Natural Learning in Digital Systems: Bringing the Developer Mindset to Education

Last week, I shared my experience with project-based learning and how it revealed the limitations of conventional classroom structures. But today, I want to take you somewhere deeper – into a thought that struck me while reading Reddit posts from a group of developers working on an AI system.

The posts revealed an incredible variety of practical AI applications: from automating medical research reviews and job description analysis to creating personalised learning tools and content summarisers. What caught my attention wasn't just what they built, but how they approached their work. As one developer mentioned, they "automated 5% of my work with AI, my goal is to get to 100%. Slow and steady. Doable in about 2 years." This iterative, systematic approach to improvement resonated deeply with my thoughts about education.

What if we saw ourselves as developers instead of teachers, developing a new artificial intelligence and project-based approach to education?

This question emerged during a particularly intense period of collaboration with both educators and AI developers. I watched as developers iteratively refined their systems, constantly testing, adjusting, and improving. Take the researcher who built a "virtual Publication Review Committee" - they designed a system that could evolve and adapt, incorporating multiple AI models to achieve better results. Another developer created an automated job board that learns and improves its filtering mechanisms over time.

Their approach was systematic yet creative, technical yet deeply human-centred. One developer shared how they built an AI tool to help D&D players, not just for rules lookup but to enhance the human experience of gameplay. Another created a bias analysis tool for media articles, emphasizing the importance of maintaining human oversight and diverse perspectives in AI systems.

It made me wonder: What if we brought this same mindset to education? What if, like these developers, we approached teaching not as a fixed process but as an evolving system that we could continuously refine and improve?

The Developer's Lens

When a developer approaches a problem, they don't just write code – they architect systems. They create environments where desired outcomes can emerge naturally. They don't directly tell the computer what to do at every step; instead, they create conditions where the right behaviours can evolve.

Sound familiar?

Isn't this exactly what we're trying to do in education?

The parallels became increasingly clear as I dove deeper into both worlds:

System Architecture vs. Curriculum Design

Just as developers create systems that can handle various inputs and produce desired outputs, educators design learning environments that can accommodate different types of learners and guide them toward specific outcomes. But there's a crucial difference in approach.

Traditional teaching often follows a linear path:

• Present information
• Demonstrate application
• Practice
• Assess
• Move on

A developer's mindset would approach it differently:

• Map the entire learning ecosystem
• Identify key interaction points
• Design feedback loops
• Create adaptive pathways
• Build in self-correction mechanisms
• Continuously optimize based on data

Iterative Development vs. Educational Evolution

In software development, the concept of "shipping early and often" allows for rapid feedback and improvement. Each iteration brings new insights and opportunities for enhancement. Compare this to traditional education, where curriculum changes often take years to implement and evaluate.

What if we approached lesson planning like sprint planning?

What if each class was seen as a new release, with retrospectives and iterative improvements built into the process?

The AI Connection

But let's push this metaphor further. Modern developers aren't just creating static systems – they're building incredibly complex learning machines. AI systems improve through experience, adapt to new situations, and optimise for better outcomes. The development is so incredibly new.

This is where the parallel becomes particularly interesting.

When developers train AI systems, they:

• Provide quality training data
• Create clear feedback mechanisms
• Design reward structures
• Monitor for biases and errors
• Adjust parameters based on performance
• Scale successful approaches

Now, imagine applying these principles to education:

• Curating rich, relevant learning experiences
• Establishing clear feedback loops
• Creating meaningful reward structures
• Monitoring for learning biases
• Adjusting teaching parameters based on outcomes
• Scaling successful teaching approaches

The Project-Based Learning Integration

This developer mindset naturally aligns with project-based learning (PBL). In PBL, like in system development, we're creating environments where learning emerges through exploration and problem-solving.

Consider how developers approach a new project:

1. Understand the problem space
2. Break down complex requirements
3. Plan iterative development cycles
4. Build minimal viable solutions
5. Test and gather feedback
6. Refine based on real-world usage
7. Scale successful components

Now, imagine applying this to education:

1. Understand learning objectives
2. Break down complex concepts
3. Plan learning cycles
4. Create minimal viable learning experiences
5. Gather student feedback
6. Refine based on actual learning outcomes
7. Scale successful teaching methods

The Human Element

But here's where we need to be careful. We're not trying to turn education into a purely technical process. Rather, we're adopting the mindset of modern developers who understand that the best systems are those that enhance human capabilities rather than replace them.

Think about how the best AI systems are designed:

• They augment human intelligence rather than replace it
• They provide tools for better decision-making
• They adapt to individual user needs
• They learn from interaction
• They maintain human agency

These same principles should guide our educational approach:

• Augment teacher capabilities
• Provide tools for better learning decisions
• Adapt to individual student needs
• Learn from student-teacher interactions
• Maintain student agency

Practical Implementation

So how do we begin this transformation? Here are some concrete steps:

Adopt System Thinking

• Map your classroom's learning ecosystem
• Identify key interaction points
• Design feedback loops
• Create adaptive pathways
• Build in self-correction mechanisms

Implement Iterative Development

• Plan in shorter learning cycles
• Gather continuous feedback
• Make rapid adjustments
• Document what works
• Scale successful approaches

Create Learning Environments

• Design for emergence rather than control
• Build in exploration opportunities
• Create safe spaces for failure
• Encourage systematic thinking
• Foster collaborative problem-solving

Leverage Technology Thoughtfully

• Use AI tools to augment teaching
• Implement adaptive learning systems
• Create digital feedback mechanisms
• Track learning analytics
• Enable personalized learning paths through localised Project-Based Learning

The Challenges

This transformation isn't without its challenges.

