Technology readiness predictors of AI integration: SEM-PLS evidence from pre-service biology teachers in Indonesia

Agil Zuhal Triandro, Raharjo Raharjo, Mahanani Tri Asri

Abstract


The rapid advancement of Artificial Intelligence (AI) requires educators to possess adequate technological readiness. However, current literature predominantly focuses on in-service teachers using general acceptance models, often overlooking whether traditional psychological inhibitors remain relevant for 'digital native' students in developing contexts. This study aims to analyze the technology readiness profile of pre-service biology teachers and investigate the predictive effect of Technology Readiness Index (TRI) dimensions Optimism, Innovativeness, Discomfort, and Insecurity on their readiness to integrate AI in biology teaching. Employing a cross-sectional survey design, data were collected from 200 active undergraduate students at Universitas Negeri Surabaya and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The model explains a substantial variance in AI readiness (R2 = 0.538). The results revealed that Optimism acts as the most dominant significant driver, followed by Innovativeness. Crucially, this study offers new evidence that psychological inhibitors (Discomfort and Insecurity) no longer significantly affect AI integration readiness among mature users, challenging common assumptions in early adoption literature. The findings suggest a paradigm shift where pre-service teachers are pragmatic users driven by perceived utility rather than fear. Therefore, curriculum developers and policymakers must shift strategies from anxiety mitigation to the creation of biology-specific "embedded AI" tools that demonstrate tangible pedagogical benefits.


Keywords


Digital maturity; generative AI; readiness factor; science teacher education; technology integration

Full Text:

PDF

References


Abdulayeva, A., Zhanatbekova, N., Andasbayev, Y., & Boribekova, F. (2025). Fostering AI literacy in pre-service physics teachers: inputs from training and co-variables. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1505420

Adelana, O., Ayanwale, M., & Sanusi, I. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring - based system. Cogent Education, 11. https://doi.org/10.1080/2331186x.2024.2310976

Ayanwale, M., Adelana, O., Molefi, R. R., Adeeko, O., & Ishola, A. (2024a). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open. https://doi.org/10.1016/j.caeo.2024.100179

Ayanwale, M., Frimpong, E. K., Opesemowo, O., & Sanusi, I. (2024b). Exploring Factors That Support Pre-service Teachers’ Engagement in Learning Artificial Intelligence. Journal for STEM Education Research. https://doi.org/10.1007/s41979-024-00121-4

Chatterjee, S., & Bhattacharjee, K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443–3463. https://doi.org/10.1007/s10639-020-10159-7

Chen, Y., & Zou, Y. (2024). Enhancing education quality: Exploring teachers’ attitudes and intentions towards intelligent MR devices. European Journal of Education. https://doi.org/10.1111/ejed.12692

Falebita, O., & Kok, P. J. (2024). Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes. Journal for STEM Education Research. https://doi.org/10.1007/s41979-024-00132-1

Fundi, M., Sanusi, I., Oyelere, S., & Ayere, M. (2024). Advancing AI Education: Assessing Kenyan In-service Teachers’ Preparedness for Integrating Artificial Intelligence in Competence-Based Curriculum. Computers in Human Behavior Reports. https://doi.org/10.1016/j.chbr.2024.100412

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.

Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497. https://doi.org/10.1016/j.ijinfomgt.2022.102497

Hu, L., Wang, H., & Xin, Y. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM. Educ. Inf. Technol., 30, 12609–12631. https://doi.org/10.1007/s10639-025-13353-7

Ishmuradova, I., Zhdanov, S., Kondrashev, S., Erokhova, N., Grishnova, E., & Volosova, N. (2025). Pre-service science teachers’ perception on using generative artificial intelligence in science education. Contemporary Educational Technology. https://doi.org/10.30935/cedtech/16207

Jatileni, C., Sanusi, I., Olaleye, S., Ayanwale, M., Agbo, F., & Oyelere, P. (2023). Artificial intelligence in compulsory level of education: perspectives from Namibian in-service teachers. Education and Information Technologies, 1–28. https://doi.org/10.1007/s10639-023-12341-z

Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7

Kampa, R. K. (2023). Combining technology readiness and acceptance model for investigating the acceptance of m-learning in higher education in India. Asian Association of Open Universities Journal. https://doi.org/10.1108/aaouj-10-2022-0149

Kurniawan, A., Hariyadi, S., Prabowo, A. S., & Savira, N. I. I. (2024). The Perceptions of the Pre-service and In-service Biology Teachers on Artificial Intelligence in Biology Learning. International Journal of Biology Education Towards Sustainable Development. https://doi.org/10.53889/ijbetsd.v4i1.432

Lacuna, J. (2025). Exploring the Readiness of Pre-Service Teachers for AI Integration in Philippine Education. International Journal of Research and Innovation in Social Science. https://doi.org/10.47772/ijriss.2025.90300392

