The Lifelong Loop: AI Learning Needs Human Connection and Critical Thinking
A conversation with Dr. Inge de Waard—AI & Learning Lead, veteran EdTech researcher, and active longevity advocate—on bridging the gap between algorithmic skills-matching and human-centric education.
By Louise Servoin · 2026-09-21 · 8 min read
## Dr. Inge de Waard – AI & Learning Lead at EIT InnoEnergy; Founder of Vurig Grijs (Flamboyant Grays)
Expert in educational technologies, self-directed learning, and AI implementation in education. The Open University PhD alumna and Athabasca University Governor General’s Academic Gold Medal recipient. Leader of EIT InnoEnergy’s AI-driven professional upskilling initiatives, bridging the gap between advanced technology design, corporate lifelong learning, and demographic inclusion. Based in Aalter, Belgium.
Some learning professionals understand the code. Others understand the complex psychology of how adults actually absorb knowledge. Rarely do you find an innovator who has successfully traversed both domains while championing the human element in an increasingly automated world.
Dr. Inge de Waard’s trajectory represents a continuous, pioneering effort in educational technology. From deploying early mobile learning personal environments for healthcare workers in remote, low resource regions across three continents, to designing MobiMOOC (the world’s first open online course dedicated to mobile learning) she has spent over two decades redefining how and where learning happens. Today, as the AI & Learning Lead for EIT InnoEnergy, she directs the educational AI-integrated strategy of both Masters+ (the Master school of InnoEnergy - https://mastersplus.innoenergy.com/) and the AI4GD project (a European project to shape the next generation of AI powered energy leaders - https://ai4greendeal.com/). All of this is done alongside her latest endeavor in progress, Vurig Grijs (Flamboyant Grays), a community initiative designed to empower people aged 50 to 105 to reinvent themselves.
We sat down with Inge to discuss why AI literacy is fundamentally a human challenge rather than a technical one, ensuring AI is understood by its user, instead of simply taking its output as truthful and useful. According to Inge the future of work and living belongs to the self-directed, multigenerational learner.
## The Connectivist Catalyst
You’ve been at the forefront of learning innovation since the late 1990s, from early mobile learning initiatives to launching MobiMOOC in 2011. How did those decentralized, connectivist roots shape your current view of AI in education?
They are deeply connected. And this connected upskilling that took place then, is mirrored in how we are upskilling both alumni of our Master students, with professional learners at this time and AI age as well. The key to this connected viewpoint is that InnoEnergy strongly believes that if we offer vanguard Masterplus courses, we will attract the leading professionals of tomorrow. The idea seems simple, but is seldom taking place: our students are taught across different European Tech and Business Universities; then they start working in our business partner networks or InnoEnergy invests in startups of our alumni and Master students. This way, we support students as well as companies throughout the professional needs, as well as careers.
This idea of interconnected learning started earlier in my career. When we launched MobiMOOC in 2011, it was an experiment in connectivism, an exploration of how autonomy, diversity, openness, and interactivity allow learners to collectively build knowledge. We saw that when you give people the freedom to self-direct their learning, a highly resilient educational ecosystem emerges from what initially looks like chaos.
My early work with the Institute of Tropical Medicine in Antwerp, where we deployed mobile personal learning environments for nurses and doctors in low-resource settings in Asia, Africa and Latin America WAP-enable phones, and later smartphones and portable solar chargers, taught me a vital lesson: technology is only as good as its situational relevance. That pilot won the Brandon Hall Gold Medal in 2010 because it wasn't just about the hardware; it was about context shaped by the people living in it, and engaging all partners.
## Full Knowledge Shift
What do you see as the biggest shift in knowledge and learning with AI versus other Educational Technologies?
The big shift in knowledge is that everyone will be using AI in the near future. And AI is fallible and a huge support at the same time. So again, everyone is involved (students, professionals and citizens). This means we all need to understand what AI can and cannot do. AI is not a tool, it impacts our entire professional society, and it demands an increased critical awareness. To me it is critical to show people how to resist taking the easy AI outputs as correct results. Using AI in an empowered way means weighing any outcomes so that we can practice our ongoing critical, human thinking.
While learning in the past came from experts that knew and had time to validate any new knowledge. AI offers a double sided sword when it comes to knowledge. On the one hand, AI is the ultimate tool for scaling that contextual, self-directed learning. It is the ultimate dynamic library, with ample resources. This offers an alternative to push one-size-fits-all content. Instead, it can serve as an engine for personalized, adaptive, and highly situated content support. Allowing us to transition from a sometimes rigid "one-to-many" model of instruction to a customized, lifelong learning loop. On the other hand, too many people are led astray by what AI pushes on to us in the first instance, they forget to include additional sources or question AI for its input. AI is built to round up, it is constructed to use semantic language that gives the user the impression that AI knows, and it pulls together snippets, not necessarily the real facts. Forget hallucinations, these are wrong assumptions taken for granted by AI that we - as users - need to double check in order to get the best results. GenAI is not based on mathematical precision, it is built on patterns in natural language, therefore it is fallible. This isn’t necessarily a bad thing, it just means we need to keep on thinking. And as learners we need to embrace that learning demands effort. Humans learn as much from failure as from success, as well as struggling to understand what specific knowledge might mean.
