This study aimed at conceptualizing the transformation of creative authorship within algorithmic educational environments, to determine if the distinctive contributions of humans, artificial intelligence (AI), and institutional accountability could be separated analytically in human and AI co-created educational outputs. A documentary content analysis of 30 documents (published or updated between 2023 and 2026) containing academic literature, institutional policy-oriented documents, and analytical reports regarding the role of generative artificial intelligence within education, academic writing, creative learning, assessment, and copyright regulation was conducted. The analysis revealed that the notion of creative authorship does not characterize a completely independent act of authorship on the part of an author nor does it represent an entire substitution of an author by an algorithm; it does exist as a distributed and therefore accountable process that is shaped by human intention, machine-generated support, institutional principles, and the expectations of the assessment process. Five predominant thematic clusters were identified from the 30 documents: pedagogical applications of generative AI; creative cognition and ideation; distributed authorship; academic integrity; and the legal/normative regulation of AI-generated outputs. Eight sources were focused on teaching and eight were focused on evaluating academic writing practices within an academic setting. Seven articles were also analyzed within the framework of academic integrity. Five articles analyzed were concerned with creative cognition; five were based on collaborative processes of authorship; five articles analyzed legal-normative regulation. The results clearly indicate that the majority of sources analysed provide evidence that artificial intelligence works primarily as 1) an educational support system; 2) a partner/co-creator in the learning process; 3) an evaluator for assessing students; and 4) a regulatory body when assessing/validating submitted work. Based on these results, this research recommends that educational institutions no longer simply prohibit artificial intelligence use or seek to identify it within student work and instead focus on developing new forms of assessment that reflect human contribution, revision, responsibility, and originality within algorithmic learning environments.
