Generative AI is increasingly permeating academic work and enabling the rapid production of “plausible” texts, code, and solutions with limited observable effort. In this European policy context, the European-Union is actively addressing the implications of AI for higher-education across teaching, learning, and research, by promoting labour-market-relevant digital skills among university students and researchers and by issuing ERA-level guidelines for the responsible use of generative AI in research (European-Commission, 2020; Directorate General, 2024). 

We conceptualize generative AI as an institutional disruption in Christensen’s sense (Christensen, 1997) that unsettles the equilibrium linking higher-education to labor markets by eroding the credibility of degrees and academic outputs as signals under conditions of information asymmetry (Spence, 1973).  

We interpret this evolving disruption through a Bourdieusian lens (Bourdieu, 1986, 1996; Bourdieu & Passeron, 1990), emphasizing AI’s challenge to mechanisms of distinction and the reproduction of the social order, maintained through various forms of capital (cultural, social, economic). Credentials, as a form of state-recognized institutionalized cultural capital, symbolize competence and social status. Academic titles consolidate social hierarchies through symbolic power. Thus, educational credentials help produce the “state nobility”, elites whose authority rests partly on state-sanctioned institutionalized cultural capital (Bourdieu, 1996). 

The aim of this paper is to develop an integrated framework for understanding how generative AI reshapes higher-education’s signaling and distinction mechanisms. 

Research questions: How does the integration of generative AI in higher-education reshape the credibility of higher-education degrees as a signal of competence to the labor market? How do higher-education institutions respond to these changes while seeking to maintain institutional prestige? 

From a Bourdieusian perspective, higher-education functions as a field that monopolizes “legitimate culture” and credentialing, converting institutional recognition into cultural and symbolic capital (Bourdieu, 1986, 1996). Academic success is formally narrated as talent and effort, yet universities systematically reward prior cultural capital and reproduce social hierarchies through forms of symbolic violence (Bourdieu & Passeron, 1990; Nairz-Wirth & Wurzer, 2015). When exclusivity is threatened-through mass expansion or alternative pathways, institutions tend to generate new distinctions and boundary mechanisms to preserve stratification (Bourdieu, 1996). Generative AI disrupts these mechanisms of distinction by allowing high-status performances (e.g., competent academic voice, plausible argumentation) to be simulated without fully internalizing academic modes of inquiry and writing, thereby creating new channels of positioning (e.g., AI tool proficiency, prompt engineering, verification literacy) that may not overlap with traditional cultural capital. 

Signaling theory clarifies why this matters for the labor market: education serves as a signal of potential productivity only if the effective costs of acquiring credentials are sufficiently differentiated by ability, and if false signals are deterred by verification and penalties (Spence, 1973; Connelly et al., 2025). Generative AI compresses these differential costs by lowering the cost of producing key academic outputs (seminar papers, final assignments, presentations), increasing the risk of false signaling unless institutions develop credible verification or penalty mechanisms (Connelly et al., 2025). When grades and degrees rely on outputs that can be generated at low cost, their signaling power may erode, prompting employers to shift attention toward alternative indicators such as work experience, tests, interviews, or work tasks (Van Belle et al., 2020), and increasing the salience of new credential forms (Tholen, 2023; Kässi & Lehdonvirta, 2024). 

Inspired by disruptive innovation theory, we further interpret AI as challenging the value model of higher-education and the organizational “DNA” that makes internal change difficult (Christensen, 1997; Christensen & Eyring, 2011). Rather than immediate or linear reform, adaptation is therefore expected to proceed gradually through drift, layering, conversion, and displacement (Streeck & Thelen, 2005), as institutions contend with pressures to re-establish credibility while maintaining hierarchical differentiation within the field (Bourdieu, 1996). 

Methodologically, this paper contributes a theory-driven conceptual model developed through integrative synthesis and analytical reasoning. We conceptualize generative AI not merely as a technological change but as an institutional disruption problem (Christensen, 1997) and further expand this perspective by examining the implications for academic distinction and potential mechanisms of reproduction (Bourdieu, 1986, 1996; Bourdieu & Passeron, 1990), which are considered in conjunction with the credibility of credentials as labor-market signals under conditions of information asymmetry (Spence, 1973). This interdisciplinary view is complemented by the integration of gradual institutional change theory, enabling us to derive propositions regarding likely institutional adaptation pathways. These pathways may involve processes of layering, drift, and displacement (Streeck & Thelen, 2005). This suggests that specific fields within higher education must navigate these dynamics to reclaim the credibility of their credentials and maintain their status in a rapidly evolving environment. We focus analytically on humanities and social sciences, which are particularly exposed in this context, since text-based performance constitutes a central mechanism of evaluation and distinction.

Our model highlights how AI undermines the credibility of academic performance and reshapes the conditions under which credentials function as reliable signals, thereby impacting the strategies of higher-education institutions in maintaining their status and signaling effectiveness. The model yields three propositions about how these institutions are likely to respond: (Streeck & Thelen, 2005).: (a) layering, most pronounced among most pronounces research universities, where AI-aware assessment, verification, and oral/in-situ examination practices are integrated into existing routines to restore credibility while preserving distinction; (b) conversion, whereby institutions seek to strengthen signaling by tightening ties between academia and industry through long-term, structured research collaborations, reorienting existing institutional arrangements toward labor-market validation of competence. (c) displacement, more likely among lower-status institutions. Here, the traditional 'degree-as-signal' model is partially displaced by labor-market-aligned short-cycle programs, certificates, and modular credentials. From a Bourdieuian perspective, these transformations reflect the dynamic interplay between institutional habitus, capital, and field. Layering illustrates how dominant institutions adapt their practices to preserve their cultural capital and social status in the face of disruption. Drift highlights the erosion of symbolic capital as the value of traditional credentials diminishes, challenging the established hierarchies within the field. Displacement signifies a shift in the types of capital valued by lower-status institutions, as they respond to labor market demands and redefine their educational offerings. This paper raises two core questions: how degree-granting institutions maintain legitimacy tied to competence and status, and whether the rapid integration of AI in higher education may transform the social order. While it remains unclear whether AI constitutes a disruption capable of producing fundamental societal change, a Bourdieusian perspective suggests that AI may instead reinforce existing hierarchies, as it operates as a resource embedded in economic capital and the prevailing field of power.

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