkar, P. Using Learning Analytics to Develop Early-Warning Systems for At-Risk Students. International Journal of Educational Technology in Higher Education 2019, 16, 40. [Google Scholar] [CrossRef]
  53. Scarlatos, A. Editorial Overview: Special Issue on Artificial Intelligence in Education. J. Educ. Technol. Syst. 2024, 52, 299–300. [Google Scholar] [CrossRef]
  54. Lazendic, G.; Justus, J.-A.; Rabinowitz, S. NAPLAN Online Automated Scoring Research Program: Research Report; Australian Curriculum, Assessment, and Reporting Authority: Sydney, NSW, Australia, 2018. [Google Scholar]
  55. Onopriienko, I.; Onopriienko, K.; Bourekkadi, S. Immersive Technologies in Adult Learning as an Innovative Marketing Tool in the Educational Market. Bus. Ethics Leadersh. 2023, 7, 63–72. [Google Scholar] [CrossRef]
  56. Drost, B.R.; Levine, A.C. The Alternative Narrative: When Digital Learning Worked During the Pandemic. J. Educ. 2024. [Google Scholar] [CrossRef]
  57. Yoshimori, H.; Yoshimori, M. An Education Gift—Integrated Cognitive and Non-Cognitive Skills—For Future Generations to Grow the Economy in the Digital Phase. Socio Economic Chall. 2022, 6, 5–18. [Google Scholar] [CrossRef]
  58. Managheb, S.; Zamani, A.; Shams, B.; Farajzadegan, Z. The effect of communication skills training by video feedback method on clinical skills of interns of Isfahan University of medical sciences compared to didactic methods. Health Educ. J. 2012, 71, 546–552. [Google Scholar] [CrossRef]
  59. Okulich-Kazarin, V.; Artyukhov, A.; Skowron, Ł.; Artyukhova, N.; Dluhopolskyi, O.; Cwynar, W. Sustainability of Higher Education: Study of Student Opinions about the Possibility of Replacing Teachers with AI Technologies. Sustainability 2024, 16, 55. [Google Scholar] [CrossRef]
  60. Xiaoling, P.; Xuan, Z. The Transformation of Artificial Intelligence in the 5G Era and the Impact on Education. In 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI); IEEE: Changchun, China, 2022; pp. 903–907. [Google Scholar] [CrossRef]
  61. Ghnemat, R.; Shaout, A.; Al-Sowi, A.M. Higher Education Transformation for Artificial Intelligence Revolution: Transformation Framework. Int. J. Emerg. Technol. Learn. (Ijet) 2022, 17, 224–241. [Google Scholar] [CrossRef]
  62. Humin Yang, M. Anbarasan, and Thanjai Vadivel. Knowledge-Based Recommender System Using Artificial Intelligence for Smart Education. J. Interconnect. Netw. 2022, 22 (Supp. S2), 2143031. [Google Scholar] [CrossRef]
  63. Zhang, X.; Chen, L. College English Smart Classroom Teaching Model Based on Artificial Intelligence Technology in Mobile Information Systems. Mob. Inf. Syst. 2021, 2021, 5644604. [Google Scholar] [CrossRef]
  64. Bajaj, R.; Sharma, V. Smart Education with Artificial Intelligence-Based Determination of Learning Styles. Procedia Comput. Sci. 2018, 132, 834–842. [Google Scholar] [CrossRef]
  65. Rowland, J.G.; Jain, L.C. Artificial Intelligence Languages in Engineering Education. Int. J. Electr. Eng. Educ. 1994, 31, 138–143. [Google Scholar] [CrossRef]
  66. Filgueiras, F. Artificial Intelligence and Education Governance. Educ. Citizsh. Soc. Justice 2023, 17461979231160674. [Google Scholar] [CrossRef]
