In Sarmistha Ghosh’s AP Biology classroom in Charlotte, North Carolina, protein folding is not just a diagram in a textbook. Students are hunched over pipe cleaners, beads, and Amino Acid Starter Kit© models, arguing about where a polar side chain should go—while an AI prediction of the same protein spins on the screen at the front of the room.
For these students, artificial intelligence is not a magic answer engine. It is one more modeling partner alongside physical kits, simulations, and data sources—something to question, compare, and use to refine their own ideas about how protein structure connects to function.
"One of the most exciting insights has been observing how pairing AI with hands-on molecular modeling, like the 3D Molecular Designs Amino Acid Starter Kit©, transforms abstract concepts into meaningful learning experiences," said Ghosh. "The physical models help students visualize protein structure and molecular interactions, while AI encourages them to ask deeper questions, analyze structure-function relationships, and think like scientists. Together, they create a classroom environment where hands-on modeling and AI complement each other to foster authentic scientific inquiry rather than replace it."
Protein folding is notoriously abstract for high school students. They must hold in their heads that amino acids differ in polarity and charge, that chains coil and fold in three dimensions, and that tiny changes can ripple up to whole‑organism effects.
students work with realistic structures that used to be locked behind expensive lab techniques. Instead of only hearing that “structure determines function,” students can see how a particular deletion or substitution reshapes a protein and then connect that change to disease symptoms or drug design.
At the same time, she is careful not to let AI eclipse the conceptual work. In her classroom, AI sits alongside hands‑on models, not above them. Students still build, manipulate, and argue about physical structures before they ever ask what AlphaFold “thinks.”
Ghosh introduces protein folding with a deliberately low-tech start. Students first explore how disulfide bonds, hydrogen bonds, ionic interactions, and polar versus nonpolar side chains shape a protein’s three‑dimensional form. They use pipe cleaners, beads, and the Amino Acid Starter Kit©, with color and shape cues to represent different amino acid properties.
That concrete work surfaces common misconceptions: students may oversimplify “hydrophobic” as just “doesn’t like water” or struggle to see how multiple weak interactions collectively stabilize a fold. Ghosh provides scaffolds that prompt them to identify and justify interactions, and she reports that by the end of this segment, most students can successfully identify hydrogen bonding sites and reason about why certain regions tuck inward or remain exposed.
Only after this hands-on modeling phase do students turn to AI. They access sequences from UniProt or the NCBI Protein Database, then generate or retrieve AlphaFold predictions for proteins such as human insulin or CFTR. Students compare their physical models and mental rules about folding with the AI‑generated three‑dimensional structures, checking where their reasoning aligns and where the AI output pushes them to revise their thinking.
This sequence—hands-on modeling, then AI prediction and comparison—keeps student cognition at the center. AI becomes a testable model, not a black box.
One of the anchor experiences in Ghosh’s unit is a case study on cystic fibrosis. Students retrieve the CFTR protein sequence (UniProt P13569) and examine the AlphaFold predicted structure alongside models they have built with beads, pipe cleaners, and the Amino Acid Starter Kit©.
Here, AI supports a richer narrative rather than replacing explanation. The predicted structure allows students to visualize what “misfolding” might look like, but they must still articulate how that structural change interferes with channel function and how potential drugs might stabilize or rescue the protein.
Ghosh reports that debating questions such as “How could AI help us design a drug for this misfolded protein?” or “What can we trust in this prediction and what still needs experimental confirmation?” push students into higher-order reasoning about both biology and technology.
To deepen students’ experience of protein structure, Ghosh layers multiple digital tools into the unit. Students move among:
Using these resources, students walk through a “lesson flow” that starts with raw amino acid sequences and ends with physical and digital models they can explain to peers. For example, they examine insulin sequences from humans, Western lowland gorillas, and Bornean orangutans, highlighting conserved cysteine residues that form disulfide bonds and discussing why these positions are so strongly conserved.
By comparing sequences and structures across species, students get repeated practice connecting amino acid properties to fold patterns and ultimately to function. Visual and tactile representations like the Amino Acid Starter Kit and 3D viewers help them bridge scales—from individual residues to whole‑protein shapes and organism‑level traits.
In the latter part of the unit, Ghosh introduces more explicitly AI‑driven tools, including image recognition and AI‑assisted sequence analysis. Students can, for instance, use tools like Google Lens or computer vision APIs to analyze photos of their models, label features such as disulfide bridges or hydrophobic pockets, and check whether their interpretations align with AI‑detected patterns.
They also experiment with AI‑powered sequence alignment and prediction tools, such as ColabFold, to align human insulin with homologs in other primates and generate structural models for each species. Students then evaluate differences in predicted folding, hypothesize about evolutionary or functional consequences, and test those ideas against what they know about protein stability and function.
For advanced or AP students, Ghosh outlines an optional extension into CRISPR and
Throughout, she explicitly surfaces limits and risks. Students discuss when AI tools might discourage critical thinking, how over‑reliance on automated answers could undermine conceptual learning, and why human oversight remains essential in both research and education. Ghosh emphasizes that “drawing the line” means using AI as a tool to enhance curiosity, reasoning, and creativity—not to short‑circuit them.
Ghosh’s work suggests several practical moves any high school or introductory college biology teacher can adapt, even with limited time or tech access:
Even small steps—such as adding an AI‑predicted structure comparison to an existing protein folding lab—can help students see AI as a tool they can interrogate and use, rather than a distant or opaque technology.
This post is adapted from Sarmistha Ghosh’s article, “Integrating AI in Research & Project-Based Learning in Biology Instruction,” published in The American Biology Teacher, 88(3), 159–165 (2026). You can read the full article in the journal: https://doi.org/10.1525/abt.2026.88.3.159.