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From Scholars for Scholars

Gender bias in generative artificial intelligence (GenAI) is both a technical and a social phenomenon: it emerges from historically patterned data, model design, and interactions in institutional use, and it cannot be understood by engineering or by social critique alone. This critical integrative review develops a more differentiated account. It connects feminist epistemology, Science and Technology Studies, critical AI scholarship, natural language processing, and governance research to examine five levels: historical knowledge production, technical representation and generation, benchmark evaluation, institutional deployment, and accountability. The review explains tokenization, next-token prediction, transformers, and the transition from static embeddings to contemporary language models before assessing evidence from standard fairness tests—coreference tests (WinoBias), sentence-pair tests (CrowS-Pairs), and stereotype tests (StereoSet)—as well as open-ended generation, multilingual testing, and text-to-image systems. It shows that measured bias varies with task, prompt, language, model version, and metric. What a test records and what that record means are therefore distinct questions: measurements are situated and depend on the instrument, and their interpretation draws on theory rather than following from the numbers alone. Evidence from employment, education, healthcare, and translation further indicates that the relevant unit of analysis is the model-in-context—the model together with the institution and workflow in which its outputs are used. Technical mitigation can reduce specific harms but does not repair unequal criteria, incomplete evidence bases, or weak institutional accountability. The review proposes a multilevel governance approach combining technical evaluation, documentation, professional and community oversight, appeals, remedies, and public-interest knowledge infrastructure. Its distinctive contribution is to connect three observations usually kept apart—how bias is measured, how generative systems concentrate epistemic authority, and how statistical learning is oriented toward past data—and to show why democratic and feminist governance can keep alternative technological futures open.

Competence frameworks function as structured knowledge codification systems: authoritative, domain-wide representations of professional knowledge that define what counts as competence within a field and how it should be organised. As such, they constitute a form of professional knowledge encyclopaedia, structured to comprehensively map an emerging or established knowledge domain. This paper examines how eight EU and international competence frameworks codify AI-related knowledge for STEAM educators, using a dual analytical lens comprising AI-TPACK and STEAMCompEdu. Read through this dual lens, the cross-framework analysis identifies four knowledge dimensions insufficiently addressed in the existing landscape: interdisciplinary pedagogical knowledge, creative-aesthetic AI knowledge, ethics as embedded disciplinary practice, and inquiry-oriented AI pedagogy. Based on these findings, the paper proposes three emerging theoretical extensions to AI-TPACK to address the identified lacunae and discusses implications for the design of structured professional knowledge codification systems that better represent complex, interdisciplinary, and rapidly evolving knowledge domains.