Artificial Intelligence in Aquaculture: Applications, Research Gaps, and Implications for Professional Education and Governance — A Systematic Literature Review

Artificial intelligence (AI) is increasingly used in aquaculture to support monitoring, prediction, automation, and production management, while also creating new requirements for professional competence, ethical oversight, and governance. This systematic literature review, conducted under an integrative approach and reported in accordance with PRISMA 2020, examined practical applications of AI and their implications for professional education and governance in aquaculture. Searches in Scopus, Web of Science, PubMed, and Google Scholar identified 877 records, of which 97 met the eligibility criteria and were included in the qualitative synthesis. The evidence was concentrated on technical applications, particularly fish detection and biomass estimation, water and environmental monitoring, automated production systems, health and biosecurity, and feeding and growth optimization. Direct educational evidence was scarce: only one study examined aquaculture professionals' knowledge and interest in AI, and no included study evaluated formal curricular interventions or learning outcomes in Fisheries Engineering. The synthesis also identified concerns involving data quality, algorithmic bias, technological exclusion, privacy, standardization, and accountability. These findings indicate that responsible adoption requires locally representative data, transparent validation, digital and data literacy, human oversight, and coordinated governance. AI has substantial potential to support aquaculture, but its contribution to professional education remains an important and insufficiently investigated research field.