IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i9p3066-d166314.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/9/3066/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/9/3066/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Gherheş Vasile, 2018. "Why Are We Afraid of Artificial Intelligence (Ai)?," European Review of Applied Sociology, Sciendo, vol. 11(17), pages 6-15, December.
    8. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    2. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    3. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    4. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
    5. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    6. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    7. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    8. Jie Gao & Xinping Huang & Lili Zhang, 2019. "Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    9. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    10. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    11. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    12. Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    13. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    14. 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.
    15. Yung Yau & Wai Kin Lau, 2018. "Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    16. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    17. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    18. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    19. Daiho Uhm & Jea-Bok Ryu & Sunghae Jun, 2017. "An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    20. Yi-Zeng Hsieh & Shih-Syun Lin & Yu-Cin Luo & Yu-Lin Jeng & Shih-Wei Tan & Chao-Rong Chen & Pei-Ying Chiang, 2020. "ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation," Sustainability, MDPI, vol. 12(14), pages 1-17, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3066-:d:166314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.