A practical guide from AI for Education that gives educators and institutions a structured decision-making tool for evaluating generative AI use. The SEE Framework — Safe, Ethical, Effective — replaces gut-feel AI adoption with principled questions that surface what actually matters.
As Reach University builds its AI capacity, the hardest work isn't choosing platforms — it's building the habit of asking the right questions before deploying AI in any context. The SEE Framework provides a repeatable, teachable structure for doing exactly that.
Whether a faculty member is deciding whether to use AI to assist with grading, or a staff member is evaluating a new candidate-facing tool, the SEE Framework gives them three lenses: Is it Safe? Is it Ethical? Is it Effective? Together, these replace the anxiety of "is this OK?" with a productive inquiry that surfaces risk, responsibility, and real impact.
"There may not be one correct answer, but there should be thoughtful questions."
AI for Education — SEE Framework in ActionThree lenses applied together — not separately — to any decision about using generative AI. The power is in the combination: something can be technically safe but ethically questionable, or ethically sound but ineffective for the stated goal.
Examines the risks, protections, permissions, and safeguards involved in the AI use case.
Explores responsibility, fairness, transparency, and the appropriate role of human judgment.
Evaluates whether GenAI meaningfully supports the intended learning or operational outcome.
AI for Education provides five pre-built scenarios — each with a downloadable PDF that walks through the Safe, Ethical, and Effective lenses in full. These serve as both worked examples and discussion starters.
A student uses an AI math tutor outside class hours. Explores data collection by the chatbot, accuracy of AI explanations, and whether self-directed AI tutoring supports or replaces the development of problem-solving skills.
A student uses GenAI to draft or polish their personal essay. Raises questions about authenticity, institutional transparency requirements, whether AI assistance disadvantages students without access, and what the essay is actually meant to assess.
A student uses a GenAI tool to research the health impacts of ultra-processed foods. Examines whether AI-generated research summaries are accurate, appropriately cited, and whether using them builds or bypasses information literacy skills.
A teacher uses AI to provide feedback or assign scores on student work. Explores who is accountable for grades, whether students have been informed, how bias in AI systems may affect outcomes, and whether AI feedback serves students' growth.
A school considers adopting an AI writing detector to flag student submissions. Raises questions about false-positive rates and their impact on students of color or English language learners, institutional trust, and whether detection is the right response to the underlying concern.
Each scenario includes a downloadable PDF with the full SEE analysis and discussion prompts. Visit aiforeducation.io to download them.
↑ Back to topThe SEE Framework maps directly onto the kinds of AI decisions Reach University is navigating now. The table below connects the framework to specific Reach contexts.
| Reach Context | Lens(es) | Key Questions for Reach |
|---|---|---|
| Faculty using Gemini to draft candidate feedback | Ethical · Effective | Is the candidate aware AI-assisted feedback is being used? Does AI feedback support or replace the faculty relationship that defines the Reach Method? |
| Staff using AI to draft outreach emails | Safe · Ethical | Is any FERPA-protected candidate information being entered into a non-approved tool? If Gemini is approved, is this the tool being used? |
| IT deploying a candidate-facing AI assistant (Domain 3) | Safe · Ethical · Effective | What data does the tool collect? Is it disclosed to candidates? Does it actually help them — or does it create a barrier to reaching a human who could help more? |
| Evaluating whether to adopt a new AI platform | Safe · Effective | Does it meet FERPA compliance requirements? Is it superior to our approved tools, or is it novelty? What does the contract say about data use? |
| Candidates using AI for course assignments | Ethical · Effective | Has the faculty member set clear expectations? Does AI use support or undermine the competency the assignment is designed to develop? |
| Building AI literacy across the task force | Ethical · Effective | Are we building genuine understanding, or just compliance? Does the professional development we design actually change how people make AI decisions? |
The SEE Framework is a practical tool the task force can use immediately — as a shared vocabulary for evaluating AI decisions, as a structure for professional development workshops, or as a template for Reach's own AI literacy curriculum. Download the five scenario PDFs from AI for Education and try running one as a team exercise. The goal is not to arrive at a policy, but to build the habit of asking better questions.