Bridging the Digital Divide: Understanding Technological Anxiety Toward AI in the Next Generation of IT Professionals
Abstract
This study investigates the perception of AI-driven job displacement among computing students. Using a machine learning approach with psychometric data, the analysis identified key factors influencing students’ fear of replacement, including academic program, semester, learning strategies, and proficiency in using LLMs. Results show that students with less exposure to AI and those relying on memorization report higher anxiety, while those trained to develop and critically engage with GenAI tools exhibit more confidence. This work highlights the importance of curriculum design, AI literacy, and ethical reflection to prepare students for an AI-driven future.References
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Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). “The k-means Algorithm: A Comprehensive Survey and Performance Evaluation,” Electronics, v. 9, n. 8, p. 1295. DOI: 10.3390/electronics9081295
Asio, J. M. R., & Suero, A. N. (2024). “Artificial Intelligence Anxiety, Self-Efficacy, and Self-Competence among Students: Implications to Higher Education Institutions”, Education Policy and Development, v. 2, n. 2, p. 82-93. DOI: 10.31098/epd.v2i2.2541
Carvalho, D., Ferro, M., Corrêa, F., Faria, V., Lima, L., Souza, A., & Gromato, M. (2024). “Um Estudo Sobre a Percepção e Atitude dos Usuários de Sistemas Computacionais em Relação à Inteligência Artificial”, In Anais do V Workshop sobre as Implicações da Computação na Sociedade, pp. 13-23. Porto Alegre: SBC. DOI: 10.5753/wics.2024.1919.
Chan, C. K. Y., & Hu, W. (2023). “Students’ voices on generative AI: perceptions, benefits, and challenges in higher education”, International Journal of Educational Technology in Higher Education, v. 20, p. 43. DOI: 10.1186/s41239-023-00411-8
Cumming, G. (2008). “Replication and P intervals: P values predict the future only vaguely, but confidence intervals do much better”, Perspect. Psychol. Sci. 3, 286–300. DOI: 10.1111/j.1745-6924.2008.00079.x.
Ferreira, R., Freitas, E., Cabral, L., Dawn, F., Rodrigues, L., Rakovic, M., Raniel, J., & Gašević, D. (2024). “Words of Wisdom: A Journey through the Realm of NLP for Learning Analytics – A Systematic Literature Review”, Journal of Learning Analytics, v. 11, n. 3, p. 82–105. DOI: 10.18608/jla.2024.8403.
Franco, V. R. (2021). “Aprendizado de Máquina e Psicometria: Inovações Analíticas na Avaliação Psicológica”, Avaliação Psicológica, 20(3), a-c. DOI: 10.15689/ap.2021.2003.ed.
Guo, D., Zhu, Q., Yang, D., Xie, Z., Dong, K., Zhang, W., Chen, G., Bi, X., Wu, Y., Li, Y. K., Luo, F., Xiong, Y., & Liang, W. (2024). “DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence”, ArXiv. DOI: 10.48550/arXiv.2401.14196.
Hamerly, G., & Elkan, C. (2003). “Learning the k in k-means,” In Proceedings of the 17th International Conference on Neural Information Processing Systems (NIPS'03), v. 17, p. 281–288.
Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90–95. DOI: 10.1109/MCSE.2007.5.
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). “Vision, challenges, roles and research issues of Artificial Intelligence in Education”, Computers and Education: Artificial Intelligence, v. 1, 100001. DOI: 10.1016/j.caeai.2020.100001
Jošt, G., Taneski, V., & Karakatič, S. (2024). “The impact of Large Language Models on programming education and student learning outcomes,” Applied Sciences, v. 14, n. 10, p. 4115. DOI: 10.3390/app14104115
McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Ghassemi, M., Foschin, L., et al. (2019). “Reproducibility in machine learning for health”, ArXiv. DOI: 10.1126/scitranslmed.abb1655.
McKinney, W. (2010). “Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference”, 51–56. DOI: 10.25080/Majora-92bf1922-00a.
