For decades, Kolb’s Experiential Learning Theory (KELT) has shaped education, emphasizing a four-stage learning cycle (experience, reflection, conceptualization, application). However, research shows that learning is far more dynamic, non-linear, and self-organizing than Kolb’s rigid model suggests. CDTT, developed by Schenck and Cruickshank (2015), builds on neuroscience, constructivism, and dynamic skill theory to propose a fractal-like, recursive model of learning. Rather than moving through a fixed sequence, CDTT emphasizes:
- Iterative learning cycles: Revisiting topics at higher levels of complexity over time.
- Personalized scaffolding: Adjusting learning support dynamically based on a learner’s developmental stage.
- Cognitive energy efficiency: Prioritizing deep processing while managing cognitive load.
- Self-adjusting feedback loops: Constant refinement through interactive feedback.
- CDTT aligns with how we naturally develop cognitive and social and emotional skills, where early concepts form the foundation for more abstract reasoning, much like a spiral that expands upward rather than a ladder with rigid steps.
How Marzy AI Aligns with the Fractal Learning Model
1. Marzy Adapts Learning in Recursive, Self-Adjusting Loops
CDTT argues that learning is not a single-pass event, but a recursive process where students revisit concepts in increasingly complex ways. Marzy embodies this principle by:
- Tracking what a child already knows and reintroducing topics at deeper levels.
- Providing iterative reinforcement, ensuring knowledge isn’t just memorized but integrated and applied.
- Adapting responses dynamically, fine-tuning content based on real-time interactions with the learner.
For example, in social and emotional learning (SEL), Marzy might:
- Introduce basic emotional vocabulary, such as recognizing “happy,” “sad,” or “angry” in everyday situations.
- Encourage deeper self-awareness, helping the child articulate more complex emotions like frustration or disappointment and connect with the feelings associated with the emotions.
- Guide the child through social problem-solving scenarios, such as how to handle a disagreement with a friend.
- Introduce perspective-taking, helping them consider how others feel in different situations, fostering empathy.
- Explore emotion regulation strategies, such as deep breathing, self-talk, or seeking support from a trusted adult.
Rather than treating SEL as a linear learning process, Marzy revisits and deepens these interrelated concepts over time, just as a fractal spiral loops back while expanding outward. This approach mimics a fractal spiral, where learning expands outward rather than moving in a straight line.
2. Marzy Supports Executive Function Development—A Core Pillar of CDTT
One of CDTT’s key contributions is highlighting how cognitive energy is managed during learning. Traditional models assume students can effortlessly absorb and apply knowledge, but neuroscience shows that executive function skills—focus, working memory, and self-regulation—are essential for meaningful learning.
Marzy supports executive function by:
- Breaking tasks into smaller, manageable steps to prevent cognitive overload.
- Helping children pause and reflect, allowing knowledge to consolidate.
- Encouraging self-directed goal setting, fostering metacognition and motivation.
- This aligns directly with CDTT’s emphasis on cognitive load management—reducing unnecessary distractions and ensuring that learning flows naturally.
3. Marzy Engages in Meaningful Conversations That Build on Prior Knowledge
A major flaw in traditional education is its focus on one-size-fits-all instruction—a stark contrast to CDTT, which emphasizes individualized dialogue, scaffolding, and relational learning. Marzy transforms learning into an ongoing conversation, where:
- Past experiences shape future learning. If a child discusses space, Marzy may later bring up gravity, physics, or black holes—gradually expanding conceptual depth.
- Questions are not just answered, but extended. Instead of just defining a word, Marzy might ask, “What do you think this means?” leading the child to construct their own understanding.
- Learning is interactive and student-led. The AI can follow curiosity rather than sticking to a rigid curriculum.
This aligns with CDTT’s emphasis on co-construction—where learners are not passive recipients but active participants in their own education.
4. Marzy Supports Self-Determination and Intrinsic Motivation
CDTT incorporates Self-Determination Theory (SDT), emphasizing autonomy, competence, and relatedness as key to motivation. Marzy nurtures this by:
- Giving children choices in their learning path, increasing engagement.
- Offering encouragement and real-time feedback, reinforcing a sense of competence.
- Promoting social interactions with family and friends that reinforce learning, making learning feel more like an interactive discussion than a scripted lesson.
- Rather than forcing learning in a rigid way, Marzy sparks curiosity, ensuring that children feel a sense of ownership over their knowledge.
Beyond Traditional Learning: A Future of Fractal, Adaptive Education
The traditional education system struggles with rigid grading, testing, and linear progression through siloed content—the system was built before we understood how learning truly happens. CDTT offers a compelling alternative: a flexible, neuroscience-informed model that mirrors real cognitive development.
Marzy AI brings this vision to life by:
- Personalizing learning in iterative loops, much like the CDTT fractal spiral.
- Fostering executive function skills, critical for self-regulated learning.
- Encouraging deep thinking through dialogue, instead of rote memorization.
- Empowering students to take ownership of their learning journey.
By combining AI with research-backed educational science, Marzy isn’t just another ed-tech tool—it’s a shift toward a more natural, adaptive, and effective way to learn. A way that respects the complexity of human cognition and helps children build knowledge like a fractal spiral—layer by layer, conversation by conversation.
Schenck, J., & Cruickshank, J. (2015). Evolving Kolb: Experiential education in the age of neuroscience. Journal of Experiential Education, 38(1), 73-95. https://doi.org/10.1177/10538259145471