  67. Linderoth, C.; Hultén, M.; Stenliden, L. Competing visions of artificial intelligence in education—A heuristic analysis on sociotechnical imaginaries and problematizations in policy guidelines. Policy Futures in Education 2024, 14782103241228900. [Google Scholar] [CrossRef]
  68. Bilan, S.; Šuleř, P.; Skrynnyk, O.; Krajňáková, E.; Vasilyeva, T. Systematic Bibliometric Review of Artificial Intelligence Technology in Organizational Management, Development, Change and Culture. Bus. Theory Pract. 2022, 23, 1–13. [Google Scholar] [CrossRef]
  69. Kavitha, K.; Joshith, V.P. The Transformative Trajectory of Artificial Intelligence in Education: The Two Decades of Bibliometric Retrospect. J. Educ. Technol. Syst. 2024, 52, 376–405. [Google Scholar] [CrossRef]
  70. Gupta, T. Research on the Application of Artificial Intelligence in the Education and Teaching System. In Proceedings of the 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, 19–21 July 2023; pp. 1168–1173. [Google Scholar] [CrossRef]
  71. Wei, W.; Xie, H.; Li, S.; Chen, Z. Explore the Innovation of “Artificial Intelligence + Education” in the Education Domain. In 2021 4th International Conference on Information Systems and Computer Aided Education (ICISCAE 2021); Association for Computing Machinery: New York, NY, USA, 2021; pp. 2309–2312. [Google Scholar] [CrossRef]
  72. Cóndor-Herrera, O.; Arias-Flores, H.; Jadán-Guerrero, J.; Ramos-Galarza, C. Artificial Intelligence and Tomorrow’s Education. In Advances in Artificial Intelligence, Software and Systems Engineering; Lecture Notes in Networks and Systems; AHFE 2021; Ahram, T.Z., Karwowski, W., Kalra, J., Eds.; Springer: Cham, Switzerland, 2021; Volume 271. [Google Scholar] [CrossRef]
  73. Li, Q. The Use of Artificial Intelligence Combined with Cloud Computing in the Design of Education Information Management Platform. Int. J. Emerg. Technol. Learn. 2021, 16, 32–44. [Google Scholar] [CrossRef]
  74. Huang, Z. Application of Artificial Intelligence System in Smart Education in Cloud Environment with Optimization Models. In Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 8–10 April 2021; pp. 313–316. [Google Scholar] [CrossRef]
  75. Wang, S.; Wang, H.; Jiang, Y.; Li, P.; Yang, W. Understanding Students’ Participation of Intelligent Teaching: An Empirical Study Considering Artificial Intelligence Usefulness, Interactive Reward, Satisfaction, University Support and Enjoyment. Interact. Learn. Environ. 2023, 31, 5633–5649. [Google Scholar] [CrossRef]
  76. Liu, Y.; Cao, S.; Chen, G. Research on the Long-term Mechanism of Using Public Service Platforms in National Smart Education—Based on the Double Reduction Policy. Sage Open 2024, 14. [Google Scholar] [CrossRef]
  77. Blanchard, O.; Buchs, A. Clarifying Sustainable Development Concepts Through Role-Play. Simul. Gaming 2015, 46, 697–712. [Google Scholar] [CrossRef]
  78. Hung, L.D. Fast and Sustainable Development Space: An Integrated Approach. J. Asian Econ. Integr. 2022, 4, 72–91. [Google Scholar] [CrossRef]
  79. Ritchie, R.; Mispy, O.O. Measuring Progress Towards the Sustainable Development Goals. Available online: https://SDG-Tracker.org (accessed on 2 September 2024).
