We describe our focus in Learning, Media and Technology as the 'critical analysis of the social, cultural and political aspects of digital media production, consumption, technology and culture in educational contexts '. But what work is the word 'critical' doing in current research on education and technology? The meaning of 'critical' has never been stable. It varies across the fields that contribute to the conversation on edtechfrom education to sociology, media studies, cultural studies, history, philosophy and othersand it changes over time. Some scholars wonder if we are facing a 'fatigue of critique' or suggest we move towards 'post-critique' (Editorial Team 2020;Hodgson, Vlieghe, and Zamojski 2018). Yet at the same time, I have been intrigued recently by the emergence of generative, speculative and utopian approaches to criticality in the most mainstream of places: funded research projects. Given this 'mainstreaming' of what was previously marginal in the field of education and technology, now seems like an opportune moment to take stock of the priorities in current critical research on learning, media and technology, and to highlight areas for the future of this conversation.Transformation: new edtech, new policies, new processes, new practices One core interest is how emerging technologies are potentially transforming education and society. Critical research is, in this sense, about observing emerging technologies, questioning the hype surrounding them and reflecting on their sociopolitical implications. Recent research has looked into specific hardware, software and platforms, arguing that datafying apps like ClassDojo serve as gamified mechanisms for controlling students' behaviour (Manolev, Sullivan, and Slee 2019), that learning-to-code apps like Grasshopper deploy specific pedagogies in their sociotechnical materialities (Decuypere 2019), or that teachers are largely unaware of how data can be exploited when students use wearables for physical education (Lupton 2021). More broadly, studies have teased out how expert knowledge and bioinformatic technologies aim to predict educational futures (Williamson 2020), how technology reform networks are held together in and across localities (Dussel 2018), how emotional AI is being used to quantify social and emotional learning (McStay 2020) and how claims about neurotechnology's potential to modify the brain are appearing in educational spaces (Williamson 2018).Studies have begun to interrogate the science behind edtech, identifying how knowledge is produced, how assumptions are baked into edtech, and how controversies unfold when, e.g., cluster analysis is used in learning analytics or artificial intelligence methods are integrated into learning environments (Perrotta and Selwyn 2020;Perrotta and Williamson 2016). Important assumptions are carried into practice when, for instance, machine learning tutorials present machine learning to practitioners as universally applicable and easy to apply without specialist knowledge (Heuer, Jarke, and Breiter 2021).