On November 30, 2022, the launch of ChatGPT changed the game. It wasn’t a gradual introduction; it was a seismic rupture. If there was one phrase that resonated powerfully through the halls of Bett UK this year, it was simple and blunt: “Shift happens.”
As learning leaders at Globant, we have a responsibility to look beyond the technological hype. After analyzing hours of conferences and debates with industry leaders in London, I concluded: AI is not just an efficiency tool; it is a cultural challenge that forces us to redefine what it means to learn and create in the digital age. This shift demands a deeper reflection on how AI learning reshapes cognitive effort, authorship, and professional growth, especially as AI becomes embedded in both education and enterprise environments.
Here are the critical lessons for both the field of education and our Learning & Development strategies.
1. The dopamine trap and the “Slippery Slope”
One of the most lucid analyses came from Annie Chechitelli (CPO of Turnitin) and pedagogical experts, who pointed out a fundamental human risk: the search for the “dopamine shortcut.” Learning is a difficult cognitive process; it involves frustration, error, and effort. In the age of LLM learning, where large language models generate instant outputs, the temptation to bypass cognitive struggle is stronger than ever. AI offers students and employees a quick exit to avoid that anxiety and obtain instant gratification.
The danger, backed by preliminary studies from MIT, is the “slippery slope”: once we outsource our thinking once, we tend to do it more and more, reducing our cognitive engagement and critical capacity.
The Challenge for Companies in the corporate environment is that this translates into teams that generate impeccable reports, code, or strategies in seconds, but lose the capacity to understand the “why” behind those solutions. If we allow AI to think for us, we run the risk of having a workforce that knows how to deliver, but has forgotten how to reason.
2. From surveillance to transparency (transparency over detection)
For years, the instinctive response to disruptive technology has been prohibition or surveillance. However, in the AI era, attempting to “detect” the use of algorithms is a losing and counterproductive battle. The key is not detection, but transparency.
The new frontier does not focus on judging the final deliverable, but on visualizing the writing process: the drafts, the edits, the time spent, and the iteration.
We must move from evaluating the final result (the product) to valuing the archaeology of the work. In our Upskilling programs, we must ask “How did you use AI to enhance your judgment?” rather than if it was used at all. Transparency fosters honest conversations about the ethical and productive use of technology, instead of creating a game of cat and mouse.
3. A competency framework for the “Learner-Driven”
From the Education University of Hong Kong comes a fascinating model that we can adapt to the corporate world: the Generative AI Competency Framework. This model proposes four critical dimensions (Literacy, Design, Teaching, and Assessment) and, most importantly, distinguishes between the instructor’s use and how the instructor empowers the learner.
The goal is to move from an instructor-centered model to a “Learner-Driven” one. But be careful: we cannot assume that employees have the innate self-efficacy to guide their own learning with AI. They need scaffolding.
For us, this implies designing training paths where:
- Literacy: Is not just knowing how to prompt, but understanding ethical implications and biases.
- Process Assessment: Designing tasks where the employee must iterate with the AI, question its hallucinations, and refine the output based on their human experience.
This framework aligns with the didactic and pedagogical proposal that sustains our AI Talent Shift program, which we developed at Globant to accompany clients and organizations in the adoption of a critical, efficient, and human use of AI.
4. Intentional learning: insight, discernment, and creativity
If AI can do the work, what is the role of the human? The answer lies in Intentional Learning, a concept masterfully broken down in one of the main keynotes. For learning to be relevant today, it must be based on three pillars that AI cannot fully replicate:
- Insight (Vision): Deeply understanding what is being learned and why it is valuable to the business.
- Discernment: The capacity to judge truthfulness and quality. AI “hallucinates” and lies with confidence. Our teams need expert judgment to validate what the machine proposes.
- Creativity with Purpose: It is not about generating mass content, but creating something with meaning and human context.
Empathy is our superpower
Despite the technological sophistication, the final conclusion of Bett UK was profoundly humanistic. AI is a data machine; it does not understand emotional context, frustration, or cultural nuances.
As leaders and mentors of talent, our role has changed. We are no longer just distributors of knowledge; we are architects of learning. Our function is to design environments where it is safe to fail, where the “painful” but necessary cognitive process is valued, and where technology is used to free us from administrative tasks to give us more time for the one thing that is not scalable: human connection.
At Globant, adopting the latest technology means ensuring that, by using it, our Globers become more critical, more creative, and, paradoxically, more human.
This analysis is based on sessions and panels presented at Bett UK, including insights from Turnitin, Ufi VocTech Trust, and the Education University of Hong Kong.