  80. Selvamuthu, D.; Das, D. Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control; Springer: Singapore, 2018. [Google Scholar] [CrossRef]
  81. Singpurwalla, D. A Handbook of Statistics: An Overview of Statistical Methods; Bookboon: London, UK, 2015. [Google Scholar]
  82. Okulich-Kazarin, V. Are Students of East European Universities Subjects of Educational Services? Univers. J. Educ. Res. 2020, 8, 3148–3154. [Google Scholar] [CrossRef]
  83. Lind, G. Cultural Differences in Moral Judgment Competence? A Study of West and East European University Students. Behav. Sci. Res. 1986, 20, 208–225. [Google Scholar] [CrossRef]
  84. Boyadjieva, P. Invisible higher education: Higher education institutions from Central and Eastern Europe in global rankings. Eur. Educ. Res. J. 2017, 16, 529–546. [Google Scholar] [CrossRef]
  85. Gindrich, P.A.; Kazanowski, Z. The Creative Potential and Self-Reported Learning Disabilities of Polish University Students Who Major in Special Education. Sage Open 2017, 7. [Google Scholar] [CrossRef]
  86. Kushnir, I. It is more than just education. It’s also a peace policy’: (Re)imagining the mission of the European Higher Education Area in the context of the Russian invasion of Ukraine. Eur. Educ. Res. J. 2023, 14749041231200927. [Google Scholar] [CrossRef]
  87. Likert, R. A Technique for the Measurement of Attitudes. Arch. Psychol. 1932, 140, 1–55. [Google Scholar]
  88. Okulich-Kazarin, V. New chatGPT 3.5 Instruction (Prompt) to Calculate Statistical Indicators for Student Graduation Projects. WSEAS Trans. Comput. Res. 2024, 12, 307–317. [Google Scholar] [CrossRef]
  89. Islam, A. Plastic Bodies: Women Workers and Emerging Body Rules in Service Work in Urban India. Gend. Soc. 2022, 36, 422–444. [Google Scholar] [CrossRef]
  90. Inman, R.P. Managing the Service Economy: Prospects and Problems; Cambridge University Press: Cambridge, UK, 1985. [Google Scholar]
  91. Korczynski, M.; Evans, C. Customer Abuse to Service Workers: An Analysis of Its Social Creation within the Service Economy. Work Employ. Soc. 2013, 27, 768–784. [Google Scholar] [CrossRef]
  92. Ó Riain, S. The Missing Customer and the Ever-Present Market: Software Developers and the Service Economy. Work Occup. 2010, 37, 320–348. [Google Scholar] [CrossRef]
  93. Okulich-Kazarin, V. What Method of Learning do Media Students Prefer at Lectures: Auditory or Visual? Univers. J. Educ. Res. 2020, 8, 2660–2667. [Google Scholar] [CrossRef]
  94. Bagozzi, R.P.; Brady, M.K.; Huang, M.-H. AI Service and Emotion. J. Serv. Res. 2022, 25, 499–504. [Google Scholar] [CrossRef]
  95. Artyukhov, A.; Volk, I.; Vasylieva, T.; Lyeonov, S. The role of the university in achieving SDGs 4 and 7: A Ukrainian case. E3S Web Conf. 2021, 250, 04006. [Google Scholar] [CrossRef]
  96. Volk, I.; Artyukhov, A.; Lyeonov, S. Modeling of information system for blended education quality assurance and socio-economic impact. In Proceedings of the 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, 22–26 February 2022; pp. 590–593. [Google Scholar] [CrossRef]
  97. Bhushan, S. The New Political Economy of the Twenty-First-Century Higher Education. Indian Econ. J. 2021, 69, 352–362. [Google Scholar] [CrossRef]
  98. Lyeonov, S.; Zakharkin, O.; Okhrimchuk, Y. Information Openness as a Factor of Business Leadership in Today’s Digital Environment. In Leadership, Entrepreneurship and Sustainable Development Post COVID-19; Strielkowski, W., Ed.; NILBEC 2022; Springer Proceedings in Business and Economics; Springer: Cham, Switzerland, 2023. [Google Scholar] [CrossRef]
  99. Pozovna, I.; Arhipov, S.; Kuzior, A. Determinants of Leadership in Higher Education in European Countries. Bus. Ethics Leadersh. 2023, 7, 210–224. [Google Scholar] [CrossRef]
  100. Odunlami, A. Digital education: Contributions towards sustainable schools for the future. Manag. Educ. 2023. [Google Scholar] [CrossRef]
  101. Kharchenko, D. Content and Bibliometric Analysis of Education as a Competitive Advantage of Business. Bus. Ethics Leadersh. 2023, 7, 99–108. [Google Scholar] [CrossRef]
  102. Ahmed, H. Institutional Integration of Artificial Intelligence in Higher Education: The Moderation Effect of Ethical Consideration. Int. J. Educ. Reform 2024. [Google Scholar] [CrossRef]
  103. Amzat, I.H.; Mohd Ali, H.; Ibrahim, M.B.; Othman, A.; Bin Salleh, M.J.; Alade Najimdeen, A.H. Internationalization of Higher Education, University Quality Service, and International Students’ Loyalty in Malaysia. Sage Open 2023, 13. [Google Scholar] [CrossRef]
  104. Vicsek, L.; Bokor, T.; Pataki, G. Younger Generations’ Expectations Regarding Artificial Intelligence in the Job Market: Mapping Accounts About the Future Relationship of Automation and Work. J. Sociol. 2022, 60, 21–38. [Google Scholar] [CrossRef]
  105. Yılmaz, K.; Temizkan, V. The Effects of Educational Service Quality and Socio-Cultural Adaptation Difficulties on International Students’ Higher Education Satisfaction. Sage Open 2022, 12, 21582440221078316. [Google Scholar] [CrossRef]
Sustainability 16 07695 g001
Figure 1. Topic clusters on the query “non-violent” (https://www.scopus.com/, accessed on 1 July 2024, analysis tool—SciVal).
