Generative artificial intelligence [GenAI] has appeared to be part of higher education. Higher education institutions needed to make fast decisions, as the GenAI tools were released for the public. At first there were no clear guidelines on how to use it, and some teachers moved back to supervised exams (Scarfe et al., 2024; Sweeney, 2023). Now the GenAI tools are allowed to be used in multiple higher education institutions, and teachers and students may use them to assist in their work. Previous studies demonstrate how students and teachers consume GenAI; assisting themselves to make notes or materials, personalizing learning experiences, or utilizing it to write essays as an essay mill (Meng et al., 2025; Sweeney, 2023). It may be used as a tool for cheating (Sweeney, 2023) or to build artificial intelligence literacy (Chiu et al., 2025). However, there is a research gap on how GenAI is perceived as job demands or resources. From teachers’ perspective the GenAI tools might help at work, but teachers need to consider also how their students have used GenAI, which might increase work amount. In this study, we researched how generative artificial intelligence increases burden or assists teachers to cope with their work.

The job demands and resources model [JD-R] was used as a theoretical background for the research (Demerouti et al., 2001). The JD-R model examines the job strain in two different ways: in job demands and job resources. Job demands are all aspects of a job, which increase physical, psychological, social, and mental efforts. Too many job demands appear to lead to exhaustion, more frequent and longer absences, and finally to burnout (Bakker et al., 2003; Demerouti et al., 2001; Schaufeli & Bakker, 2004). The job resources are all positive aspects of work, which decrease the job demands’ effect, increase the engagement, enable personal growth, and assist to achieve work goals and flow (Bakker et al., 2003; Demerouti et al., 2001; Schaufeli & Bakker, 2004). Despite the division between demands and resources is simple to comprehend, not all demands are similar. The job demands can be divided into two categories: job hindrances, and job challenges (Bakker & Demerouti, 2024; Xanthopoulou et al., 2009). Job hindrances are those demands that deplete resources and do not increase motivation, such as decreased time for work, interpersonal conflicts, or deficiency of autonomy. However, job challenges are demands, which may increase motivation even though they deplete resources. These job challenges may include that the task is challenging enough, learning new skills takes time, but could be rewarding.

With this study, we aim to light up the situation where higher education teachers are and provide practical solutions for teachers and higher education institutions. Therefore, our research questions are following:

RQ1)    What job resources, challenges and hindrances higher education teachers have perceived related to GenAI in their work?

RQ2) How is pedagogical training related to perceived job resources, challenges, and demands?

Even though the research is done in one country, similar events are happening in other European countries. Therefore, this presentation generates opportunities for building comprehension about higher education teachers’ experiences related to GenAI.

The data were gathered using Webropol questionnaire, which included closed and open-ended questions about generative artificial intelligence. The total number of answers was over 300. The participants were higher education teachers from four different institutions. All participants obtained privacy policy and information from the study, and their consents to participate were asked. The data were analyzed using theory-driven content analysis (Elo & Kyngäs, 2008), using the JD-R model as a framework. In the analysis process, two researchers discussed initially the theory, and wrote down the codebook, where they made framework for four different categories; 1) teaching work job resources, challenges and hindrances; 2) interaction and communication job resources, challenges and hindrances; 3) developmental job resources, challenges and hindrances; and 4) organizational job resources, challenges and hindrances. After that, they independently categorized all answers for questions considering job resources, challenges, and hindrances. Despite using theory-driven analysis, researchers created one more category based on the data; 5) research, academic integrity and ethical job resources, challenges and hindrances. The disagreements were discussed together and finally all the answers were categorized and quantified. They calculated percental agreement, kappa, and pre-adjusted bias adjusted kappa for enhanced reliability of the study (Sim & Wright, 2005). Quantified data were analyzed with the SPSS-program to see what relation teachers' pedagogical training and the technological acceptance had with job demands and resources.

The initial findings appear to disclose that higher education teachers are perceiving GenAI as job resources, challenges, and hindrances differently. GenAI tools may affect the burden in teaching work, which is the first category. Teachers are using it in making materials, researching references, assisting in developing assignments, and evaluating students’ work. However, GenAI is not only providing ease to teachers. They expressed that GenAI is making them use more time in their work as the initial GenAI made text needed heavy modifications to be useful, the references needed to be checked for hallucinations, and students used it without explaining how. The second category revealed that GenAI tools may assist teachers to communicate with students in their own mother tongue, as it may provide quick translations. They endorsed the ability to make subtitles to videos. However, teachers did not believe everything which was made by GenAI. They needed to proofread texts carefully, and some of the materials were unusable. In the third and fourth categories, the other survey questions about how and where teachers have developed their GenAI skills, revealed that they have significant differences. Some were waiting for organizations to teach and train them, while others were taking the initiative and learning themselves. Teachers’ expectations were significantly different. The final category revealed that some teachers think about how they can teach AI-literacy to their students, how to enhance academic integrity, and ethical and environmental questions regarding GenAI tools. Teachers balance between the resources that GenAI tools provide, and the demands it may increase. These themes are presented in the conference, and how they are related to pedagogical training and other background variables. Presentation also leads to discussion about what higher education teachers, students, and organizations may do to boost personal growth, assisting achieving work goals, and developing GenAI skills.

Bakker, A. B., & Demerouti, E. (2024). Job demands–resources theory: Frequently asked questions. Journal of Occupational Health Psychology, 29(3), 188–200. https://doi.org/10.1037/ocp0000376 Bakker, A. B., Demerouti, E., de Boer, E., & Schaufeli, W. B. (2003). Job demands and job resources as predictors of absence duration and frequency. Journal of Vocational Behavior, 62(2), 341–356. https://doi.org/10.1016/S0001-8791(02)00030-1 Chiu, T. K. F., Çoban, M., Sanusi, I. T., & Ayanwale, M. A. (2025). Validating student AI competency self-efficacy (SAICS) scale and its framework. Educational Technology Research and Development. https://doi.org/10.1007/s11423-025-10512-y Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499 Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x Meng, N., Mat Deli, M., & Abdul Rauf, U. A. (2025). Educational Technology and AI: Bridging Cognitive Load and Learner Engagement for Effective Learning. Sage Open, 15(4), 21582440251395930. https://doi.org/10.1177/21582440251395930 Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study. PLOS ONE, 19(6), e0305354. https://doi.org/10.1371/journal.pone.0305354 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 Sim, J., & Wright, C. C. (2005). The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements. Physical Therapy, 85(3), 257–268. https://doi.org/10.1093/ptj/85.3.257 Sweeney, S. (2023). Who wrote this? Essay mills and assessment – Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. https://doi.org/10.1016/j.ijme.2023.100818 Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational Behavior, 74(3), 235–244. https://doi.org/10.1016/j.jvb.2008.11.003