Artificial intelligence skepticism in career domains
Open accessAbstract
The advent of artificial intelligence (AI) has sparked diverse and conflicting perspectives within academia, technological thought leaders, and corporate sectors, particularly regarding its impact on the future of work.Amidst these varied sentiments, a prevailing theme emerges caution.Both optimistic and pessimistic viewpoints on AI consistently emphasize the importance of exercising caution as we navigate the development and exploration of this technology.This article presents a comprehensive examination, incorporating deep empirical literature review, text analysis, and sentiment analysis.The study conducted the transcription and summarization of ten YouTube videos that discussed the implications of AI on job and career growth.Subsequently, sentiment analysis was conducted using ChatGPT, an AI chatbot, and a sentiment analysis formula was employed to calculate the sentiment average.The findings indicate a prevailing sentiment of cautious optimism, with a sentiment average of 0.35.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In professional contexts, skepticism often centers on ethical concerns.
“In these contexts and others, skepticism often centers on ethical concerns that take many shapes and forms (Adepoju et al., 2024; Ambartsoumean and Yampolskiy, 2023).”
From From avoidance to adoption: strategies for integrating AI in higher education contexts (2026) · cites this paper as Adepoju et al., 2024
Section 3, “Human Skepticism on Modern Technologies in the Workplace” (page unavailable), links workplace skepticism to concerns about job security, work-life balance, and personal privacy. Privacy is an ethical concern, but the paper does not establish that skepticism often centers on ethical concerns or characterize them as taking many forms. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSkepticism toward AI is not confined to education but is shared across professional domains such as medicine, law, and economics.
“At the broadest level, skepticism toward AI is not confined to education but is shared across professional domains such as medicine, law, and economics.”
From From avoidance to adoption: strategies for integrating AI in higher education contexts (2026) · cites this paper as Adepoju et al., 2024
Section 3 (page unavailable) discusses skepticism toward technology in workplaces, supporting the point that such skepticism is not confined to education. The Introduction discusses concerns about AI in labor economics, and Section 8 discusses AI’s implications for healthcare and law, but the copy does not establish that skepticism toward AI is shared across all three named domains. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The impact of artificial intelligence in medicine on the future role of the physician
Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789 · see citing works
Toward understanding the impact of artificial intelligence on labor
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900949116 · Cited by 713 · see citing works
The quantified self: What counts in the neoliberal workplace
Moore, P., & Robinson, A. (2016) · New Media & Society · doi:10.1177/1461444815604328 · Cited by 427 · see citing works
Karaca, O., Çalışkan, S. A., & Demir, K. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02546-6 · Cited by 377 · see citing works
Yu, X., Xu, S. T., & Ashton, M. (2022) · Information Technology and People · doi:10.1108/itp-04-2021-0254 · Cited by 355 · see citing works
Machine learning and medical education
Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · see citing works
Kwak, Y., Ahn, J.-W., & Seo, Y. H. (2022) · BMC Nursing · doi:10.1186/s12912-022-01048-0 · Cited by 291 · see citing works
A profession in transition: actors, tasks and roles in AI-based accounting
Leitner-Hanetseder, S., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021) · Journal of Applied Accounting Research · doi:10.1108/jaar-10-2020-0201 · Cited by 274 · see citing works
The Impact of Automation on Employment: Just the Usual Structural Change?
Vermeulen, B., Kesselhut, J., Pyka, A., & Saviotti, P. P. (2018) · Sustainability · doi:10.3390/su10051661 · Cited by 185 · see citing works
Yu, J., & Ngan, H. F. B. (2019) · Tourism Review · doi:10.1108/tr-07-2018-0097 · Cited by 125 · see citing works
The Challenges, Opportunities, and Imperative of Structured Reporting in Medical Imaging
Reiner, B. I. (2009) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-009-9239-z · Cited by 92 · see citing works
Zopiatis, A., Krambia‐Kapardis, M., & Varnavas, A. (2012) · Tourism and Hospitality Research · doi:10.1177/1467358412466668 · Cited by 89 · see citing works
Artificial intelligence: neither Utopian nor apocalyptic impacts soon
Naudé, W. A. (2020) · Economics of Innovation and New Technology · doi:10.1080/10438599.2020.1839173 · Cited by 74 · see citing works