Figure 1. Topic clusters on the query “non-violent” (https://www.scopus.com/, accessed on 1 July 2024, analysis tool—SciVal).
Sustainability 16 07695 g001
Sustainability 16 07695 g002
Figure 2. Interconnection between clusters 1 and 4 (https://www.scopus.com/ accessed on 1 July 2024, analysis tool—VOSviewer).
Figure 2. Interconnection between clusters 1 and 4 (https://www.scopus.com/ accessed on 1 July 2024, analysis tool—VOSviewer).
Sustainability 16 07695 g002
Sustainability 16 07695 g003aSustainability 16 07695 g003b
Figure 3. Student opinions: How do you feel about using artificial intelligence in the teaching process?
Figure 3. Student opinions: How do you feel about using artificial intelligence in the teaching process?
Sustainability 16 07695 g003aSustainability 16 07695 g003b
Table 1. Topic clusters on the query “non-violent” (illustrations—screenshots from SciVal).
Table 1. Topic clusters on the query “non-violent” (illustrations—screenshots from SciVal).
Topic ClusterAreaProminence Percentile, Progress
Educational Policy; Academic Performance; FinanceSocial Sciences;
Economics, Econometrics, and Finance		Sustainability 16 07695 i001
Educational Policy; Education Research; Intergenerational Mobility Social SciencesSustainability 16 07695 i002
Critical Thinking; High School Student; Learning OutcomeSocial SciencesSustainability 16 07695 i003
Academic Performance; Student Success; Self-Efficacy Social SciencesSustainability 16 07695 i004
Reflective Practice; Professional Development; Student LearningSocial Sciences;
Psychology		Sustainability 16 07695 i005
Formative Assessment; Student Learning; Recall (Cognitive Psychology) Social SciencesSustainability 16 07695 i006
Sustainable Development Goals; Industrial Sector; Student LearningSocial SciencesSustainability 16 07695 i007
Self-Efficacy; Academic Performance; High School StudentSocial SciencesSustainability 16 07695 i008
Information and Communication Technologies; Educational Technology; Pre-Service TeacherSocial Sciences;
Business, Management, and Accounting		Sustainability 16 07695 i009
Creative Thinking; Giftedness; Gifted EducationSocial Sciences;Sustainability 16 07695 i010
Science Education; High School Student; Student LearningSocial SciencesSustainability 16 07695 i011
Professional Development; Educational Policy; Pre-Service TeacherSocial SciencesSustainability 16 07695 i012
Table 2. Common description [21,59].
Table 2. Common description [21,59].
NoUniversityNumber of RespondentsFemaleMaleOther
1. Karaganda University named after Academician Buketov7343291
2. University of Economics and Innovation in Lublin (WSEI University)4533120
3. National Louis University364283810
4.Mieszko I University of Applied Sciences in Poznan5617390
5.University of Economics in Bratislava6127340
6.West Ukrainian National University11886311
7.Taras Shevchenko National University of Kyiv14488542
8.Ternopil National Pedagogical University named after V. Hnatyuk243211320
Sum11047883124
Table 3. Three parts of the questionnaire.
Table 3. Three parts of the questionnaire.
