Conference Agenda

The ostensible coming of artificial intelligence in the post-COVID educational landscape has given rise to significant optimism and critical reflection on the future of education globally. The mainstream techno-optimism of many in the educational community — some scholars included — has been untempered by work in critical scholarship, particularly critical EdTech, which has exposed concerns, such as algorithmic racism (Noble, 2018), the coloniality of datafication (Delahunty, 2024), and its potential to exacerbate the inequities already elided within the neoliberalisation of education (Selwyn, 2024). Despite these concerns the pursuit of acceptable integrations of AI technologies, particularly generative large language models (LLMs) such as ChatGPT, has continued apace within the broad landscape of education. The continued efforts to integrate AI into public education is understandable amidst the motivations of inter-governmental organisations (IGOs) which extol the necessities for school and higher educational curricula to adapt to this sociodigital evolution. For example, the World Bank claims that AI is capable of ‘transforming education at an unprecedented pace, offering game-changing opportunities to personalize learning experiences, support teachers in their daily tasks, and optimize education management’ (Molina et al., 2024, p. 5). These imperatives from IGOs have continued and intensified in the last two years with, for instance, the OECD citing its transformative effect on education so far in ‘raising expectations of more personalised learning, enhanced teaching practices, and more efficient system management’ (OECD, 2026, p. 11). It is understandable that within such contexts, further amplified by the saturation of digital and social media within present day consumer capitalism, that curricular concerns have tended to focus on how best to adapt to the ‘inevitable’ use of generative AI among students. We do not dismiss these concerns or the reality of the continuing evolution and reach of these technologies in educational and social contexts. However, to paraphrase the theorist Langdon Winner, there is a risk within this sociodigital rupture of losing our bearings amidst the phenomenon of technological innovations occluding our vision (Winner, 1978). In this work we aim to focus more so on the politics of AI in education (AIED) and chiefly the emerging impacts this is having and will have on educational policy and practices. Moving beyond a concern with the nano and micro spaces of curriculum we instead focus on the supra and macro levels, and the emerging politics of AIED along with its effects.

Situating the rise of this technology within the sociopolitical conditions of contemporary neoliberal policyscapes of education offers a critical entry to interrogating some of the logics which help to render sense to the techno-optimism of some educational actors. Borrowing from Sheila Jasanoff, the sociotechnical imaginary engendered in an era of AI centres critical gaze upon the ‘institutionally stabilized, and publicly performed visions of desirable futures’ which are structured on ‘shared understandings of forms of social life’ (Jasanoff & Kim, 2015, p.4). This theoretical perspective further signals the social implications of algorithms as well as the politics of algorithmic futuring which always seek, in establishing temporal anxieties about possible futures, to intervene in the present (Kitchin, 2023). For education and curricula within an era of ‘molecular biopolitics’ (Rose, 2007), datafication holds a central role formalising the authoritarian neoliberal restructuring of policy, and as this work will show, rendered intelligible through the pervasiveness of the ‘psychological-complex’ (psy-complex). The psy-complex refers to the assemblage of suppositions of personal and social responsibilities and relations provided by psychology (Gillborn & Delahunty, 2025). In this contribution we analyse the centrality of the psy-complex as a structuring and governing force within the new sociotechnical imaginaries sketched by the dominance of artificial intelligence in current educational discourse.

Reflective thinking has become a central concept across different levels of education, particularly in relation to learning and teaching processes (Lin et al., 2025; Rachmad, 2018; Rodgers, 2002; Sengers et al., 2005). John Dewey (1910, 1933), who played a significant role in the development of the concept of reflective thinking, defined reflective thinking as a “process of careful thinking” and emphasized its importance in educational practices. Taggart and Wilson (1998), who made important contributions to determining the strategies to be developed to encourage the dissemination of reflective thinking in educational settings, suggested that reflection can occur at different levels and that progress between these levels is possible through reflective practices. When examined in the field of education, reflective thinking skills significantly contribute to the development of higher-order thinking skills by supporting individuals' systematic and continuous questioning processes (Bosch, Härkki & Seitamaa-Hakkarainen, 2025; Mann, Gordon & MacLeod, 2009; Osterman & Kottkamp, 1993). Thus, students with reflective thinking skills have the opportunity to be in learning environments where they can freely express their thoughts and evaluate them from a critical perspective. In this context, reflective thinking is considered not only a skill that supports individual cognitive processes but also as a means of revealing the meanings students attach to their learning experiences (Chaffey de Leeuw & Finnigan, 2012; Pineda, Villanueva & Tolentino, 2022). Students' reflective questioning of their past and present learning experiences, particularly in the context of changing curricula, makes the effects of curriculum reforms on learning processes visible (Tsingos, Bosnic-Anticevich & Smith, 2014; Zubizarreta, 2009). The comprehensive curriculum changes implemented in Turkey in recent years have made it even more important to examine students' meaning-making processes. In this regard, the “K12 Skills Framework: Turkey Holistic Model” project, implemented in collaboration between the Ministry of National Education (MoNE) and UNICEF, offers an approach that aims to support not only students' acquisition of knowledge but also their holistic development based on skills, attitudes and values (MoNE, 2023a). The project, carried out in collaboration with teachers, programme specialists and academics in the field, has produced a holistic structure consisting of different conceptual skills, social-emotional learning skills, aptitudes, literacy skills and four areas represented by skills in Turkish, mathematics, science and social sciences (MoNE, 2023a). The resulting skills framework led to the emergence of the The Maarif Model Curriculum [TYMM] (MoNE, 2024a). With the changing curriculum, a holistic structure has been created that focuses not only on knowledge but also on skills. This study examines the meaning-making processes of secondary school students experiencing the changing chemistry curriculum (MoNE, 2024b) in relation to chemistry lessons. The research is a case study conducted with 9th and 10th-grade students (aged 15–16). Data were collected through semi-structured interviews conducted at the end of the term and students’ reflective journals. During the analysis process, the students' meaning-making processes regarding chemistry lessons were examined comparatively, focusing on how these processes reflected on the students' future learning approaches and practices. The research questions guiding this study are:

1) How do your current thoughts and perceptions about chemistry lessons differ from your past experiences? What factors have influenced this change?

2) How have changes in the chemistry curriculum influenced your learning processes? What impact have these changes had on your motivation and participation in chemistry lessons?

Our network of schools is implementing a large-scale educational project aimed at developing key 21st-century competencies in students. When introducing learning objectives oriented towards studying artificial intelligence within the NIS Programme 2.0 curriculum, the content, methods, and outcomes of school education are transformed. The NIS Programme 2.0 curriculum includes updating educational plans by incorporating learning goals related to generative artificial intelligence for subjects in the natural-mathematical, natural-scientific, and humanities cycles. In an era of rapid digital technology development and the global rise in the importance of AI as a tool for knowledge, creativity, and professional activity, universal learning objectives for generative AI allow for the inclusion of competencies such as model interpretation, critical evaluation of AI content, data visualization, and collaborative problem-solving. Learning goals for generative AI allow for the inclusion of competencies such as model interpretation, critical evaluation of AI content, data visualization, and collaborative problem-solving across various subjects — mathematics, biology, economics, history, and language disciplines. Research shows that AI is becoming an interdisciplinary tool for expanding educational opportunities and strengthening the connection between schools and real technological and social challenges.