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In recent months, the educators and higher education institutions have responded with concern, critique, and hope, to the rise of generative artificial intelligence (AI)’s unregulated and mounting influence. Following the period of emergency remote teaching, and the great ‘snapback’ (Jandrić et al., 2022), yet another new concern has emerged, promising to revolutionise education, or threaten its existence. The gravity of the situation has reverberated across the system, as wizardry of predictive pattern recognition fundamentally threatens the validity of long-held practices of summative assessments including essays, and online quizzes. This latest quandary/crisis shows no sign of abating, as venture capitalist funding and language modelling datasets grow. The technology becomes more deeply integrated into word processing, and cloud-based applications through which much of our academic labour is conducted. Understanding and conceptualising new technology within education has long been a necessity, as we wrestle with wrangling tools into our human interactions. Higher education’s relationship with edtech has always been characterised by a cyclical response to disruptive external influences of evolving technology, whose recent developments are often underpinned by neoliberal values of competition, efficiency, market-based solutions, and the privatisation of software platforms. Recent large language model developments are proving no different, with deregulation and the free market serving as the impetus to design and create such tools. Across higher education, educators scramble to decode the GenAI black box, deciphering hallucinations, confabulations, and smooth outputs indistinguishable from original student work. Policy responses range along a continuum of ban or embrace. New AI literacies are being woven into curricula, as change continues apace. 2023 marks a year of existential crisis precipitated by a global pandemic, followed by geopolitical events and a fatigue from the continual adaptation to a new normal. Even within, we are constantly shaping our educational systems. That pull is in many different directions – to accredit, to certify, to help learners become, to socialise, to emancipate, to measure - to meet very diverse purposes and aims. The politics and power structures inherent in our system further affect our response (Kuhn et al., 2023). While the potential of AI chatbots based on natural language processing models is undeniable, it is crucial to discern the reality from the hype and to better understand how our actions and responses are shaping our educational systems in this evolving domain. This editorial examines this dilemma further, to consider the impact on our scholarship of teaching and learning and how we as a community of researchers and educators respond.
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