Lemke, C., Kirchner, K., Anandarajah, L., & Herfurth, F. (2023). Exploring the Student Perspective: Assessing Technology Readiness and Acceptance for Adopting Large Language Models in Higher Education. European Conference on E-Learning. https://doi.org/10.34190/ecel.22.1.1828

Li, N., & Liang, Y. (2025). Teachers’ AI readiness in Chinese as a Foreign Language education: Scale development and validation. System, 129, 103597. https://doi.org/10.1016/j.system.2025.103597

Liu, N. (2025). Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: the mediating role of confidence and AI readiness. BMC Psychology, 13. https://doi.org/10.1186/s40359-025-02620-4

Mnguni, L. (2024). A Qualitative Analysis of South African Pre-service Life Sciences Teachers’ Behavioral Intentions for Integrating AI in Teaching. Journal for STEM Education Research. https://doi.org/10.1007/s41979-024-00128-x

Mnguni, L., Nuangchalerm, P., Islami, R. El, Sibanda, D., Ramulumo, M., & Sari, I. J. (2024). AI Integration in Biology Education: Comparative Insights into Perceived Benefits and TPACK among South African and Indonesian Pre-service Teachers. Asia-Pacific Science Education. https://doi.org/10.1163/23641177-bja10086

Nja, C., Idiege, K. J., Uwe, U. E., Meremikwu, A., Ekon, E., Erim, C., Ukah, J., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10, 1–19. https://doi.org/10.1186/s40561-023-00261-x

Özüdogru, G., & Durak, H. (2025). Conceptualizing pre-service teachers’ artificial intelligence readiness and examining its relationship with various variables: The role of artificial intelligence literacy, digital citizenship, artificial intelligence-enhanced innovation and perceived threats from artificial intelligence. Information Development. https://doi.org/10.1177/02666669251335657

Parasuraman, A., & Colby, C. L. (2015). An Updated and Streamlined Technology Readiness Index. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730

Pu, S., Ahmad, N., Khambari, M. N. Md., Yap, N. K., & Ahrari, S. (2021). Improvement of Pre-Service Teachers’ Practical Knowledge and Motivation about Artificial Intelligence through a Service-learning-based Module in Guizhou, China: A Quasi-Experimental Study. Asian Journal of University Education. https://doi.org/10.24191/ajue.v17i3.14499

Rahman, M. K., Hossain, M. A., Ismail, N. A., Hossen, M. S., & Sultana, M. (2025). Determinants of students’ adoption of AI chatbots in higher education: the moderating role of tech readiness. Interactive Technology and Smart Education. https://doi.org/10.1108/itse-12-2024-0312

Ramnarain, U., Ogegbo, A., Penn, M., Ojetunde, S., & Mdlalose, N. (2024). Pre-Service Science Teachers’ Intention to use Generative Artificial Intelligence in Inquiry-Based Teaching. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10159-z

Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the Intention for the Adoption of Artificial Intelligence-Based Robots in the University to Educate the Students. IEEE Access, 10, 125666–125678. https://doi.org/10.1109/access.2022.3225555

Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences. https://doi.org/10.3390/educsci15020167

Salas-Pilco, S., Xiao, K., & Hu, X. (2022). Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review. Education Sciences. https://doi.org/10.3390/educsci12080569

Sanusi, I., Ayanwale, M., & Tolorunleke, A. E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Comput. Educ. Artif. Intell., 6, 100202. https://doi.org/10.1016/j.caeai.2024.100202

Shahid, M. K., Zia, T., Liu, B., Iqbal, Z., & Ahmad, F. (2024). Exploring the relationship of psychological factors and adoption readiness in determining university teachers’ attitude on AI-based assessment systems. The International Journal of Management Education. https://doi.org/10.1016/j.ijme.2024.100967

Sun, F., Tian, P., Sun, D., Fan, Y., & Yang, Y. (2024). Pre-service teachers’ inclination to integrate AI into STEM education: Analysis of influencing factors. Br. J. Educ. Technol., 55, 2574–2596. https://doi.org/10.1111/bjet.13469

Uren, V., & Edwards, J. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. Int. J. Inf. Manag., 68, 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588

Yue, M., Jong, M., & Ng, D. (2024). Understanding K-12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Educ. Inf. Technol., 29, 19505–19536. https://doi.org/10.1007/s10639-024-12621-2

Zhao, Y., Li, Y., Xiao, Y., Chang, H., & Liu, B. (2024). Factors Influencing the Acceptance of ChatGPT in High Education: An Integrated Model With PLS-SEM and fsQCA Approach. SAGE Open, 14. https://doi.org/10.1177/21582440241289835

Zheng, W., Zhiji, Sun, J., Wu, Q., & Hu, Y. (2024). Exploring Factors Influencing Continuance Intention of Pre-Service Teachers in Using Generative Artificial Intelligence. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2024.2433300




DOI: http://dx.doi.org/10.30821/biolokus.v8i2.4992

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Jurnal Biolokus : Jurnal Penelitian Pendidikan Biologi dan Biologi

indexed by :