Integrating digital technologies in interpreter education - Lihong Pan

Research aim

To explore how digital technologies (e.g., AI-powered tools, virtual reality, speech recognition, and adaptive learning systems) enhance cognitive processing, task performance, and skill acquisition in interpreter/translator training, and start a new module in interpreting - Simultaneous Interpreting with AI.

Key research questions

1. How do digital interpreting tools (e.g., AI-based glossaries, automatic speech recognition, and virtual interpreting simulations) support cognitive load management in student interpreters?
2. What are the pedagogical benefits and challenges of integrating AI-driven platforms, VR-based interpreting environments, and speech-to-text systems in interpreter education?
3. How does technology-enhanced interpreter training impact student engagement, memory retention, and real-time decision-making in consecutive and simultaneous interpreting?
4. What role do adaptive learning technologies play in providing personalised feedback and self-assessment for interpreting students?

Methodology

• Mixed-methods approach: Combining qualitative (interviews with alumni and current students, classroom observations, student reflections) and quantitative (pre/post-tests, reaction time analysis, accuracy rates) data.
• Experimental study: Comparing traditional vs. technology-enhanced interpreting training, using VR interpreting booths, AI-assisted note-taking, and interactive digital platforms.
• Longitudinal case studies: Tracking students over time to measure improvements in fluency, accuracy, and cognitive processing efficiency.

Significance of the study

This research will provide evidence-based insights into how digital technologies can optimise interpreter education, inform curriculum design, and enhance student interpreters' cognitive agility, retention, and professional readiness in both academic and professional settings.