Digital Teaching Competencies and Algorithmic Ethics in Learning Assessments Within Virtual Environments: A Systematic Review

The integration of algorithm-mediated grading in virtual higher education demands an urgent reevaluation of Digital Teaching Competencies to address critical algorithmic opacity. This study aims to systematize available scientific evidence on the intersection between DTC and Algorithmic Ethics in learning assessment within virtual environments. Following PRISMA 2020 guidelines and strict eligibility criteria, the search was executed in Web of Science and Scopus using Boolean operators, screening 262 initial records down to a final corpus of six empirical studies; the protocol was registered in OSF. Regarding results, the reviewed literature suggests that teachers may primarily employ technical and operational DTC for automated assessment design, leaving critical evaluation potentially underdeveloped. Furthermore, empirical research frequently highlights ethical dilemmas concerning data privacy risks, potential automation bias, and sociotechnical discrimination against marginalized students. Additionally, higher levels of advanced DTC and data literacy appear to serve as relevant factors for mitigating these ethical risks and auditing automated outcomes. To address this, the literature proposes implementing institutional algorithmic governance frameworks, systematic socio-ethical teacher training, and shifting automated designs toward formative, transparent feedback models. In conclusion, ethical automated assessment seems to rely substantially on individual teaching competence due to institutional policy gaps, highlighting a potential need for a transition toward techno-pedagogical frameworks focused on algorithmic justice.