Does strategic generative AI learning in higher education trigger job anxiety? An experimental study

Authors

  • Kurnadi Gularso Universitas Bunda Mulia
  • Michael Christian Universitas Bunda Mulia
  • Guan Nan Universitas Bunda Mulia
  • LI Jiancheng Beijing Language and Culture University

DOI:

https://doi.org/10.56587/bemi.v4i1.135

Keywords:

AI learning innovation, higher education digital transformation, AI job replacement anxiety, generative AI knowledge, strategic management

Abstract

Background: The use of AI-based instructional videos in higher education is rapidly expanding, offering greater convenience and potential benefits in the learning process. However, alongside these advantages, students are increasingly concerned about the possibility that AI may replace human roles, particularly in the context of future employment.

Purpose: This study aims to examine the effects of perceived ease of use (PEU), perceived benefits (PB), and knowledge of generative AI (KAI) on AI job replacement anxiety (REPLC), as well as to test the moderating role of KAI in these relationships. As part of its broader contribution, this study positions AI literacy as a strategic capability shaping workforce readiness.

Method: This study employs an explanatory quantitative approach using a quasi-experimental design involving 200 undergraduate students in Jakarta. Respondents were first exposed to an AI-generated lecture video before completing a questionnaire measured on a five-point Likert scale. Data were analysed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS software. 

Findings: The results indicate that PEU and PB are not significant with respect to KAI, while KAI has a positive and significant effect on such anxiety. In addition, KAI has a significant negative effect on PB and PEU. However, the moderating effects are not empirically supported. These findings highlight that AI literacy plays a critical role in shaping students’ awareness of potential job disruption caused by AI technologies. Furthermore, AI literacy can be considered as a strategic competence to prepare students for digital transformation and future workforce issues.

References

Acosta-Enriquez, B. G., Ramos Farroñan, E. V., Villena Zapata, L. I., Mogollon Garcia, F. S., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19). https://doi.org/10.1016/j.heliyon.2024.e38315

Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., & Abdullah-Al-Jubair, M. (2024). The Generative AI Landscape in Education: Mapping the Terrain of Opportunities, Challenges, and Student Perception. IEEE Access, 12, 147023–147050. https://doi.org/10.1109/ACCESS.2024.3461874

Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). “Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms”. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3

Almulla, M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e32220

Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1), 7169. https://doi.org/10.1038/s41598-025-91634-4

Amiri, H., Peiravi, S., rezazadeh shojaee, S. sara, Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M. (2024). Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Medical Education, 24(1), 412. https://doi.org/10.1186/s12909-024-05406-1

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, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3

Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1), 64. https://doi.org/10.1186/s40561-023-00284-4

Christian, M., Nan, G., Gularso, K., Dewi, Y. K., Sunarno, & Wibowo, S. (2024). Impact of AI Anxiety on Educators Attitudes Towards AI Integration. 2024 3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024. https://doi.org/10.1109/ICCIT62134.2024.10701130

Christian, M., Pardede, R., Gularso, K., Dewi, Y. K., Sunarno, & Amiro, T. (2024). Examining Learning Anxiety in AI-Enhanced Educational Environments Among Urban Lecturers. 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), 1–5. https://doi.org/10.1109/ICCIT62134.2024.10701128

Christian, M., Pardede, R., Yulita, H., Dewi, Y. K., Wibowo, S., & Sunarno, S. (2024). Anxiety-Related AI Configuration and AI Usage Behavior Among Lecturers: Gender’s Moderating Role. 2024 IEEE International Conference on Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), 1–6. https://doi.org/10.1109/CERIA64726.2024.10914840

Christian, M., Yulita, H., Muzammil, O. M., Dewi, Y. K., Seralarat, K., & Nan, G. (2025). From Technology to Digital Ecosystem Engagement: Investigating Students’ Satisfaction with the Distance Learning Experience. 2025 International Conference on Information Management and Technology (ICIMTech), 800–805. https://doi.org/10.1109/ICIMTech67074.2025.11264848

Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Singh Jagdeep, S. J. S. (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3). https://doi.org/10.1016/j.heliyon.2024.e25896

Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8). https://doi.org/10.1016/j.heliyon.2024.e29317

Elnaem, M. H., Okuyan, B., Mubarak, N., Thabit, A. K., AbouKhatwa, M. M., Ramatillah, D. L., Isah, A., Al-Jumaili, A. A., & Nazar, N. I. M. (2025). Students’ acceptance and use of generative AI in pharmacy education: international cross-sectional survey based on the extended unified theory of acceptance and use of technology. International Journal of Clinical Pharmacy, 47(4), 1097–1108. https://doi.org/10.1007/s11096-025-01936-w

