Human Factors in AI-Assisted Interpreter Training: A Design Science Approach to Human-Centred Educational Technology

Source abstract

The increasing integration of artificial intelligence (AI) into education creates opportunities to support learning, but its educational value depends not only on technological capabilities but also on human–AI interaction. This issue is particularly relevant in interpreter training, where learning involves high cognitive demands, contextual decision-making, and time-sensitive activities. This study investigates how Human-Centred Artificial Intelligence (HCAI) principles can guide the design of AI-assisted educational environments for interpreter training. A Design Science Research (DSR) methodology was adopted to develop and evaluate an AI-assisted learning environment integrating AI capabilities across the interpreter training cycle. Drawing on literature on Computer-Assisted Interpreting, HCAI, human–AI interaction, technology acceptance, and interpreting cognition, five design principles were derived: learner agency and human control, transparency and calibrated trust, protection of cognitive resources, contextualised assistance, and AI as a support for learning. These principles were operationalised in the artefact and evaluated through heuristic evaluation and a technology acceptance study involving 42 professional interpreters. Results show that perceived usefulness alone is insufficient to explain technology acceptance, with trust, reliability, ease of use, latency, workflow integration, and cognitive demands also shaping adoption. The study contributes a human-centred framework for aligning AI capabilities with learners’ needs and educational objectives.