Journal Article10.3390/su16177695
When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education
Valery Okulich-Kazarin,Аrtem Аrtyukhov,Łukasz Skowron,Nadiia Аrtyukhova,Tomasz Wołowiec +4 more
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TL;DR: This study investigates the intersection of AI tools, smart education, and SDG 4.3, revealing that 31.94% of students require "non-violent" learning environments, highlighting implications for pedagogical theory and practice.
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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.
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Citations
A Cross-Disciplinary Academic Evaluation of Generative AI Models in HR, Accounting, and Economics: ChatGPT-5 vs. DeepSeek
Najib Bou Zakhem,Malak Bou Diab*,Suha Tahan,Najib Bou Zakhem,Malak Bou Diab*,Suha Tahan +5 more
Abstract: As generative AI is being further integrated into academic and professional contexts, there is a demonstrable need to determine the performance of generative AI within specific, applied domains. This research compares the performances of ChatGPT-5 and DeepSeek on tasks in the domains of accounting, economics, and human resources. The models were provided two prompts per domain, and outputs were evaluated by academics across five criteria: accuracy, clarity, conciseness, systematic reasoning, and indicators of potential bias. The inter-rater reliability was reported using Cohen’s Kappa. From the findings, both models display differences in performance. ChatGPT-5 outperformed DeepSeek in accounting and human resources, while DeepSeek outperformed ChatGPT-5 on epistemic economics tasks. Since results have shown that ChatGPT-5 outperformed DeepSeek in two out of three domains, the research recommends a reliability-based framework to compare generative AI outputs within business disciplines and offers practical suggestions on when and how to use the models within academic and professional contexts.
References
A technique for measurement of attitudes
R. Likert
- 01 Jan 1932
TL;DR: In this paper, the authors describe a technique which has been developed for the measurement of race prejudice, which avoids the following assumptions: (a) that the individual can say, to his own or the investigator's satisfaction, "This is how prejudiced I am," and (b) to the extent that theindividual can accurately assess his degree of antipathy, he will report honestly the findings of such introspection.
8.1K
A research framework of smart education
TL;DR: A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21st century learning.
Smart Education with artificial intelligence based determination of learning styles
Richa Bajaj,Vidushi Sharma +1 more
TL;DR: A framework of a tool is proposed here, which takes into consideration multiple learning models and artificial intelligence techniques for determining students’ learning styles, and is suggested that this tool be deployed in a cloud environment to provide a scalable solution that offers easy and rapid determination of learning styles.
245
Using learning analytics to develop early-warning system for at-risk students
TL;DR: It was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks’ time.
Customer abuse to service workers: an analysis of its social creation within the service economy
Marek Korczynski,Claire Evans +1 more
TL;DR: In this paper, the authors argue that a large part of customer abuse is endogenously created within the fabric of the service economy and that frequent customer abuse was associated with a configuration of the promotion of customer sovereignty (at organizational, sectoral and national levels), the weak position of labour, the higher social status position of customers vis-a-vis workers and the structuring of service interactions as encounters.
108