Nurses’ and community support workers’ experience of telehealth: a longitudinal case study
Sharma, U., & Clarke, M. (2014) · BMC Health Services Research · doi:10.1186/1472-6963-14-164 · Cited by 73 · see citing works
de Laat, M. F., Joksimovíc, S., & Ifenthaler, D. (2020) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-03-2020-0026 · Cited by 69 · see citing works
Understanding Technological Unemployment: A Review of Causes, Consequences, and Solutions
Lima, Y., Barbosa, C. E., Salazar, H., & de Souza, J. M. (2021) · Societies · doi:10.3390/soc11020050 · Cited by 49 · see citing works
The rise of the machines and how they impact workers' psychological health: An empirical study
Stamate, A. N., Sauvé, G., & Denis, P. L. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.315 · Cited by 34 · see citing works
Oberst, U., De Quintana, M., Del Cerro, S., & Chamarro, A. (2021) · Management Research Review · doi:10.1108/mrr-06-2020-0356 · Cited by 32 · see citing works
Muntanyola‐Saura, D. (2016) · International Sociology · doi:10.1177/0268580916655972 · Cited by 27 · see citing works
Coelli, M. B., & Borland, J. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3472764 · Cited by 25 · see citing works
Rural Luddism and the makeshift economy of the Nottinghamshire framework knitters
Roberts, M. T. (2017) · Social History · doi:10.1080/03071022.2017.1327644 · Cited by 22 · see citing works
Manufacturing in the wild – viewing human-based assembly through the lens of distributed cognition
Lindblom, J., & Thorvald, P. (2017) · Production & Manufacturing Research · doi:10.1080/21693277.2017.1322540 · Cited by 18 · see citing works
Radiation therapist perceptions on how artificial intelligence may affect their role and practice
O'Shaughnessey, J., & Collins, M. (2022) · Journal of Medical Radiation Sciences · doi:10.1002/jmrs.638 · Cited by 16 · see citing works
Warning, A., Weber, E., & Püffel, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.868789 · Cited by 15 · see citing works
DDoD: Dual Denial of Decision Attacks on Human-AI Teams
Tag, B., van Berkel, N., Verma, S., Zhao, B. Z. H., Berkovsky, S., Kaafar, D., Kostakos, V., & Ohrimenko, O. (2023) · IEEE Pervasive Computing · doi:10.1109/mprv.2022.3218773 · Cited by 15 · see citing works
Attitudes Toward Technologies for Conception: A 15‐Year Follow‐Up
Lasker, J. N., & Murray, D. E. (2001) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2001.tb00169.x · Cited by 14 · see citing works
Shinde, A., Pawar, D., & Sonawane, K. (2021) · International Journal of Basic & Clinical Pharmacology · doi:10.18203/2319-2003.ijbcp20212387 · Cited by 12 · see citing works
The Pentrich Rebellion – A Nottingham Affair?
Gaunt, R. A. (2018) · Midland History · doi:10.1080/0047729x.2018.1522464 · Cited by 7 · see citing works
Roux-Kemp, A. L. (2021) · Journal of Law Technology and Trust · doi:10.19164/jltt.v2i1.1004 · Cited by 4 · see citing works
Mantello, P. A., Ho, T. M., Nguyen, M., & Vuong, Q. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3772076 · Cited by 3 · see citing works
Roux-Kemp, A. L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3891007 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Prejean, E., Aldredge, M., Liao, W., Kilcoyne, M., Parker, C. (2020). Lessons Learned During Turbulent Times to Weather and Thrive In The Storm Of Covid-19. Int J Innov Educ Res, 11(8), 276-288. DOI: 10.31686/ijaer.vol 8.iss 11 .2744. · DOI
Cited by
Sorted by citations
Gerçek, M., & Özveren, C. G. (2025) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2025.101895 · Cited by 20 · see citing works
Ateeq, A. A., & Almuraqab, N. A. S. (2024) · Contributions to finance and accounting · doi:10.1007/978-3-031-75960-4_5 · Cited by 3 · see citing works
Lin, T., & Zhang, Y. (2026) · Journal of Science Communication · doi:10.22323/358020260107085703 · Cited by 4 · see citing works
Korzyński, P., Protsiuk, O., Tursunbayeva, A., Papa, A., & Cremer, D. D. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-01-2025-0055 · Cited by 2 · see citing works
From avoidance to adoption: strategies for integrating AI in higher education contexts
Gerard, J., Hadi, M. U., Natsheh, B. R. R., Singh, M., & Reid, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1869723
Shi, X. C., Leung, X. Y., Bai, B., & Buhalis, D. (2026) · Tourism Management · doi:10.1016/j.tourman.2026.105478
- Authors
- Adepoju, O. D., Tijani, B., & Karera, S.
- Venue
- International Journal for Digital Society
- Publisher
- Infonomics Society
- Published
- 2024-03-30
- Volume
- 15
- Issue
- 1
- Pages
- 1880-1888
- ISSN
- 2040-2570 (ISSN-L)
- OpenAlex
- W4390628393
- Cited by
- 6
- References
- 31
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 1
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal for Digital Society (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- future of work, job growth, cautious optimism, YouTube videos, sentiment analysis, ChatGPT, public perception of AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
