Reassembling ‘Merit’ in the era of Artificial Intelligence

Networked Learning perspectives

Abstract

A spectre continues to haunt education: the spectre of meritocracy. Once conceived as a moral and institutional alternative to aristocracy, meritocracy promised to reward one’s ability and effort rather than bloodline (Young, 1958). Yet in the twenty-first century, even as critiques expose its ideological and structural contradictions through demonstrating how it legitimizes and reproduces inequality (Littler, 2017; Markovits, 2019; Sandel, 2020) and how the very definition of merit is not universal but varies across temporal and spatial contexts (Lamont, 2019; Mijs, 2019), ‘merit’ is continuously invoked as a universal measure and a personal trait central to fairness.

This paper argues that by paying attention to the socio-material conditions and processes through which merit is empirically conceptualized, enacted, and sustained within educational assemblages, education research can renew its critique of merit’s long-held individualistic assumptions. Thus, this paper recognizes the rise of generative artificial intelligence (Gen AI) as an inflection point that renders this gap newly visible. As Gen AI becomes increasingly embedded in practices of learning, writing, feedback, and assessment, it unsettles previously taken-for-granted distinctions between human capability and technological function, individual skill and machinic augmentation. By blurring these lines, Gen AI demonstrates the potential to reveal the fragility of ‘merit’ as an individual and embodied construct, necessitating new theoretical vocabularies that allow us to trace merit as a relational, distributed, and mediated construct. Therefore, this paper proposes adopting Networked Learning (NL) as a framework for rethinking merit in the postdigital era, which allows us to understand learning as a relational and distributed processes between different actors and contexts (Goodyear et al., 2004; Fenwick & Edwards, 2010; Gourlay, 2021).

Grounded in these insights, produced by connecting NL research with debates on meritocracy and educational justice, this paper concludes by outlining the implications of “networked merit.” If merit is understood as a relational effect rather than an individual possession, then it becomes evident that contemporary disputes surrounding Gen AI, such as whether AI-assisted writing is “cheating” or just another demonstration of “AI-competency,” cannot be resolved through moral appeals to individual integrity alone. Instead, they demand close attention both to the institutional arrangements that define authorship and the technology-entangled micro-practices of learning as sites of merit-generation. In doing so, it positions NL more than a descriptive and interpretive paradigm, as a political and ethical theory necessary for exploring how value, recognition, and justice could be timely ‘reassembled’ in this brave new era.