Exploring the relationship between AI competency, self-efficacy, and burnout among higher education lecturers
DOI:
https://doi.org/10.15282/ijhtc.v11i2.14071Keywords:
AI-Driven, Burnout, Higher Education, Self-Efficacy, Technology integrationAbstract
In higher education, lecturers face increasing challenges to meet diverse student needs and maintain instructional quality, contributing to heightened workplace stress and potential burnout. This study explored the relationships between lecturers' AI competency, measured via the Intelligent-TPACK framework, academic self-efficacy across functional domains, and burnout dimensions (Emotional Exhaustion, Depersonalization, and Personal Accomplishment) among university lecturers in Malaysia (N = 38). Utilizing a quantitative correlational design, descriptive statistics showed that teaching self-efficacy remained stable, whereas research-related self-efficacy exhibited higher variability. Regarding well-being, respondents reported moderate emotional exhaustion, variable depersonalization, and high personal accomplishment. Pearson correlation analyses revealed no statistically significant relationship between AI competency and overall self-efficacy (r = .290, p = .078), nor between AI competency and the three burnout subscales. These findings demonstrate that technological proficiency alone does not directly enhance professional confidence or reduce occupational stress. Consequently, higher education institutions seeking to protect faculty well-being should prioritize holistic structural support, equitable workload distribution, and organizational policies over a sole reliance on technological upskilling.
Downloads
References
Adebayo, A. A. (2024). Artificial intelligence and lecturers’ effectiveness in ogun state owned universities. Fuoye International Journal of Business and Economics Education, 1(1), 188-198. http://fjbeed.fuoye.edu.ng/index.php/public/article/view/27/23.
Aggarwal, D. (2023). Integration of innovative technological developments and AI with education for an adaptive learning pedagogy. China Petroleum Processing and Petrochemical Technology, 23(2), 709-714. https://www.researchgate.net/profile/deepshikha-aggarwal/publication/374030848_integration_of_innovative_technological_developments-_and_ai_with_education_for_an_adaptive_learning_pedagogy/links/650ab418d5293c106cc905b3/integration-of-innovative-technological-developments-and-ai-with-education-for-an-adaptive-learning-pedagogy.pdf.
Ahmed, S., Khalil, I., Chowdhury, B., Haque, R., Rahman, A., Senathirajah, S., & Din, O. (2022). Motivators and barriers of artificial intelligence (AI) based teaching. Eurasian Journal of Educational Research, 100, 74-89. https://doi.org/10.14689/ejer.2022.100.006
Arslantas, T. K., & Gul, A. (2022). Digital literacy skills of university students with visual impairment: A mixed-methods analysis. Education and Information Technologies, 27(4), 5605–5625. https://doi.org/10.1007/s10639-021-10860-1
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Barkhuizen, N., & Rothmann, S. (2008). Occupational stress of academic staff in South African higher education institutions. South African Journal of Psychology, 38(2), 321–336. https://doi.org/10.1177/008124630803800205
Cao, B., Hassan, N. C., & Omar, M. K. (2024). The impact of social support on burnout among lecturers: A systematic literature review. Behavioral Sciences, 14(8), 727. https://doi.org/10.3390/bs14080727
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
Chance, S. (2017). Inciting grassroots change. In Encyclopedia of Strategic Leadership and Management (pp. 15). IGI Global. https://doi.org/10.4018/978-1-5225-1049-9.ch063
Cindrakasih, R., Jahara, J., Soegiarto, I., Ma’sum, H., & Asmuri, A. (2024). Strategy for Implementing Artificial Intelligence in the Lecturer Performance Evaluation System in Higher Education. International Journal of Educational Narratives, 2(2), 224-234. https://doi.org/10.70177/ijen.v2i2.822
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE publications.
