After more than 32 years in corporate and professional language learning, ELAM has witnessed numerous “game-changing” technologies emerge and fade. Some quietly disappeared and others genuinely changed how people learn. From the first digital resources to today’s fully online, industry-specific programmes, technology has steadily reshaped professional language training. Generative AI feels different again. Not because it replaces good teaching—but because it changes what is possible, and how we need to think about learning.
There’s no denying the opportunity Generative AI brings. Personalization is perhaps the most striking shift. For the first time, learning can adapt instantly and individually, at scale. Beginners benefit from tools like Duolingo, which adjust difficulty in real-time, helping them stay motivated rather than feeling overwhelmed. More advanced learners turn to writing assistants and conversational agents for immediate feedback. As Dr Elsa Petit notes, “For advanced learners, writing assistants like Grammarly and conversational agents such as ChatGPT provide feedback on tone and coherence. These tools can accelerate progress, but cultural nuance and idiomatic expression still require human judgment” (Almusharraf & Khlaif, 2024). In professional contexts, where credibility often hinges on tone, precision, and cultural awareness, that judgment cannot be automated.
We also see the impact of AI on engagement and confidence. In our programmes, AI-supported role-plays, simulations, and scenario work give learners space to try, adjust, and try again—without the pressure that often comes with speaking a foreign language at work.” This mirrors wider research showing that AI-based practice can reduce anxiety and encourage creativity by offering low-stakes opportunities to practice speaking and writing” (Zhang et al., 2024). Add in gamified activities and a mix of videos, transcripts, and visuals, and learning becomes more flexible and inclusive for people with different styles and needs.
But the picture isn’t entirely straightforward. Bias and cultural sensitivity remain real concerns. AI tools still struggle with idiomatic language and can produce content that feels flat, inaccurate, or culturally limited. Used well, these limitations become teaching moments. Used poorly, they undermine learning. That’s why task design matters. ”Language learning needs to focus less on polished outputs and more on process, reflection, and critical thinking” (Koh & Doroudi, 2023). At ELAM, that means asking learners not just what they wrote, but why they wrote it—and how they would improve it.
Ethical and policy discussions reinforce this approach. There is a growing consensus that Generative AI literacy is now essential. Harvard Business School highlights the importance of responsible use and understanding limitations. HEPI and Zenodo call for curriculum reform. UNESCO continues to advocate for human-centred pedagogy. All points in the same direction: AI should support learning, not replace judgment or expertise.
This shift also changes the role of the trainer. Language professionals now need strong digital and AI literacy themselves—not to chase trends, but to guide learners critically and confidently. Our ELAM Connect summit is the best testimonial to this. When used thoughtfully, AI enables richer lessons, more targeted feedback, and deeper cultural exploration. Without that guidance, it risks encouraging surface-level learning or quiet dependence.
After three decades of practice, ELAM’s view is clear. Generative AI is a powerful addition to language learning, but it is not the heart of it. Real communication, cultural understanding, and critical thinking remain deeply human. AI can speed things up. It’s experienced educators who make sure learning still means something.