Fan, L., Deng, K., & Liu, F. (2025). Educational impacts of generative artificial intelligence on learning and performance of engineering students in China. Scientific Reports, 15(1), 26521. https://doi.org/10.1038/s41598-025-06930-w

Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in Higher Education: Balancing Innovation and Integrity. British Journal of Biomedical Science, 81. https://doi.org/10.3389/bjbs.2024.14048

Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A workbook. SpringerCham. https://doi.org/10.1007/978-3-030-80519-7

Han, S., Kang, H. S., Gimber, P., & Lim, S. (2025). Nursing Students’ Perceptions and Use of Generative Artificial Intelligence in Nursing Education. In Nursing Reports (Vol. 15, Issue 2, p. 68). https://doi.org/10.3390/nursrep15020068

Hardie, P., Darley, A., Derwin, R., Eustace-Cook, J., Kearns, S., Mc Brien, B., Siddiquee, A., Zheng, D., & Mooney, M. (2026). Applications, attitudes and ethical considerations of Generative Artificial Intelligence (Gen AI) in nursing education: a scoping review. BMC Nursing, 25(1), 148. https://doi.org/10.1186/s12912-025-04253-9

Heil, J., Ifenthaler, D., Cooper, M., Mascia, M. L., Conti, R., & Penna, M. P. (2025). Students’ perceived impact of GenAI tools on learning and assessment in higher education: the role of individual AI competence. Smart Learning Environments, 12(1), 37. https://doi.org/10.1186/s40561-025-00395-0

Hwang, Y., & Wu, Y. (2025). The influence of generative artificial intelligence on creative cognition of design students: a chain mediation model of self-efficacy and anxiety. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1455015

Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A. (2024). Artificial intelligence in medical education - perception among medical students. BMC Medical Education, 24(1), 804. https://doi.org/10.1186/s12909-024-05760-0

Kai, C., Ping, W., & Xiaomin, J. (2026). AI anxiety and adoption intention in higher education based on an extended TAM-UTAUT and PLS-SEM analysis. Scientific Reports, 16(1), 3672. https://doi.org/10.1038/s41598-026-35823-9

Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. In Sustainability (Vol. 15, Issue 16). https://doi.org/10.3390/su151612451

Khlaif, Z. N., Salameh, N., Ajouz, M., Mousa, A., Itmazi, J., Alwawi, A., & Alkaissi, A. (2025). Using Generative AI in nursing education: Students’ perceptions. BMC Medical Education, 25(1), 926. https://doi.org/10.1186/s12909-025-07416-z

Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1498132

Kwak, Y., Ahn, J.-W., & Seo, Y. H. (2022). Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions. BMC Nursing, 21(1), 267. https://doi.org/10.1186/s12912-022-01048-0

Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24(1), 401. https://doi.org/10.1186/s12909-024-05400-7

Li, K. C., Chong, G. H., Wong, B. T., & Wu, M. M. (2025). A TAM-Based Analysis of Hong Kong Undergraduate Students’ Attitudes Toward Generative AI in Higher Education and Employment. In Education Sciences (Vol. 15, Issue 7). https://doi.org/10.3390/educsci15070798

Li, R., Ouyang, S., & Lin, J. (2025). Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety. BMC Psychology, 13(1), 454. https://doi.org/10.1186/s40359-025-02757-2

Lin, H., & Chen, Q. (2024). Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC Psychology, 12(1), 487. https://doi.org/10.1186/s40359-024-01979-0

Malakul, S. (2025). Exploring Factors Influencing Teachers’ Acceptance of AI Tools for Creating Animated Educational Videos With Pedagogical Agents. Journal of Computer Assisted Learning, 41(4), e70083. https://doi.org/10.1111/jcal.70083

Mohamed, M. G., Goktas, P., Khalaf, S. A., Kucukkaya, A., Al-Faouri, I., Seleem, E. A. E. S., Ibraheem, A., Abdelhafez, A. M., Abdullah, S. O., Zaki, H. N., & Nashwan, A. J. (2025). Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based structural equation modelling approach. BMC Nursing, 24(1), 703. https://doi.org/10.1186/s12912-025-03436-8

Navarrete, E., Nehring, A., Schanze, S., Ewerth, R., & Hoppe, A. (2025). A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness. International Journal of Artificial Intelligence in Education, 35(4), 1631–1694. https://doi.org/10.1007/s40593-025-00481-x