Marrone, R., Zamecnik, A., Joksimovic, S. et al. (2024). “Understanding Student Perceptions of Artificial Intelligence as a Teammate. Tech Know Learn”. DOI: 10.1007/s10758-024-09780-z
Nkedishu, V. C., & Okonta, V. (2024). “Unpacking Optimism versus Concern: Tertiary Students' Multidimensional Views on the Rise of Artificial Intelligence (AI),” International Research Journal of Multidisciplinary Scope, v. 5, p. 362-377. DOI: 10.47857/irjms.2024.v05i04.01261
Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). “Machine learning in psychometrics and psychological research”, Frontiers in Psychology, 10. DOI: 10.3389/fpsyg.2019.02970
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. DOI: 10.5555/1953048.2078195
Pinto, P. H. R., Araujo, V. M. U. D., Ferreira Junior, C. D. S., Goulart, L. L., Aguiar, G. S., Beltrão, J. V. C., Lira, P. D. D., Mendes, S. J. F., Monteiro, F. D. L. V., & Avelino, E. L. (2023). “Assessing the Psychological Impact of Generative AI on Computer and Data Science Education: An Exploratory Study”, Preprints. DOI: 10.20944/preprints202312.0379.v2
Pinto, P. H. R. (2025). LLMs Dataset UFPB [Data set]. Kaggle. DOI: 10.34740/kaggle/dsv/11081000.
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). DOI: 10.1007/s42979-021-00592-x.
Szucs, D., and Ioannidis, J. P. A. (2017). “Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature”, PLoS Biol. 15:e2000797. DOI: 10.1371/journal.pbio.2000797.
Silva, M., Seixas, E., Ferro, M., Viterbo, J., Seixas, F., & Salgado, L. (2024). “Ética e Responsabilidade na Era da Inteligência Artificial: Um Survey com Estudantes de Computação”, In Anais do XXXII Workshop sobre Educação em Computação, (pp. 854-865). Porto Alegre: SBC. DOI: 10.5753/wei.2024.3148.
Tu, Xinming, et al. “What Should Data Science Education Do with Large Language Models?” Harvard Data Science Review, vol. 6, no. 1, 19 Jan. 2024, DOI: 10.1162/99608f92.bff007ab.
United States Congress. (2023). “Artificial Intelligence Literacy Act of 2023 (H.R.6791),” Bill introduced in the U.S. House of Representatives, 118th Congress, 1st Session. Available at: [link]
Velastegui-Hernandez, D. C., Rodriguez-Pérez, M. L., & Salazar-Garcés, L. F. (2023). “Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students”, Salud, Ciencia y Tecnología - Serie de Conferencias, v. 2, p. 582. DOI: 10.56294/sctconf2023582
Wang, Y.-M., Wei, C.-L., Lin, H.-H., Wang, S.-C., & Wang, Y.-S. (2022). “What drives students’ AI learning behavior: a perspective of AI anxiety,” Interactive Learning Environments, v. 32, n. 6 , p. 2584–2600. DOI: 10.1080/10494820.2022.2153147
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.-Y., & Wen, J.-R. (2024). “A Survey of Large Language Models”, arXiv:2303.18223v15 [cs.CL]. DOI: 10.48550/arXiv.2303.18223
Zhou, X., Fang, L., & Rajaram, K. (2025). “Exploring the digital divide among students of diverse demographic backgrounds: A survey of UK undergraduates”, Journal of Applied Learning & Teaching, 8(1). DOI: 10.37074/jalt.2025.8.1.22.
Published
2025-07-20
How to Cite
MELO, Bergson Gabriel da Silva Oliveira; ARAÚJO, Vitor Meneghetti Ugulino de; ARAUJO, Ivo Crescencio de; SANTOS, Allejandro Sousa dos; PINTO, Pedro Henrique Ramos.
Bridging the Digital Divide: Understanding Technological Anxiety Toward AI in the Next Generation of IT Professionals. In: WORKSHOP ON THE IMPLICATIONS OF COMPUTING IN SOCIETY (WICS), 6. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 65-77.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2025.8556.