## The AI Skills Equation
At EIT InnoEnergy, you lead the transition to integrating AI in more educational projects, settings and at company level. How does this system operate, and what makes it different from traditional professional training?
Professional training was solid, stable, but with AI the only constant is change itself. We increase AI skills for our students, teachers and colleagues alike, preparing them for change. This is not simply using AI in order to get to results quicker, it is testing AI outcomes, pushing the boundaries that it delivers us to grow above the average user or professional. As a result we emphasize diversity, both from a learning design perspective as well as from a representational perspective. Diversity, which is at the core of nature’s success, is replicated by the diversity in learning preferences and ways to achieve learning success in humans, with and without AI.
Each professional takes what is useful for them to be better prepared for the future workspace. For instance, enabling students to be better prepared to start their own successful startup, they can immediately leapfrog into mid-career positions (as the early career jobs are taken up by AI). For teachers as well as my colleagues at InnoEnergy, it is pivotal to stay on top of their field by knowing the impact of AI on their sector, as well as develop the skills they need to stay relevant in their future workspace. The bonus is that these skills are not limited to InnoEnergy, but they expand into our full ecosystem of more than 1400 European academic and industry partners.
Professional training for AI means that the core of the AI skills are generic and take into account the full impact of AI on the different professional roles, only after this initial elevation of AI skills do we go deeper into sectorspecific skills. For instance, do you know the actual benefits and risks of AI (e.g. on the plus side: automation, process enhancement, … and on the downside: redundancy of existing jobs, decreasing cognitive capacities, …). People need to become more aware, and dare to really investigate. In order to be prepared for this new transformed professional world, we need to know what works and what does not.
With AI we know that there is no longer one single content truth that will stand for years, at least not when it comes to innovative content. AI changes all the time, innovations using AI are constantly evolving, and combinations of new possibilities are forever changing. This means there is no right or wrong, the only constant and truth is change itself.
"Diversity, which is at the core of nature’s success, is replicated by the diversity in learning preferences and ways to achieve learning success in humans, with and without AI." — Dr. Inge de Waard
## Reskilling the Flamboyant Grays
You recently founded Vurig Grijs (Flamboyant Grays), a community project in progress for role models aged 50 to 105 who have completely reinvented their lives. Why is active longevity such a critical topic in the era of artificial intelligence?
We are living through a massive demographic shift alongside an unprecedented technological revolution. Historically, society has written off professionals once they cross the age of 50, assuming their capacity for learning or adaptation has peaked. This is not just ageist; it is an incredible waste of human capital.
Through Vurig Grijs and its predecessor Secret Shakers, I have been researching what makes new life and career changes successful for individuals over 50, and how they approach reinventing themselves. What I find is that some of these older, wiser minds possess an incredible wealth of experience and contextual judgment. Skills that AI has a hard time replicating.
To me, their resilience, as well as drive to come up with new initiatives giving additional meaning to their lives is essential if we want to support a human, citizen-based society that has a place for all: all generations, all people. This research shows that AI is taken up by that demographic, and also, that they have the means and understanding to come up with new initiatives for themselves despite AI. Let us not forget, that in case AI does round up some jobs, it is pivotal that we understand how we can make our lives meaningful in case professions change or might even be reduced.
True educational sustainability requires us to design adaptive, multi-generational learning environments. Reskilling is not a phase you finish in your youth; it is a lifetime practice.
## The Golden Thread
We often see a gap between what technology can do and what professionals actually adopt. How do we ensure AI becomes a 'co-pilot' that elevates human judgment rather than a tool that encourages intellectual passivity?
Professionals and citizens alike need to resist the temptation to take whatever AI delivers us for granted. We must also ensure human skills shine in the age of AI. GenAI is built to give closed round up answers, its answers are phrased in such a way that we take it for granted, and … it misses the creativity to truly come up with innovations by itself. This means every human needs to explore GenAI, enter into discussions with it, push it, question it … and have fun while doing so. My go to activity is having a philosophical dialogue with it, explicitly telling it not to round up, and not to assume it knows me… try it, the process becomes the eye opener.
Following one single idea has never delivered a rich society, it is only by questioning it, exploring it, and sharing stories that we invent the best that we can.
Tags: AI in Education, EdTech, Lifelong Learning, InnoEnergy, The Open University, Upskilling, Active Longevity, Diversity and Inclusion, AI4GreenDeal