Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field.

This study investigates how perceptual and emotional factors—perceived naturalness, aesthetic pleasure, environmental concern, and disgust—shape consumer acceptance of a human-hair-derived bio-fabricated textile product (a unisex cardholder). In a scenario-based online survey, participants viewed an AI-generated image accompanied by a short vignette. A purposive sample of young adults in Istanbul with prior experience purchasing sustainable textile products was recruited and screened. All constructs were measured with standard Likert-type scales and translated into Turkish using a two-way back-translation procedure. Data were analyzed with PLS-SEM. Model fit was acceptable, and the model accounted for a substantial share of the variance in adoption intention. Aesthetic pleasure showed a clear positive influence on adoption intention, whereas perceived naturalness did not display a direct effect. Environmental concern modestly strengthened the link between naturalness and adoption. Disgust emerged as the dominant moderator, fully conditioning the naturalness pathway and reducing—but not eliminating—the effect of aesthetic pleasure. Together, these findings indicate that perceived naturalness, aesthetic pleasure, environmental concern, and disgust jointly shape adoption intention and that practical emphasis should be placed on reducing feelings of disgust while enhancing aesthetic appeal.