Demartini, C. G., Sciascia, L., Bosso, A., & Manuri, F. (2024). Artificial intelligence bringing improvements to adaptive learning in education: A case study. Sustainability, 16(3), 1347. https://doi.org/10.3390/su16031347
En, L. D. C., Jingjing, Z., Peng, J., & Chung, T. C. (2025). AI-based adaptive learning framework for enhancing educational management in Malaysia using the EduAdaptNet model. Proceedings - 1st International Conference on Frontier Technologies and Solutions (ICFTS 2025). https://doi.org/10.1109/ICFTS62006.2025.11031611
Ezekiel, o. B., & akinyemi, a. L. (2022). Utilisation of artificial intelligence in education: The Perception of University of Ibadan Lecturers. Journal of Global Research in Education and Social Science (JOGRESS), 16(5), 32-40. https://doi.org/10.56557/JOGRESS/2022/v16i58053
García-Carmona, M., Marín, M. D., & Aguayo, R. (2019). Burnout syndrome in secondary school teachers: A systematic review and meta-analysis. Social Psychology of Education, 22, 189–208. https://doi.org/10.1007/s11218-018-9471-9
Gibson, S., & Dembo, M. H. (1984). Teacher efficacy: A construct validation. Journal of Educational Psychology, 76(4), 569–582. https://doi.org/10.1037/0022-0663.76.4.569
Gupta, S., Dharamshi, R. R., & Kakde, V. (2024). An impactful and revolutionized educational ecosystem using generative AI to assist and assess the teaching and learning benefits, fostering the post-pandemic requirements. In 2024 2nd International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE. https://www.scopus.com/pages/publications/85192542310?origin=scopusAI.
Guskey, T. R. (2002). Professional development and teacher change. Teachers and Teaching, 8(3), 381–391. https://doi.org/10.1080/135406002100000512.
Hashem, R., Ali, N., El Zein, F., Fidalgo, P., & Khurma, O. A. (2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research & Practice in Technology Enhanced Learning, 19. https://doi.org/10.58459/rptel.2024.19023.
Hemmings, B., & Kay, R. (2010). Research self-efficacy, publication output, and early career development. International Journal of Educational Management, 24(7), 562–574. https://doi.org/10.1108/09513541011079978.
Henny, J., Anita, A. R., Hayati, K. S., & Rampal, L. (2014). Prevalence of burnout and its associated factors among faculty academicians. Malaysian Journal of Medicine and Health Sciences, 10(1), 51-59.
Janib, J., Rasdi, R. M., Omar, Z., Alias, S. N., Zaremohzzabieh, Z., & Ahrari, S. (2021). The relationship between workload and performance of research university academics in Malaysia: The mediating effects of career commitment and job satisfaction. Asian Journal of University Education, 17(2), 85-99.
Jarmas, B., & Raed, Z. (2018). Stress and burnout among lecturers and pedagogical instructors in colleges of education. European Journal of Education Studies, 4(4), 142-160. https://doi.org/10.5281/zenodo.1210049.
Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress. Journal of Educational Psychology, 102(3), 741–756. https://doi.org/10.1037/a0019237.
Liu, K., Zhang, X., Chen, W., Yu, A. S. L., Kellum, J. A., Matheny, M. E., Simpson, S. Q., Hu, Y., & Liu, M. (2022). Development and validation of a personalized model with transfer learning for acute kidney injury risk estimation using electronic health records. JAMA Network Open, 5(7), 1–20. https://doi.org/10.1001/jamanetworkopen.2022.19776.
Manser Payne, E. H., Peltier, J., & Barger, V. A. (2021). Enhancing the value co-creation process: Artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing, 15(1), 68–85. https://doi.org/10.1108/JRIM-10-2020-0214.
Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Occupational Behavior, 2(2), 99–113. https://doi.org/10.1002/job.4030020205.
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397.
Matos, M. D. M., Sharp, J. G., & Iaochite, R. T. (2022, July). Self-efficacy beliefs as a predictor of quality of life and burnout among university lecturers. In Frontiers in Education (Vol. 7, p. 887435). Frontiers Media SA. https://doi.org/10.3389/feduc.2022.887435.
Meduri, K., Nadella, G. S., Gonaygunta, H., Kumar, D., Addula, S. R., Satish, S., ... & Rehman,
Meilani, Y. F. C. P., Bernarto, I., & Nahar, F. H. (2022). The relationship between autonomy, workload, and work-life balance to job performance of female lecturers at private universities during pandemic COVID-19. Jurnal Aplikasi Manajemen, 20(3), 477-487. https://doi.org/10.21776/ub.jam.2022.020.03.02.
Melweth, H. M. A., Alkahtani, A. S., & Al Mdawi, A. M. M. (2024). The impact of artificial intelligence on improving the quality of education and reducing future anxiety among a sample of teachers in Saudi Arabia. Kurdish Studies, 12(2), 5741-5758. https://doi.org/10.58262/ks.v12i2.426.
Mohamed, S., Nikmat, A., Hashim, N. A., Shuib, N., & Raduan, N. J. N. (2021). Burnout and its relationship to psychological distress and job satisfaction among academicians and non-academicians in Malaysia. International Journal of Higher Education, 10(1), 85-92. https://eric.ed.gov/?id=EJ1286038.
