Introduction: A Decade of Learning and Reinvention
More than 10 years ago, I began exploring how technology could enhance the way we learn. My background in applied linguistics taught me how to analyze meaning, interaction, and context. Now, as an instructional designer, I’ve spent years turning those insights into practical solutions. I wanted to create learning experiences that were alive, purposeful, and responsive.
Over this decade, my journey has been defined by curiosity and reinvention. From the early days of mobile learning, when we were just beginning to understand what it meant to put knowledge “in the palm of your hand,” to today, where I am building adaptive systems that personalize learning in real time, one belief has remained constant: design and technology must serve the human experience of learning.
What started as an exploration of mobile learning capabilities evolved into a deeper understanding of personalization, context-aware learning, and ultimately, the transformative potential of adaptive AI in education. Each project, each challenge, and every late night sketching prototypes contributed to a growing vision of what learning could become when technology truly serves the learner's individual needs.
Mobile Learning: Designing for the Moment of Need
In 2013, I wrote "Developing a Mobile Learning Strategy" for ATD. At that time, mobile learning was still emerging in corporate training. The publication came from real-world problems I faced with clients. They saw that their workforce was becoming more mobile but had a hard time providing training solutions that fit this new reality.
The key insight behind this work was that mobile learning wasn't just about fitting e-learning content onto smaller screens. Mobile learning required "completely rethinking our approach to instructional design, graphic design, user experience, and information presentation (Villar, 2013)." The unique characteristics of mobile learners—their need for immediacy, their contextual constraints, and their brief attention spans—demanded fundamentally different design principles.
Through extensive research and hands-on experience, I identified three critical elements that must align for mobile learning to succeed:
• The Learner: understanding mindsets and needs, and making decisions from the learners’ perspective.
• The Need: identifying performance problems that just-in-time information can solve.
• The Context: leveraging the learner’s environment to enhance knowledge and action.
The technical constraints of 2013 mobile devices forced us to embrace what we now recognize as fundamental principles of effective learning design. The concepts of immediacy, interactivity, and immersion that I advocated for in mobile learning weren't just technical necessities—they were pedagogical improvements.
• Immediacy involves creating "short bursts of activities" for learners to access when needed.
• Interactivity makes learning active, engaging, and task-driven.
• Immersion uses responsive design, mobile sensors, and AR to anchor learning in real environments.
Looking back, this work was about much more than mobile devices. It was my first public statement that learning design must adapt to the learner’s reality, not the other way around. It was about honoring the human side of technology, ensuring that every tool serves a purpose: helping people learn, grow, and make better decisions when it matters most.
From Mobile to AI: The Evolution Toward Real-Time Adaptation
The same questions that drove my work in mobile learning — What does the learner need? In which context? How can instructional design make the experience meaningful? — are the ones guiding me today as I explore the potential of artificial intelligence. The lessons learned from mobile learning — the importance of context, the power of micro-interactions, and the need for systems that adapt to individual learner needs — laid the groundwork for my agency’s new product: ARI™ (Adaptive Response in Real Time).
In 2018, I began sketching the idea of a system that could listen to the learner, understand their unique profile, and adapt the learning path in real time. I called it ARI — Adaptive Response in Real Time. At that moment, bringing the vision to life would have required building my own large language model — something out of reach for a solo entrepreneur. But I held on to the vision, and today, ARI is no longer just an idea — it is a working system.
What makes ARI different is that it is not a one-size-fits-all course with surface-level personalization. ARI uses the learner’s own inputs—like their experience, language, and goals—to dynamically generate a tailored learning path. Each decision point, each piece of feedback, is shaped by real-time data and meaningful design.
Just as mobile learning once freed us from the classroom, ARI frees us from rigid, linear courses. It demonstrates how AI, when guided by strong pedagogical principles and ethical governance, can make digital learning more human.
And together with the great enhancement that personalization can bring to the learner’s journey, ARI represents a fundamental shift in how we think about the role of instructional designers. Rather than creating static learning experiences, we're becoming architects of adaptive learning systems—designing the frameworks, rules, and interactions that enable AI to create personalized experiences at scale.
In this context, AI doesn't diminish the importance of human expertise; it amplifies it. The instructional designer's understanding of learning theory, audience analysis, and effective pedagogical approaches becomes even more critical when embedded in AI-driven learning systems.
Looking Ahead: Designing the Next Decade
If the last decade was about mobility and accessibility, the next will be about adaptivity and personalization. ARI is only the beginning. Its framework can evolve into new forms of digital products where learning is not imposed or locked in a fixed format but co-created in real time.
Imagine a future where:
• Courses adapt to align with the learner’s journey, using real-time data to shape each step.
• AI extends human capability, making possible what was once out of reach for both learners and designers.
• Personalization remains ethical and purposeful, guided by creative design principles that keep learning deeply human.
This vision comes with challenges. AI raises questions about transparency, bias, and trust. But these challenges are not barriers — they are design opportunities. They remind us that innovation must be paired with responsibility.
Ten years ago, I dreamed of making learning portable. Today, I am building systems that make learning personal. Tomorrow, the journey will continue — fueled by curiosity, creativity, and the unshakable belief that technology, when used with care, can enhance learning experiences.
The most exciting developments still lie ahead—as we explore what becomes possible when technology truly responds to the unique potential of every learner.
Author’s Note
Writing this piece has been more than a professional reflection — it’s been a personal journey through the past decade of my life as a designer, strategist, and lifelong learner. Every step, from exploring mobile learning to building ARI, has taught me that innovation is not about chasing technology, but about honoring the human experience at the heart of learning.
Thank you for being part of this journey. Whether you’ve followed my work for years or are just discovering it, I hope these reflections inspire you to imagine what’s possible when creativity, design, and technology come together with purpose.
- Mayra
