In Macau, a city renowned for its cultural and linguistic diversity, experts believe that artificial intelligence—and especially generative models—can play a transformative role in education, translation, and public service. In a special report, Barry Reynolds, Professor of English Language Education at the University of Macau, and veteran interpreter Leo Stepanov discussed the evolving potential of artificial intelligence (referred to locally as ´man-made wisdom´) within Macau’s multilingual context.
Reynolds highlighted the limitations of traditional language teaching methods prevalent in many of Macau’s non-tertiary schools, noting their lack of effectiveness. He argued that generative artificial intelligence offers opportunities to revitalize language education by promoting dynamic, meaningful, and context-rich approaches rather than rote memorization. Reynolds asserted that with correct integration, these tools could enhance proficiency by fostering authentic language use, provided educators guide students to utilize them thoughtfully.
Stepanov, drawing on decades of experience in the translation industry, underscored the ability of artificial intelligence to automate routine and repetitive tasks—such as drafting technical manuals and government documentation—which frees human linguists to focus on creative and complex assignments. He envisions a future where digital assistants with instant translation capabilities could streamline multilingual public services across Macau, reducing the need for front-desk staff and improving accessibility for residents. While both experts recognized that the widespread adoption of artificial intelligence will impact certain job roles, they agreed that technology remains a tool rather than a replacement: human translators offer crucial skills in cultural context and emotional intelligence that current artificial intelligence systems cannot fully replicate. Reynolds concluded that any potential decline in language proficiency is linked not to technology but to outdated pedagogical practices, affirming that proficiency is likely to improve as artificial intelligence and teaching methods co-evolve.