Pellas, N. (2023). The influence of sociodemographic factors on students’ attitudes toward AI-generated video content creation. Smart Learning Environments, 10(1), 57. https://doi.org/10.1186/s40561-023-00276-4

Peng Yang, Xiaodie Wang, Jing Zhang, Sharmin Kutty Sivaraman, & Pu Song. (2025). Exploring the Impact of Artificial Intelligence on University Students’ Perception of Slow Employment: A Psychological and Behavioral Analysis. International Journal of Interactive Mobile Technologies (IJIM), 19(18), 92–105. https://doi.org/10.3991/ijim.v19i18.57243

Pham, T. D., Karunaratne, N., Exintaris, B., Liu, D., Lay, T., Yuriev, E., & Lim, A. (2025). The impact of generative AI on health professional education: A systematic review in the context of student learning. Medical Education, 59(12), 1280–1289. https://doi.org/10.1111/medu.15746

Preiksaitis, C., & Rose, C. (2023). Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ, 9, e48785. https://doi.org/10.2196/48785

Shen, M., Shen, Y., Liu, F., & Jin, J. (2025). Prompts, privacy, and personalized learning: integrating AI into nursing education—a qualitative study. BMC Nursing, 24(1), 470. https://doi.org/10.1186/s12912-025-03115-8

Shrivastava, P. (2025). Understanding acceptance and resistance toward generative AI technologies: a multi-theoretical framework integrating functional, risk, and sociolegal factors. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1565927

Tang, X., Yuan, Z., & Qu, S. (2025). Factors Influencing University Students’ Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model. Journal of Computer Assisted Learning, 41(1), e13105. https://doi.org/10.1111/jcal.13105

Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1268549

Türk, N., Batuk, B., Kaya, A., & Yıldırım, O. (2025). What makes university students accept generative artificial intelligence? A moderated mediation model. BMC Psychology, 13(1), 1257. https://doi.org/10.1186/s40359-025-03559-2

Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-Service Teachers’ GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching. In Behavioral Sciences (Vol. 14, Issue 5, p. 373). https://doi.org/10.3390/bs14050373

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, 1–17. https://doi.org/10.1080/10494820.2022.2153147

Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887

Wu, F., Dang, Y., & Li, M. (2025). A Systematic Review of Responses, Attitudes, and Utilization Behaviors on Generative AI for Teaching and Learning in Higher Education. In Behavioral Sciences (Vol. 15, Issue 4, p. 467). https://doi.org/10.3390/bs15040467

Yan, Z., & Qianjun, T. (2025). Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes. Scientific Reports, 15(1), 25802. https://doi.org/10.1038/s41598-025-10941-y

Yaşar, H., & Karagucuk, V. (2025). The effect of artificial intelligence anxiety on career decidedness among students in English-related departments at universities. Discover Artificial Intelligence, 5(1), 48. https://doi.org/10.1007/s44163-025-00284-y

Yuan, Y., Fu, J., Leng, L., Wen, Z., Wei, X., Han, D., Hu, X., Liang, Y., Luo, Q., Zhang, X., & Hu, R. (2025). The strengths, weaknesses, opportunities, and threats of generative artificial intelligence: a qualitative study of undergraduate nursing students. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1672140

Zhang, X., Hu, X., Sun, Y., Li, L., Deng, S., & Chen, X. (2025). Integrating AI Literacy with the TPB-TAM Framework to Explore Chinese University Students’ Adoption of Generative AI. In Behavioral Sciences (Vol. 15, Issue 10, p. 1398). https://doi.org/10.3390/bs15101398

Zhao, Y., Yuan, Y., Wen, Z., Leng, L., Shi, L., Hu, X., Wei, X., Zuo, M., Mou, J., Luo, Q., Chen, M., Hu, R., & Gao, H. (2025). The current status, knowledge, attitudes, and challenges of generative artificial intelligence use among undergraduate nursing students: a single-center cross-sectional survey of western China. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1648416

Downloads

Published

2026-05-20

How to Cite

Gularso, K., Christian, M., Nan, G., & Jiancheng, L. (2026). Does strategic generative AI learning in higher education trigger job anxiety? An experimental study. Bulletin of Educational Management and Innovation, 4(1), 55–81. https://doi.org/10.56587/bemi.v4i1.135

Issue

Vol. 4 No. 1 (2026): Bulletin of Educational Management and Innovation

Section

Articles

License

Copyright (c) 2026 Kurnadi Gularso, Michael Christian, Guan Nan, LI Jiancheng

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish in this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).