NoPartsContent
1. Appeal to respondents Dear Colleague,
Please write down your answers in a simple questionnaire. It will take less than five minutes and help you understand your attitude toward artificial intelligence (AI).
The interview is voluntary and anonymous. By answering questions, you are participating in the creation of a new future.
Please click the blue “Zapisz Ankietę” button after the questionnaire. Thank you for your time.
2. Metrics (questions 1–4)1. Gender
2. Age
3. Study (degree)
4. Country
3. Body (12 questions from 5 to 16)5. How do you feel about artificial intelligence?
6. How do you feel about using artificial intelligence in the teaching process? (a central question)
7. How often do your professors use artificial intelligence in the teaching process?
8. How often do you need to use artificial intelligence in the learning process?
9. Multiple: In what situations do you use artificial intelligence during learning?
10. Do you think artificial intelligence threatens higher education in the next five years?
11. Do you think artificial intelligence is a threat for future generations?
12. Do you fear that using artificial intelligence in higher education will get out of control within the next five years?
13. How often do you use artificial intelligence in your learning process?
14. Will artificial intelligence replace university teachers in 5 years?
15. If artificial intelligence replaces university teachers, how would you feel about it?
16. Will you be happy if artificial intelligence replaces university teachers?
Table 4. Distribution of answers.
Table 4. Distribution of answers.
Group of RespondentsNDefinitely PositivelyRather PositivelyHard to SayRather NegativelyDefinitely Negatively
1. Kazakhstan72922181112
2. Poland445231231
3. Poland36471156824312
4. Poland56101615123
5. Slovakia612724730
6. Ukraine11831463281
7. Ukraine144305525268
8. Ukraine2435811352182
Total110224145524312439
Table 5. Statistical indicators of responses [88].
Table 5. Statistical indicators of responses [88].
Group of RespondentsNM(x)δxδx−1
1. Kazakhstan7231.9446.6246.95
2. Poland449.0928.7529.08
3. Poland36415.1135.8135.86
4. Poland5626.7944.2844.69
5. Slovakia614.9221.6221.80
6. Ukraine1187.6326.5426.66
7. Ukraine14423.6142.4742.62
8. Ukraine2438.2327.4827.54
Population110214.7035.4835.52
Table 6. Verification of statistical hypotheses (one-way verification, μ0 = 0.00%).
Table 6. Verification of statistical hypotheses (one-way verification, μ0 = 0.00%).
Statistical IndicatorsValue for Respondent Groups:
12345678
Sample size, N72443645661118144243
The average of the sample, M(x)31.949.0915.1126.794.927.6323.618.23
The standard deviation for the sample, δx46.6228.7535.8144.2821.6226.5442.4727.48
Average error, ṠẊ = δx/√n5.4944.3341.8775.9172.7682.4433.5391.763
Value |tstat| for μ0 = 0.00%,
(M(x) − μ0)/ṠẊ 		5.8132.0978.0504.5281.7773.1236.6714.669
Value ttabl for the standard testing level of α (0.05)1.6451.6451.6451.6451.6451.6451.6451.645
|tstat| > ttablYesYesYesYesYesYesYesYes
Table 7. Comparison of the previous [59] and current results.
Table 7. Comparison of the previous [59] and current results.
IndicatorM(x)δx
Students with negative attitudes towards the use of AI in the teaching process4.92–31.94%21.62–46.62%
Students confident that AI will replace university teachers [59]10.85%31.10%
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Okulich-Kazarin, V.; Artyukhov, A.; Skowron, Ł.; Artyukhova, N.; Wołowiec, T. When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education. Sustainability 2024, 16, 7695. https://doi.org/10.3390/su16177695

AMA Style

Okulich-Kazarin V, Artyukhov A, Skowron Ł, Artyukhova N, Wołowiec T. When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education. Sustainability. 2024; 16(17):7695. https://doi.org/10.3390/su16177695

Chicago/Turabian Style

Okulich-Kazarin, Valery, Artem Artyukhov, Łukasz Skowron, Nadiia Artyukhova, and Tomasz Wołowiec. 2024. "When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education" Sustainability 16, no. 17: 7695. https://doi.org/10.3390/su16177695

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop