In the context of global changes, the recent decade has encountered a rapid development of educational digitalisation processes, which were particularly accelerated in 2020–2022 not only by the COVID-19 pandemic, but also by the penetration of Generative AI into educational processes. Despite the fact that foreign language education has always been exposed to innovative technologies (CALL, MALL), the global challenges have caused the need for teachers to further equip themselves with new competencies and skills (Alm & Watanabe, 2023; Du & Gao, 2022; Kilde, 2024; Ng et al, 2023). As a result, self-directed learning has become a critical skill for academic success and lifelong learning (Azevedo, 2024). Pre-service teachers are the future educators, thus, it is significant to understand their self-directed learning for this new era, the Fifth Industrial Revolution (5IR), which signifies the synergy between humans and machines (Noble et al, 2022; Azevedo et al, 2024). In order to find distinct solutions and explore broader patterns, it is essential not to concentrate only on Eurocentric approaches. UNESCO (2022) emphasises the importance of LINKS (Local and Indigenous Knowledge Systems) for transdisciplinary knowledge cooperation. Researching teachers from diverse contexts may contribute to finding new solutions, pedagogies, distinct knowledge that is feasible as a result of learning to think differently about ourselves as well as by re-examination of Western-centric educational frameworks (Braidotti, 2013, 2019, 2022; Bayley, 2018; Zembylas, 2024). Thus, the aim of the present study is to explore how self-directed learning of pre-service foreign language teachers from two different socio-cultural contexts - post-Soviet Lithuania and post-colonial Kenya- is manifested within teacher education programmes in the AI era. The objectives are as follows:

1. To evaluate Lithuanian and Kenyan foreign language teachers’ perceptions towards self-directed learning; 

2. To investigate how prepared they are to engage in self-directed learning in the AI era; 

3. To understand what opportunities and challenges Lithuanian and Kenyan foreign language teachers encounter in the process of self-directed learning;

4. To investigate the ways Lithuanian and Kenyan foreign language teachers’ self-directed learning reflects the principles of critical pedagogy, posthumanism, and decolonial thinking.

The theoretical framework draws on Garrison's (1997) self-directed learning model, interpreted through the lens of critical theory and posthumanism, to explore the self-directed learning of pre-service foreign language teachers in Lithuania and Kenya within the AI era. Paulo Freire's (1967) critical pedagogy provides a lens for understanding self-directed learning as an emancipatory, dialogic process, emphasizing critical consciousness in diverse educational contexts. Rosi Braidotti’s (2013, 2019, 2022) posthumanism complements this by challenging human-centric knowledge production and highlighting the interconnectedness between humans and technologies, offering a way to analyze the ways AI influences self-directed learning in the digital age. Furthermore, Zembylas’s (2024) decolonial thinking enriches this framework by critiquing colonial legacies in education and advocating for the inclusion of indigenous knowledge systems, encouraging a re-examination of Western-centric educational models. Despite the growing body of research on self-directed learning within the context of AI (Alm & Watanabe, 2023; Azevedo, 2024; Mananay, 2024), there remains a gap in empirical studies specifically focusing on pre-service foreign language teachers from diverse socio-cultural contexts, particularly from post-Soviet and post-colonial settings. This study aims to fill that gap, providing a unique contribution to the field by examining the process of self-directed learning of Lithuanian and Kenyan foreign language teachers within the context of AI. It will not only provide a more holistic understanding of the phenomenon, but also challenge existing frameworks by highlighting the importance of both Western and Indigenous knowledge systems (UNESCO, 2022) , offering fresh insights into the process of self-directed learning in the context of AI as well as the socio-cultural dynamics for teacher education.

It is considered to adopt a comparative ethnographic research strategy for the present study, which is usually applied with the aim to illuminate cultural groups and their shared patterns in behaviour, beliefs, and language (Creswell, 2007). Since the aim of the present study is to explore how self-directed learning of pre-service foreign language teachers from two different socio-cultural contexts - post-Soviet Lithuania and post-colonial Kenya- is manifested within teacher education programmes in the AI era, the adoption of comparative ethnography would not only reveal more clearly how practices change conventional understandings, but would also show why existing explanations are inadequate for specific contexts and how certain meanings shift across different fields (Simmons & Smith, 2019). The methodology would incorporate fieldwork that would be conducted both in Lithuania and Kenya, allowing for a detailed examination of the process of self-directed learning in these two distinct settings. Observations within teacher preparation programs in both countries will form a core part of the data collection, offering insights into how pre-service teachers engage with learning, if at all, in the classroom environment. These observations will be supplemented by examining various artefacts such as teaching and learning materials, curriculum documents, which will be analysed to understand the underlying patterns of self-directed learning in these contexts. Additionally, audio recordings and memos will serve in capturing the dynamics of teacher-student interactions and other relevant classroom moments that might not be immediately visible in written materials. In-depth semi-structured interviews will be conducted with approximately 20 pre-service foreign language teachers studying in teacher education programmes—10 from Lithuania and 10 from Kenya. This methodology will not only allow for an in-depth understanding of individual and collective learning perceptions but also facilitate cross-cultural comparisons, which will be key to addressing the research questions and exploring the implications for teacher education in the AI era. Additionally, expert interviews will be conducted with teacher educators from both countries to gain insights into broader pedagogical and institutional frameworks. Combining interviews with pre-service teachers and teacher educators will provide a comprehensive view of self-directed learning in the AI era, offering valuable and unique perspectives for enhancing teacher education practices.

Potential findings of this study would align with the objectives set for understanding how the process of self-directed learning of pre-service foreign language teachers in Lithuania and Kenya is manifested within teacher education programmes in the AI era. 1. Perceptions towards self-directed learning: Teachers in both Lithuania and Kenya will likely have different perceptions of self-directed learning, shaped by their socio-cultural contexts. Lithuanian teachers may see it as closely tied to technological advancement, fostering autonomy, while Kenyan teachers may emphasize community-based, indigenous practices that reflect collective learning traditions and decolonial thinking; 2. Preparedness to engage in self-directed learning within the context of AI: Teachers in both countries are likely to recognize the growing importance of AI in education, but their preparedness to engage in the process of self-directed learning within the context of AI may differ. Lithuanian teachers may have better access to digital tools and resources, allowing them to more easily integrate AI into their learning processes. Kenyan teachers may face challenges due to limited access to technology, which may impact their readiness to engage with AI in learning. 3. Opportunities and challenges in self-directed learning: both groups will likely face similar opportunities, such as increased flexibility and access to resources, enabling greater self-management (Garrison, 1997). However, limited access to digital tools, inadequate training, and institutional constraints may hinder their potential, emphasising the need for equitable support and resources across contexts. 4. The lens of critical pedagogy, posthumanism, and decolonial thinking: Teachers from both countries may reflect the principles of critical pedagogy (Freire, 1968) and posthumanism (Braidotti, 2013, 2019, 2022) in their self-directed learning, where technology becomes an extension of human learning. Kenyan teachers, in particular, may emphasize indigenous knowledge systems, challenging Western-centric models & aligning with decolonial thinking (Bayley, 2018; Zembylas, 2024), which advocates for culturally responsive pedagogies.

Alm, A., & Watanabe, Y. (2023). Integrating ChatGPT in language education: A Freirean perspective. Iranian Journal of Language Teaching Research, 11(3), 19–30. https://doi.org/10.30466/ijltr.2023.121404 Azevedo, R., Città, G., Gentile, M., & Ifenthaler, D. (2024). Editorial. Artificial Intelligence and the paradigm shift of teachers’ role. Italian Journal of Educational Technology, 32(1), 5-7. https://doi.org/10.17471/2499-4324/1358 Bala, A., & Gheverghese Joseph, G. (2007). Indigenous knowledge and western science: the possibility of dialogue. Race & Class, 49(1), 39-61. https://doi.org/10.1177/0306396807080067 Braidotti, R. (2013). The Posthuman. Cambridge: Polity Press. Braidotti, R. (2019). Posthuman knowledge. Cambridge: Polity Press. Braidotti, R. (2022). Posthuman knowledge. Polity Press. Bayley, A. 2018. Posthumanism, Decoloniality and Re-Imagining Pedagogy.”Parallax 24 (3): 243–253. https://doi.org/10.1080/13534645.2018.1496576. Creswell, J. W. (2007). Qualitative Inquiry and Research Design: Choosing among Five Approaches (2nd ed.). Thousand Oaks, CA: Sage Publications. Du, Y., & Gao, H. (2022). Determinants affecting teachers’ adoption of AI-based applications in EFL Context: An analysis of analytic hierarchy process. Education and Information Technologies, 27(7), 9357–9384. https://doi.org/10.1007/s10639-022-11001-y European Parliament legislative resolution of 13 March 2024 on the proposal for a regulation of the European Parliament and of the Council on laying down harmonized rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union Legislative Acts (COM(2021)0206—C9-0146/2021—2021/0106(COD)). 2024. https://www.europarl.europa.eu/ doceo/document/TA-9-2024-0138_EN.pdf European Commission. (2022). Industry 5.0. https://research-and innovation.ec.europa.eu/research-area/industrial-research-and-innovation/industry-50_en Freire, P. (1968). Pedagogy of the oppressed: 30th anniversary edition. Bloomsbury Publishing USA. Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18-33. https://doi.org/10.1177/074171369704800103 Kildė, L. (2024). The Integration of Generative AI in Foreign Language Teacher Education: A Systematic Literature Review. Pedagogika, 154(2), 5–26. https://doi.org/10.15823/p.2024.154.1 Knowles, M. S. (1975). Self-directed learning. Association Press. Mananay, J. A. (2024). Integrating Artificial Intelligence (AI) in Language Teaching: Effectiveness, Challenges, and Strategies. International Journal of Learning, Teaching and Educational Research, 23 (9), 361-382. https://doi.org/10.26803/ijlter.23.9.19 UNESCO (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. https://unesdoc.unesco.org/ark:/48223/pf0000366994 UNESCO. (2022). The Role of Local and Indigenous Knowledge Systems (LINKS) in Sustainable Development. Paris: UNESCO. Rappaport, J. (2008). Beyond Participant Observation: Collaborative Ethnography as Theoretical Innovation.Collaborative Anthropologies, 1(1), 1–31. Simmons, E. S., & Smith, R. N. (2019). The Case for Comparative Ethnography. Comparative Politics, 51 (3), p. 341-359. Zembylas, M. (2024). Decolonisation as dis-enclosure: Overcoming the dangers of positionality and identity in comparative education. Comparative Education, 60(1), 1–17. https://doi.org/10.1080/03050068.2024.2326751 Zembylas, M. (2024). Decolonising Data in Higher Education: Critical Issues and Future Directions. Learning, Media and Technology. https://doi.org/10.1080/17439884.2024.2386334