Traditional science courses often focus on memorizing facts, formulas, and classifications. While foundational knowledge is important, this approach doesn’t always build the reasoning and problem-solving muscles students need for real scientific work. That’s why educator-scientist Giorgio Lagna — who teaches Genetics, Human Physiology, and Anatomy & Physiology — has redesigned his classes to harness AI tools in a way that builds deeper understanding.
The result isn’t flashy tech for its own sake: it’s a teaching approach that helps students learn how to think like scientists.
Turning AI Into a “How-to-Think” Tutor
Rather than letting students rely on AI to shortcut homework, Lagna designs AI interactions that push them to reason. The AI acts as a Socratic tutor — asking guiding questions, probing assumptions, and encouraging students to explain why they think something is true.
Example:
A student learning about genetic inheritance might be asked directly:
| “What is the genotype of the parent?”
But instead of giving an answer, the AI responds:
| “Before we assign a genotype, what does the phenotype suggest? And which inheritance patterns could explain it?”
Students don’t just memorize rules — they practice scientific thinking. They learn to justify their logic, evaluate alternatives, and interpret information the way researchers do.
Branching Case Studies: Learning by Doing
In our summary video, we highlight Lagna’s approach to using AI to create branching case studies — scenarios that evolve based on student decisions. These aren’t static worksheets; they mirror the real complexity of scientific and clinical work.
Example:
A physiology student might be presented with a patient who reports dizziness. The AI-generated scenario then asks:
- Which symptom should you investigate next?
- What diagnostic test would you order?
- What mechanism could explain the findings?
If the student chooses an unnecessary test, the scenario shifts and illustrates the consequences. If they make a correct decision, the case advances with new data.
This approach lets students see cause-and-effect relationships — much like troubleshooting an instrument, adjusting protocol parameters, or interpreting unexpected lab results.
Why It Works
1. Active learning replaces passive memorization
Students engage with problems that require analysis, not recall. They learn by making choices and reflecting on outcomes.
2. It mirrors actual lab and research environments
Real science is full of branching decisions:
| “Do I rerun the sample? Adjust the temperature? Change the buffer?”
This method prepares students for exactly that kind of thinking.
3. It scales easily for instructors
Lagna highlights that these tools aren’t just for tech specialists. Any instructor can adopt AI-supported questioning or branching scenarios with accessible platforms — no advanced programming required.
4. Students learn how to think, not just what to think
This is especially critical in the era of generative AI, where simple memorization has less value than the reasoning behind it.
A Better Path for the Future of Science Education
As AI becomes more integrated into both classrooms and labs, education must move beyond rote learning. Lagna’s approach shows one of the most promising directions: using AI not to replace thinking, but to develop it.
Students trained in this way enter labs and research settings with stronger critical-thinking muscles, better decision-making habits, and more confidence navigating complex scientific problems.
It’s an approach that aligns with what today’s lab professionals need most — and with the real scientific work that companies like SeqGen support every day.
Welcome to the SeqGen Lab AI Guide. This series shares practical insights, tutorials, and real examples of how AI tools can enhance lab workflows and scientific research. We hope you find these posts helpful in applying AI to your work. Stay tuned — more content, case studies, and actionable tips are on the way.
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