AI Literacy for Legal Translation: Developing Digital Resilience

Synthesis: Proposes a four-component AI literacy framework for legal translation professionals: conceptual AI knowledge, technical operational skills, critical evaluation competencies, and ethical AI Governance awareness. Argues generative AI extends rather than replaces professional translation competence. Identifies linguistic, technical, legal, ethical and cognitive risks of AI in legal translation and maps them to literacy components. Framework applicable beyond legal domain to other professional education contexts where AI augments expert judgment. AI Literacy, Generative AI, Workplace Learning, Human-in-the-Loop, and Higher Education. Proposes a four-component AI literacy framework for legal translation professionals: conceptual AI knowledge, technical operational skills, critical evaluation competencies, and ethical governance awareness. Argues generative AI extends rather than replaces professional translation competence. Identifies linguistic, technical, legal, ethical and cognitive risks of AI in legal translation and maps them to literacy components. Framework applicable beyond legal domain to other professional education contexts where AI augments expert judgment.

What this means for practice

• Instructors. Sequence AI out of the early curriculum: have students build core legal translation competence through human from-scratch translation before you introduce AI-assisted workflows.
• Require an AI-or-human risk assessment as the first step of every assignment, in which students decide whether a task is safe to delegate and how much human intervention it needs.
• Teach post-editing and verification as graded skills: have students detect hallucinations, mistranslations, terminological inconsistencies, and breaches of legal drafting conventions in AI output, and name the automation-bias and illusion-of-quality pressures that make those errors easy to miss.
• Move prompting instruction beyond zero-shot requests. In the author's pilot survey of 22 first-year MA students, most prompts were simple zero-shot or thinly contextualized requests and iterative interaction with the model was rare.
• Invite practicing legal translators to discuss confidentiality, liability, and access-to-justice questions, so students see the professional judgment the strategic dimension requires rather than only technical operation.

Limitations

• The framework is conceptual: it is assembled from the risk literature and the author's own teaching experience, and Biel states explicitly that it "requires empirical validation" — nothing in the chapter tests whether the four dimensions predict translator performance or resilience.
• The chapter's only empirical element is a pilot survey of 22 first-year MA students in a single program at the University of Warsaw, run in March 2026 with open-ended questions — self-reported, one cohort, no comparison group, and small enough to be diagnostic rather than evidential.
• The risk profile is extrapolated rather than measured: the author notes that empirical studies of GenAI adoption among legal translators were not yet available, so most risk claims are carried over from NMT-era research.
• The transfer claim — that the framework applies beyond legal translation to other professional education contexts where AI augments expert judgment — is asserted, not tested in any other domain.

Citation

Łucja Biel (2026). AI Literacy for Legal Translation: Developing Digital Resilience. Book chapter.