When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education
Abstract
:1. Introduction
1.1. Bibliometric Analysis
- 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.
- 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 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.
1.3. Connection: “Smart Education (Approach)–AI (Technical Tools)–Student (Stakeholder)–Survey (Feedback Tool)”
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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).
- “Non-violent” learning environments may be considered together with e-learning, smart education, SDG 4.3, and AI tools.
- (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
- 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.
2.2. Questioning of Students
- Definitely positively;
- Rather positively;
- Hard to say;
- Rather negatively;
- Definitely negatively.
2.3. Respondent Groups
2.4. Methodology of Verification of Statistical Hypotheses
3. Results
3.1. Analysis of Answers to the Question: How Do You Feel about Using Artificial Intelligence in the Teaching Process?
3.2. Verification of Statistical Hypotheses: The Number of Students with a Negative Attitude towards Using AI in the Teaching Process Is Zero
4. Discussion
- Personalized learning experiences.
- 2.
- Automated grading and feedback.
- 3.
- Democratization of education.
- 4.
- Ethical considerations and data privacy.
- 5.
- Balancing technology and human interaction.
- 6.
- Preparing students for the future.
5. Conclusions
- -
- Some students do not meet the requirements of “non-violent” learning environments.
- -
- The number of these students can be up to 31.94%.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Topic Cluster | Area | Prominence Percentile, Progress |
---|---|---|
Educational Policy; Academic Performance; Finance | Social Sciences; Economics, Econometrics, and Finance | |
Educational Policy; Education Research; Intergenerational Mobility | Social Sciences | |
Critical Thinking; High School Student; Learning Outcome | Social Sciences | |
Academic Performance; Student Success; Self-Efficacy | Social Sciences | |
Reflective Practice; Professional Development; Student Learning | Social Sciences; Psychology | |
Formative Assessment; Student Learning; Recall (Cognitive Psychology) | Social Sciences | |
Sustainable Development Goals; Industrial Sector; Student Learning | Social Sciences | |
Self-Efficacy; Academic Performance; High School Student | Social Sciences | |
Information and Communication Technologies; Educational Technology; Pre-Service Teacher | Social Sciences; Business, Management, and Accounting | |
Creative Thinking; Giftedness; Gifted Education | Social Sciences; | |
Science Education; High School Student; Student Learning | Social Sciences | |
Professional Development; Educational Policy; Pre-Service Teacher | Social Sciences |
No | University | Number of Respondents | Female | Male | Other |
---|---|---|---|---|---|
1. | Karaganda University named after Academician Buketov | 73 | 43 | 29 | 1 |
2. | University of Economics and Innovation in Lublin (WSEI University) | 45 | 33 | 12 | 0 |
3. | National Louis University | 364 | 283 | 81 | 0 |
4. | Mieszko I University of Applied Sciences in Poznan | 56 | 17 | 39 | 0 |
5. | University of Economics in Bratislava | 61 | 27 | 34 | 0 |
6. | West Ukrainian National University | 118 | 86 | 31 | 1 |
7. | Taras Shevchenko National University of Kyiv | 144 | 88 | 54 | 2 |
8. | Ternopil National Pedagogical University named after V. Hnatyuk | 243 | 211 | 32 | 0 |
Sum | 1104 | 788 | 312 | 4 |
No | Parts | Content |
---|---|---|
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? |
Group of Respondents | N | Definitely Positively | Rather Positively | Hard to Say | Rather Negatively | Definitely Negatively |
---|---|---|---|---|---|---|
1. Kazakhstan | 72 | 9 | 22 | 18 | 11 | 12 |
2. Poland | 44 | 5 | 23 | 12 | 3 | 1 |
3. Poland | 364 | 71 | 156 | 82 | 43 | 12 |
4. Poland | 56 | 10 | 16 | 15 | 12 | 3 |
5. Slovakia | 61 | 27 | 24 | 7 | 3 | 0 |
6. Ukraine | 118 | 31 | 46 | 32 | 8 | 1 |
7. Ukraine | 144 | 30 | 55 | 25 | 26 | 8 |
8. Ukraine | 243 | 58 | 113 | 52 | 18 | 2 |
Total | 1102 | 241 | 455 | 243 | 124 | 39 |
Group of Respondents | N | M(x) | δx | δx−1 |
---|---|---|---|---|
1. Kazakhstan | 72 | 31.94 | 46.62 | 46.95 |
2. Poland | 44 | 9.09 | 28.75 | 29.08 |
3. Poland | 364 | 15.11 | 35.81 | 35.86 |
4. Poland | 56 | 26.79 | 44.28 | 44.69 |
5. Slovakia | 61 | 4.92 | 21.62 | 21.80 |
6. Ukraine | 118 | 7.63 | 26.54 | 26.66 |
7. Ukraine | 144 | 23.61 | 42.47 | 42.62 |
8. Ukraine | 243 | 8.23 | 27.48 | 27.54 |
Population | 1102 | 14.70 | 35.48 | 35.52 |
Statistical Indicators | Value for Respondent Groups: | |||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
Sample size, N | 72 | 44 | 364 | 56 | 61 | 118 | 144 | 243 |
The average of the sample, M(x) | 31.94 | 9.09 | 15.11 | 26.79 | 4.92 | 7.63 | 23.61 | 8.23 |
The standard deviation for the sample, δx | 46.62 | 28.75 | 35.81 | 44.28 | 21.62 | 26.54 | 42.47 | 27.48 |
Average error, ṠẊ = δx/√n | 5.494 | 4.334 | 1.877 | 5.917 | 2.768 | 2.443 | 3.539 | 1.763 |
Value |tstat| for μ0 = 0.00%, (M(x) − μ0)/ṠẊ | 5.813 | 2.097 | 8.050 | 4.528 | 1.777 | 3.123 | 6.671 | 4.669 |
Value ttabl for the standard testing level of α (0.05) | 1.645 | 1.645 | 1.645 | 1.645 | 1.645 | 1.645 | 1.645 | 1.645 |
|tstat| > ttabl | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
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
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 StyleOkulich-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