In Conversation With Michelle Weise: Future EdTech Trends 2026

An Interview with Michelle Weise: Navigating the AI-Driven Learning Frontier

The conversation with Michelle Weise felt less like an interview and more like a guided expedition into the future. Her perspective, grounded in extensive research yet articulated with the clarity of someone who understands practical implications, offered a vital compass for anyone grappling with the accelerating pace of change in online learning. We began by discussing the fundamental shifts AI is driving, not just in what we learn, but how.

Interviewer: Michelle, it feels like we’re at an inflection point. Tools like ChatGPT and specialized AI tutors are no longer niche; they’re becoming part of the mainstream learning toolkit. From your perspective, what’s the most significant cognitive shift AI is enabling in how individuals approach learning today?

Michelle Weise: The most significant shift is towards accelerated, hyper-personalized cognitive automation – specifically, around foundational learning principles like retrieval practice and spaced repetition. For decades, cognitive science has shown us that active recall beats passive re-reading, and that distributing learning over time enhances long-term retention. Yet, implementing these effectively at scale, for diverse learners, has always been a challenge. It required significant discipline, self-awareness, and often, an external tutor or a very well-designed course.

Now, AI changes that equation entirely. Consider a tool like Anki, which implements spaced repetition. It’s powerful, but it requires manual input for flashcards. Imagine an AI tutor that reads your course material, generates context-rich retrieval questions at optimal intervals tailored to your forgotten curve, and adapts the difficulty based on your performance. This isn’t just about rote memorization; it’s about the AI understanding your specific knowledge gaps and presenting information in a way that maximizes neuroplasticity. We’re seeing this in nascent forms with platforms like Khanmigo or even custom GPTs trained on specific syllabi, which can transform study sessions from passive review into highly effective, adaptive practice. This isn’t just enhancing; it’s automating the hard, consistent work of applying cognitive science.

Interviewer: That’s powerful – automating the application of learning science. But with so many tools emerging, how do learners avoid the paralysis of choice or information overload? It feels like we’re constantly sifting through shiny new objects.

Michelle Weise: That’s a critical challenge, and it speaks to the need for meta-learning and AI literacy. My own experience has been a journey of trial and error. I remember initially trying to integrate every new AI feature into my research workflow, only to find myself drowning in tabs and prompts. The “imperfection” was trying to force-fit technology rather than starting with a clear learning objective. What I’ve learned, and what I advise others, is to focus on specific, high-leverage learning tasks.

For example, when I needed to quickly grasp a new programming language for a personal project – something completely outside my core expertise – I didn’t try to master every AI coding assistant. Instead, I picked one, let’s say a specialized GPT with a code interpreter, and used it as a conversational tutor. I’d ask it to explain concepts, debug small snippets, and even generate simple exercises. This wasn’t about replacing the learning, but augmenting it. The AI became a personalized, infinitely patient expert available 24/7. This focused application cut my learning time dramatically compared to traditional methods.

The key is behavioral design: integrate AI where it significantly reduces cognitive load for specific tasks, not just for the sake of using AI. Think about tools like Notion AI for summarizing complex research papers, allowing you to quickly grasp core arguments and identify areas for deeper human-led inquiry. Or using an AI tool to generate varied practice problems, shifting your mental energy from problem-hunting to problem-solving. This targeted application transforms “tool overwhelm” into “focused leverage.”

Interviewer: Beyond individual learning habits, what does this mean for the skills people need to cultivate for the future of work? Are we simply learning to prompt better, or is something deeper at play?

Michelle Weise: It’s far deeper than prompt engineering. The World Economic Forum’s Future of Jobs Report consistently highlights skills like critical thinking, analytical thinking, creativity, and digital literacy as paramount. What AI does is elevate the importance of these distinctly human skills. If AI can handle information synthesis and pattern recognition at scale, our value shifts to what AI cannot do: formulate truly novel questions, apply nuanced ethical judgment, generate original creative concepts, and foster deep human connection.

Consider a scenario where an AI can draft an entire business report. Your role isn’t to write the report; it’s to critically evaluate its biases, enhance its strategic insights with your unique industry understanding, and infuse it with persuasive storytelling that resonates emotionally with human stakeholders. You’re moving from execution to meta-cognition, from information processing to strategic discernment. This is why institutions like MIT Media Lab and Harvard’s Learning Lab are focusing on how to teach “human-AI collaboration” – not just “AI skills” – emphasizing the symbiotic relationship rather than replacement. Future skills are about knowing when to trust AI, how to challenge its outputs, and where to inject uniquely human brilliance.

Interviewer: It sounds like a re-calibration of our human capabilities in partnership with technology. But what about the role of the human instructor or mentor in this AI-enriched environment? Does their role diminish, or does it evolve?

Michelle Weise: The human element becomes even more critical, though its role transforms. AI can deliver personalized content, assess knowledge, and even simulate conversations. But AI cannot yet provide true mentorship, emotional support, contextual empathy, or inspire the kind of deep curiosity that often stems from human connection. As an educator, if an AI can handle the repetitive tasks of grading quizzes or explaining basic concepts, it frees me up to focus on higher-order thinking: facilitating complex discussions, guiding project-based learning, offering nuanced feedback that addresses a student’s metacognitive processes, and fostering a sense of community.

Think of it this way: AI optimizes the “what” and “how” of knowledge transmission, while human mentors focus on the “why” and “for whom.” They become guides for complex problem-solving, ethical dilemmas, and career navigation – areas where emotional intelligence and lived experience are irreplaceable. We’ll see a surge in “AI-augmented teaching,” where the teacher orchestrates the learning experience, leveraging AI as a powerful assistant to personalize and scale support, while reserving their human energy for those uniquely human contributions that build trust and drive inspiration. This blend acknowledges both the power and the limits of AI, centering human agency at the core of true learning.