Thoughtful AI Decisions Begin With Learning
Helping educators design meaningful learning experiences and make informed decisions about whether, when, and how AI supports student learning.
AI is changing how students access information, complete tasks, and communicate ideas. STAC works with educators, schools, districts, and educational organizations to consider what students need to understand, what thinking they need to perform, and what evidence will demonstrate their learning. Those goals guide decisions about the role of AI and other technologies.
Design for the Thinking Students Need to Develop
Generative AI can produce explanations, models, graphs, and written products that look like evidence of learning. Educators need to know whether a student understands an idea, can reason with evidence, can evaluate a claim, and can exercise judgment with appropriate independence.
STAC helps educators examine learning experiences and assessment conditions so that students have meaningful opportunities to perform the disciplinary thinking they are expected to develop. AI may support parts of that work, but it should never be introduced simply because it is available.
Start With Learning Goals
Clarify the knowledge, reasoning, and judgment learners need to develop before selecting tools or designing activities.
Make Reasoning Visible
Design investigations, tasks, and classroom interactions that provide meaningful evidence of students’ thinking.
Use AI With Purpose
Determine when AI can improve access or extend learning, and when working without it better serves the learning goal.
From AI Awareness to Educational Practice
Our work focuses on practical questions educators and education leaders face as AI becomes more widely available. We connect research, disciplinary learning, accessibility, and assessment to decisions made in classrooms and educational systems.
AI-Resilient Learning Design
Redesign learning experiences to preserve consequential intellectual work and provide meaningful evidence of student capability.
AI and Assessment
Examine what student products reveal when AI assistance is possible, and design approaches that surface understanding, reasoning, and independence.
Classroom AI Agreements
Develop shared expectations for purposeful use, transparency, evidence evaluation, and student responsibility.
Science and Engineering Practices
Use scientific questioning, modeling, investigation, analysis, explanation, and argumentation to guide intentional learning design.
Accessible Learning Experiences
Consider how tools and supports can remove barriers while preserving learners’ opportunities to reason, judge, and make decisions.
Educator and Leader Learning
Support thoughtful decision-making through professional learning, collaborative task analysis, and practical planning.
Evaluate the Role of AI Before Using It
Educators can make stronger technology decisions when they begin with the learning they intend to cultivate and consider how different supports influence students’ intellectual responsibilities.
Specify the thinking
What should students understand, investigate, explain, evaluate, or decide?
Determine the tool’s role
Will AI support access and learning, extend inquiry, or perform thinking students need to do themselves?
Recognize evidence of learning
What can students demonstrate, with and without support, that provides credible evidence of growing capability?
What Happens When AI Can Generate the Explanation?
Consider a science investigation in which students collect firsthand data and construct an explanation for an observed pattern. AI may be able to generate a polished account of the phenomenon, but that product alone cannot show how students interpreted their own results.
An intentionally designed experience asks learners to identify anomalies, assess data quality, weigh alternative explanations, and defend or revise a conclusion using evidence. Educators can then examine reasoning demonstrated through the investigation, discussion, and reflection.
Whether AI is used to compare interpretations or excluded while students develop an initial explanation should follow from the purpose of the learning experience.
What evidence would show that the student can reason scientifically?
Look beyond the finished graph or explanation. Consider how students interpret evidence, handle uncertainty, evaluate alternatives, and justify their conclusions.
Support for Educators and Educational Leaders
STAC offers research-informed professional learning and project collaboration to help organizations translate emerging questions about AI into coherent educational decisions. We work across curriculum, instruction, assessment, and inclusive learning design, with particular expertise in K–12 science education.
Professional Learning
Workshops and collaborative learning experiences connect AI-resilient task design, scientific sensemaking, assessment, and thoughtful AI decisions to classroom practice.
Thinking Literacy
Thinking Literacy is a research-informed framework developed by Velma Itamura for intentionally cultivating understanding, reasoning, judgment, and independence over time. It informs STAC’s approach to learning design and evidence of developing capability.
Let’s Design Learning Experiences With Purpose
Are you reviewing AI guidance, redesigning curriculum or assessment, or planning professional learning? STAC can help you connect those decisions to the human learning and thinking your organization wants to develop.
Email: info@stac-vernier.org