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Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)

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  • Vasile Gherheș

    (Department of Communication and Foreign Languages, Politehnica University of Timișoara, 300006 Timișoara, Romania)

  • Ciprian Obrad

    (Department of Sociology, West University of Timișoara, 300223 Timișoara, Romania)

Abstract

This study investigates how the development of artificial intelligence (AI) is perceived by the students enrolled in technical and humanistic specializations at two universities in Timisoara. It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations associated with it, and on the possible impact of artificial intelligence on certain areas of the social life. Moreover, the present study reveals the students’ perceptions on the sustainability of these changes and developments, and therefore aims to reduce the possible negative impact on consumers, and at anticipate the changes that AI will produce in the future. In order to collect the data, the authors have used a quantitative research method. A questionnaire-based sociological survey was completed by 928 students, with a representation error of only ±3%. The analysis has shown that a great number of respondents have a positive attitude towards the emergence of AI, who believe it will influence society for the better. The results have also underscored underlying differences based on the respondents’ type of specialization (humanistic or technical), and their gender.

Suggested Citation

  • Vasile Gherheș & Ciprian Obrad, 2018. "Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3066-:d:166314
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    References listed on IDEAS

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    1. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    2. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    3. Miltiadis D. Lytras & Vijay Raghavan & Ernesto Damiani, 2017. "Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 1-10, January.
    4. Patricia Ordóñez de Pablos & Miltiadis Lytras, 2018. "Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness," Sustainability, MDPI, vol. 10(6), pages 1-7, June.
    5. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    6. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
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    Cited by:

    1. Robert Dobre & Daniel Bulin & Maria-Cristina Iorgulescu & Iulia Monica Oehler-Sincai & Olimpia State, 2020. "Artificial Intelligence Sector: The Next Technology Bubble? A Comparative Analysis with Dotcom Based on Stock Market Data," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 22(76), pages 24-37, June.
    2. Gherheş Vasile, 2018. "Why Are We Afraid of Artificial Intelligence (Ai)?," European Review of Applied Sociology, Sciendo, vol. 11(17), pages 6-15, December.
    3. Woong Suh & Seongjin Ahn, 2022. "Development and Validation of a Scale Measuring Student Attitudes Toward Artificial Intelligence," SAGE Open, , vol. 12(2), pages 21582440221, May.
    4. Lena Bjørlo & Øystein Moen & Mark Pasquine, 2021. "The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    5. Yun Dai & Ching-Sing Chai & Pei-Yi Lin & Morris Siu-Yung Jong & Yanmei Guo & Jianjun Qin, 2020. "Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    6. Sergio Tobón & Josemanuel Luna-Nemecio, 2021. "Complex Thinking and Sustainable Social Development: Validity and Reliability of the COMPLEX-21 Scale," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    7. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    8. Su-Yen Chen & Chiachun Lee, 2019. "Perceptions of the Impact of High-Level-Machine-Intelligence from University Students in Taiwan: The Case for Human Professions, Autonomous Vehicles, and Smart Homes," Sustainability, MDPI, vol. 11(21), pages 1-14, November.
    9. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.

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