Mohammed, S. S., Suleyman, C., & Taylan, B. (2020). Burnout determinants and consequences among university lecturers. Amazonia Investiga, 9(27), 13-24. https://doi.org/10.34069/AI/2020.27.03.2.
Murad, D. F., Hassan, R., Naseer, T. A., & Fernando, E. (2025). Artificial intelligence in Malaysian education: An evaluation of opportunities and pathways. In 2025 International Conference on Data Science and Its Applications (ICoDSA). (pp. 813-817) IEEE. https://doi.org/10.1109/ICoDSA67155.2025.11157027.
Murairwa, S. (2015). Voluntary sampling design. International Journal of Advanced Research in Management and Social Sciences, 4(2), 185-200.
Nawi, A., Khamis, N. Y., Yaakob, M. F. M., Samuri, M. A. A., & Zakaria, G. A. N. (2023). Exploring opportunities and risks of artificial intelligence research for islamic ethical guidelines. Afkar: Jurnal Akidah dan Pemikiran Islam, 25(2), 1-34. https://doi.org/10.22452/afkar.vol25no2.1.
Rozali, W. N. A. W., Kamaruddin, A., Hussin, N. Y. C., & Ismail, R. N. H. R. (2023). Burnout among Muslim Lecturers in Malaysia: Single or Married Better? Journal of Islamic, 8(59), 416-421. https://doi.org/10.55573/JISED.085934.
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA Environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019.
Meduri, K., Nadella, G. S., Gonaygunta, H., Kumar, D., Addula, S. R., Satish, S., ... & Rehman, S. U. (2024). Human-centered AI for personalized workload management: A multimodal approach to preventing employee burnout. Journal of Infrastructure, Policy and Development, 8(9), 6918. https://doi.org/10.24294/jipd.v8i9.6918.
Sharp, J. G., Hemmings, B., Kay, R., & Callinan, C. (2013). An application of the revised ‘Lecturer Self-Efficacy Questionnaire’: An evidence-based route for initiating transformational change. Journal of Further and Higher Education, 37(5), 643-674. https://doi.org/10.1080/0309877X.2011.645596.
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578. https://doi.org/10.3102/00346543066004543.
Pitchan, M. A., & Azmi, S. (2025). Pengalaman dan Cabaran Pensyarah Universiti Kebangsaan Malaysia Menggunakan Kecerdasan Buatan Sebagai Sokongan Pengajaran dan Pembelajaran. Jurnal Komunikasi: Malaysian Journal of Communication, 41, 313-329. https://doi.org/10.17576/JKMJC-2025-4103-17.
Rahman, M. S. A., Idris, N. A., & Mazeni, N. A. (2024). The perceptions of artificial intelligence among mathematics lecturers in UiTM Cawangan Pulau Pinang.
Roh, Y., Heo, G., & Whang, S. E. (2021). A survey on data collection for machine learning: A big data-AI integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328–1347. https://doi.org/10.1109/TKDE.2019.2946162.
Salanova, M., Llorens, S., & Schaufeli, W. B. (2010). “Yes, I can, I feel good, and I just do it!” On gain cycles and spirals of efficacy beliefs, affect, and engagement. Applied Psychology, 59(1), 139–143. https://doi.org/10.1111/j.1464-0597.2009.00416.x
Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248
Shin, H., Noh, H., Jang, Y., Park, Y., Lee, S. M., & Kim, E. (2013). A longitudinal examination of the relationship between teacher burnout and depression. Journal of Educational Psychology, 105(2), 743–751. https://doi.org/10.1037/a0030414
Skaalvik, E. M., & Skaalvik, S. (2017). Motivated for teaching? Associations with school goal structure, teacher self-efficacy, job satisfaction, and emotional exhaustion. Teaching and Teacher Education, 67, 152–160. https://doi.org/10.1016/j.tate.2017.06.006
Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805. https://doi.org/10.1016/S0742-051X(01)00036-1
University Science Technology, J. O. O., Health, P., Nairobi, Nyanza, & Kenya. (2017). Effects of health records management on service delivery: A case study of Kisii Teaching and Referral Hospital. Journal of Hospital & Medical Management, 3(1), 1–5. https://doi.org/10.4172/2471-9781.100025
World Health Organization. (2024). ICD-11 for mortality and morbidity statistics. Retrieved July 11, 2024, from
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