Just as developers face technical debt, security concerns, and scaling issues, educators adopting this mindset will encounter:

• Institutional resistance
• Resource constraints
• Technical limitations
• Assessment challenges
• Time management issues
• Professional development needs

But these challenges aren't insurmountable.

They're design constraints that can inform our approach and lead to more innovative solutions.

The Future Classroom

Imagine a classroom where:

• Learning objectives are like system requirements
• Lessons are like software releases
• Student feedback is like user testing
• Grades are like performance metrics
• Teaching methods are like algorithms that can be optimised
• Learning environments are like development environments
• Projects are like real-world applications

Personal Growth

This transformation requires personal growth from educators.

We need to:

• Learn new technical skills
• Develop systems thinking
• Embrace iteration and failure
• Become comfortable with uncertainty
• Build data and AI literacies and proficiencies
• Cultivate design thinking
• Practice agile methodologies

But the rewards are worth it. Just as developers find satisfaction in creating systems that empower users, educators can find new fulfilment in designing learning environments that enable student growth.

The Bigger Picture

This shift isn't just about improving education – it's about preparing students for a world where AI and human intelligence are increasingly intertwined. By adopting a developer's mindset, we're not just teaching better; we're modelling the kind of thinking our students will need in their futures.

When we see ourselves as developers of learning systems rather than just transmitters of knowledge, we:

• Empower students to become active learners
• Create more adaptive educational experiences
• Build scalable solutions to learning challenges
• Foster innovation in education
• Prepare students for an AI-enabled future

The Way Forward

This transformation won't happen overnight. Like any good development project, it requires:

• Clear vision
• Incremental progress
• Continuous learning
• Community support
• Resource investment
• Patient iteration

But the potential rewards are immense. By bringing together the best of educational practice with the systematic thinking of software development, we can create learning environments that are:

• More engaging
• More effective
• More equitable
• More scalable
• More future-ready

Conclusion

As I reflect on the transformation we're proposing – from teachers to educational system developers – I'm reminded of Fogg Dam in Australia's Northern Territory. This remarkable wetland system, initially created as a failed agricultural project, has evolved into one of the most biodiverse ecosystems in the region. Nature, in its wisdom, took a human-engineered structure and transformed it into something far more powerful and adaptive than its original design.

This natural evolution mirrors what we've seen in our exploration of the developer mindset in education. Just as Fogg Dam found its true purpose by allowing natural systems to flourish within engineered constraints, our role as educational developers is not to force rigid structures but to create environments where learning can emerge organically.

The developers we've examined in this chapter demonstrate this principle in action. From the researcher who built an AI-powered publication review system that learns from multiple models, to the developer who created a learning-enhanced D&D assistant, to those working on bias detection in media – each shows how synthetic systems can enhance rather than replace human capabilities.

This brings us to Brandom's concept of inferentialism – the idea that understanding comes not from isolated facts but from grasping the connections between concepts and their practical implications. The developer mindset naturally aligns with this philosophical framework. Just as inferentialism suggests that meaning emerges from a web of relationships and practical consequences, developers create systems that learn and adapt through networks of interactions and real-world feedback.

Consider how the developers we studied approached their projects:

• The job board creator didn't just build a search tool; they created a system that understands the relationships between skills, requirements, and human needs
• The medical publication reviewer didn't just summarise papers; they built a system that understands the connections between different expert perspectives
• The bias detection tool developers didn't just analyse text; they created a system that understands the complex web of relationships between language, perspective, and meaning

As educational developers, we can learn from these approaches. Instead of seeing ourselves as information transmitters, we become architects of learning ecosystems. Like Fogg Dam's transformation from a failed rice paddy to a thriving wetland, we must be willing to let our carefully engineered systems evolve in response to the natural learning patterns of our students.

This means:

• Creating environments where understanding can emerge through practice and interaction
• Building feedback loops that allow continuous adaptation and improvement
• Designing systems that recognise and support the interconnected nature of knowledge
• Allowing for organic growth and unexpected developments
• Maintaining the human element while leveraging technological capabilities

As we close this article, I invite you to consider: How can you begin to think like a developer in your educational practice? What systems can you create that will allow learning to emerge naturally? How can you design environments that support the interconnected, inferential nature of understanding?

Remember, like the developers we've studied who approached problems with patience and systematic thinking, this transformation doesn't happen overnight. As one developer noted, "I have automated 5% of my work with AI, my goal is to get to 100%. Slow and steady." This is our path forward in education – thoughtful, iterative, and always centred on enhancing human capabilities rather than replacing them.

The wetlands of Fogg Dam remind us that sometimes our greatest successes come not from rigidly controlling a system, but from creating the conditions where natural processes can flourish. As educational developers, our role is to build these conditions – to create spaces where learning can emerge, adapt, and thrive in ways we might never have imagined.

The future is not about teaching versus technology. It's about understanding how to create systems where both human wisdom and artificial intelligence can work together to support the natural process of learning. Just as Fogg Dam has become a testament to the power of engineered systems working in harmony with natural processes, our educational future lies in creating frameworks where human understanding can flourish in the digital age.

Phil