Digitalisation of education has been declared a European priority for 2021–2027 (European Commission, Digital Education Action Plan 2021–2027: Resetting Education and Training for the Digital Age, 2020). 

Digitalisation offers new possibilities to teach and learn: teachers online and offline integrate various elements of teaching, learning, assessment, roles and technologies in different ways (Jahnke et al., 2017). Digitalising schools have developed new ways of communicating, planning, organisation and administration (Petterson, 2021).

 Research suggests that school digitalisation has many positive effects: fosters students’ self-directed and independent learning, stimulates their engagement and motivation to learn, enhances computing skills and access to online content, improves attendance and enrolment, increases teacher collaboration, professional development opportunities, improves student–teacher relationships, reduces discipline problems etc. (Islam & Grönlund, 2016). However, school digitalisation is a rather complicated process, as it can have not only positive, but also negative or, in some cases, no effects (Islam & Grönlund, 2016; Hatos, 2019; Warschauer, 2007).

 In this context, it is important to address the arguments that technology and the use of technology in schools themselves do not change educational practice (Islam & Grönlund, 2016; Warschauer, 2006). Technologies are just tools that “have value only in the hands of thoughtful, well-prepared people, with a clear goal in mind” (Peck and Sprenger, 2008). So, school digitalisation amplifies the role of human mentorship (Warschauer, 2007). This means that the digitalisation of schools greatly depends on teachers’ behaviour using technology.

 According to the Fogg Behavior Model, any behaviour depends on the convergence of three factors: sufficient motivation, ability and triggers to perform the behaviour (Fogg, 2009). Based on this understanding, it can be argued that the digitalisation of schools depends to a large extent on teachers’ motivation, abilities and triggers to use technology.

 Research shows that since the start of the Covid pandemic, teachers are more motivated to use technology for teaching and are better able to do this (Beardsley, Albo, Aragon, Hernandez-Leo, 2021). Scholars are obtaining more and more evidence that teachers who consider technology as valuable to the teaching/learning are more motivated to use it (Chiu, 2022). The provision of resources for technology use in the school is also seen as a strong motivating factor: the better teachers are equipped with technological resources, the more likely they tend to use them in their pedagogical practice (Chiu, 2022). Insufficient support from schools is a major barrier to the use of technology in the teaching practices (Serriawati & Azwar, 2020). The higher the school support, the more effectively teachers use technology (Serriawati & Azwar, 2020). Support from school leaders, participation in peer co-learning groups and training from external experts can reinforce teachers’ motivation to integrate technology (Chiu, 2022).

However, there is not enough understanding of what motivates teachers to use technology in the teaching practices and how to stimulate this motivation, especially taking into account the diversity of digitalisation processes worldwide and, particularly, in Europe. Hence, it is no coincidence that there have been recent calls to continue researching the influence of leaders on teachers’ motivation (Ryan & Deci, 2020), to investigate teachers’ motivation in digitalisation processes and, especially, in technology integration (Chiu, 2022), and to explore the factors that ensure improvement and dissemination of good practice of technology-assisted pedagogy (Islam & Grönlund, 2016).  

This paper focuses on the factors that motivate Lithuanian teachers to integrate one type of technology, the AI driven learning experience platforms. 

During a 4 month action research period, 11 schools of Lithuania were offered to pilot two learning experience platforms, such as Eduten Playground (https://www.eduten.com/) and LearnLab (https://learnlab.net/). 2–5 teachers in each school and their leaders collaborating with researchers “went” all the way from getting to know the platforms, understanding their philosophy and operating principles, learning to use them, more or less integrating them in the teaching, facing and solving problems, experiencing various motivational triggers at school, municipal and national levels. Throughout implementation of the project, regular online weekly meetings of teachers and the project team took place. The team visited the project schools to meet with participating teachers and school leaders and to discuss the problems, the solutions and the observed changes in teaching/ learning, teachers’ motivation to use the piloting learning experience platforms and the factors influencing this motivation. The experience of these teachers and school leaders is important because it helps to understand what factors motivate Lithuanian teachers to integrate new technology, specifically AI driven learning experience platforms, in the teaching practice. During the school visits and at the end of the action research, we interviewed project teachers (N=20) and school leaders (N=7). We conducted face-to-face interviews as part of our school visits, other interviews were conducted via video conference meetings (Zoom). We used the interview method following Brinkmann & Kvale (2018). Among others, the interviews were focused on the broad research question: how to motivate teachers to use new technologies, such as learning experience platforms, in schools. In total, about 15 hours of recordings were made, which were transcribed and encoded, focusing on what was said. After coding, we categorised the codes into two broad themes: 1) national level motivating factors, and 2) school level motivating factors.

