Better yet, what if parents and teachers had full authority and control of their child's individual AI, so they could see how and why their students learn and achieve?
Too often we lean into inquiry-based learning and Socratic methods without knowing if that matches our student's learning style. So we guess, the way teachers have always guessed, and the kids who get the most out of us are the ones lucky enough to be sitting near an educator who happens to guess right about them. Our standard approach is a bit antiquated, but we don't have to continue operating this way.
With the advancement in AI, each child can gets a small model of their own, that holds what it has actually observed and learned about them. These models wouldn't average them into a cohort and then compare them to another. The models could measures your child's progress against yesterday's learning gains, never comparing them to another child, because every child learns differently. That would make the models fair. You could also open, read, argue with, and overrule the personal models, which is the part I care about most: parental control of the AI model. The models would exists to accelerate learning for one specific child. Not engagement, not time in the app, not completion percentage, but the thing education was created to provide: learning.
We tested the idea by simulating three students on the na8ve.ai platform so that we could watch how the models behaved. Before every one of these test runs, we wrote down what we expect to happen, then track the model's behavior during testing, along with the student's learning outcomes.
We discovered some interesting findings about how these personal models handle fairness: Two children, same skill, same week, both correct, but sent in opposite directions. Nobody programmed that outcome. It came from the model reading two different children's actual memory states and reaching two different conclusions, which is exactly the judgment a good teacher would make. The more the model understands the individual child, the less biased it is in serving that child's learning needs.
A single model answering every child the same way is a recipe for scaling biases onto a child it doesn't understand.
Alternatively, a model that belongs to one student and holds only what has actually been observed about them, one that shows an adult exactly how the student learns and how it reached its conclusion, one that hands over full authority and control to the adult, is a completely different kind of model. We're excited to share that model with those who need it most, and to share the journey of na8ve, what we believe, and the evidence we discover while keeping parents and teachers in the loop of their child's learning journey.
Read the research at: https://na8ve.ai/research/personal-learning-models
hashtag#AITutor hashtag#PersonalLearningModel hashtag#STEM hashtag#Education
Top comments (0)