The study shows that at the national level there are several factors that motivate teachers to integrate new technologies, such as learning experience platforms: decent salaries, prestige of the teaching as a profession, rational demands for teachers, a system of assessment that promotes learning, the provision of technology tools etc. However, in these areas, the research participants identify several problems that hinder their motivation and that of their colleagues. For example, they consider teachers’ salaries to be too low and the remuneration system as not encouraging to learn new technologies and their use in the teaching practice. A low salary makes the teaching profession less prestigious, and the national system of school evaluation based on students’ performance in exams forces teachers to focus on preparing students for tests rather than on the holistic development of students, which is well served by experiential learning platforms. At the school level, the following factors were identified: technological equipment (computers, software, Internet), support for teacher collaboration in learning new technologies and their use in pedagogical practice, good relations between school leaders and teachers, adequate allocation of resources in the school etc. At school level motivation system, the research participants also helped to identify several serious problems that hinder teacher motivation. For example, there is too little collaboration among teachers in learning new technologies and putting them into practice, only 30% of schools are equipped with computers, efficiency of Wi-Fi does not meet the needs of the school, not all teachers are able to choose the digital tools they would like to work with etc. The identified factors that motivate teachers help to better understand why technology, specifically artificial intelligence technology, is underused in Lithuania. This understanding can also be useful for other countries where, like in Lithuania, the use of digital technologies in teaching is still underdeveloped.

Beardsley, M., Albo, L., Aragon, P., Hernandez-Leo, D. (2021). Emergency education effects on teacher abilities and motivation to use digital technologies. British Journal of Educational Technology, 52 (4), 1455-1477. Brinkmann, S., & Kvale, S. (2018). Doing interviews (2nd ed.). London: SAGE. Chiu, T.K. (2022). School learning support for teacher technology integration from a self-determination theory perspective. Education Technology Research Development, 70, 931–949. Fogg, B. J. (2009). A behavior model for persuasive design. In Proceedings of the 4th International Conference on Persuasive Technology (Persuasive '09) (pp. 1– 7). Association for Computing Machinery, Article 40. Gustafsson, U. (2021) Taking a step back for a leap forward: policy formation for the digitalisation of schools from the views of Swedish national policymakers. Education Inquiry, 12 (4), 329-346. Hatos, A. (2019). The impact of digitalization on educational achievement: a literature review from a sociological perspective. Calitatea Vieții, 30 (1), 3–16. European Comission, Digital education action plan 2021-2027: resetting education and training for the digital age, COM (2020). 624, Brussels, 30 September 2020. Jahnke, I., Bergström, P., Mårell-Olsson, E., Häll, L., & Swapna, K. (2017). Digital didactical designs as research framework – iPad integration in Nordic schools. Computers & Education, 113, 1–15. Islam, S., & Grönlund, Å. (2016). An international literature review of 1:1 computing in schools. Journal of Educational Change, 17(2), 191–222. Peck, K. L., & Sprenger, K. (2008). One-to-One educational computing: Ten lessons for successful implementation. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 935–942). New York: Springer Pettersson, F. (2021). Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies, 26, 187–204. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. Serriawati, M., & Azwar, S. (2020). Correlation between perceptions of school support and the mastery of information technology to teachers’ self-efcacy. Journal of Psychology and Instruction, 4(1), 22–28. Warschauer, M. (2007). The paradoxical future of digital learning. Learning Inquiry, 1(1), 41-49.