AI literacy night. The core message translates easily for a home audience: before typing anything about a child into a public AI tool, leave out the name, the school, and the specific diagnosis, and describe the situation in general terms instead. Families do not need to become privacy experts to protect their own chil- dren. They need the same billboard test teachers are learning, phrased for a kitchen table instead of a classroom. WHERE BIAS HIDES IN THE MACHINE A second pressure point in AI ethics training surprises more teachers: bias. Artificial intelligence systems learn patterns from historical data, and the historical record in American education includes decades of documented inequity, including the dispro- portionate placement of Black and Latino students in restrictive settings. When an AI tool recommends a placement, drafts a goal, or interprets an assessment, it can inherit and repeat those patterns instead of correcting them. Three concrete ways this shows up in practice include placement recommendations that skew more restrictive for student profiles mentioning behavioral challenges, IEP goals that reflect lower expectations for students with certain disability labels, and screening algorithms that gen- erate more false positives for particular demographic groups. Representation gaps compound the problem. AI models tend to know less about low-incidence disabilities, culturally di- verse learners, and multilingual students with disabilities, simply because there is less training data describing their experiences well. And left unchecked, AI-generated descriptions of disabil- ity tend to default to deficit-based language rather than the strengths-based, person-first framing the field has worked for decades to normalize. The response to that risk is not to abandon AI over bias con- cerns but to interrogate any new tool before it reaches a class- room. Five questions are worth asking before adopting any AI tool: what data was it trained on, who was involved in building it, does it perform equally well across different student profiles, does its output use strengths-based language, and can a teach- er override or edit its recommendations? That last question car- ries particular weight. If a teacher cannot modify or correct an AI tool's output, the tool is effectively making decisions for the teacher rather than with the teacher, which is a very different and much riskier relationship. THE PAUSE FRAMEWORK Every thread from the ethics discussion- privacy, accuracy, bias, independence, and equity- converges into a single tool teachers can carry with them: PAUSE. The framework is designed to be printed, kept next to a computer, and run through in full before using any AI tool with or for a student.
Letter
The Question to Ask Before Clicking Generate
I s any student identifiable in this prompt? Has all data been de-identified? Can the output be verified? Is professional judgment still driving the decision? Could this output reflect or amplify bias against any group of students?
P - Privacy
A - Accuracy
U - Underlying Bias
S - Independence Does the tool build a skill, or replace it? Is it a scaffold or a crutch?
Does every student benefit equally? Who might be left out or harmed?
E - Equity
Table 1: PAUSE Framework
The Independence letter draws a distinction central to the framework: the difference between a scaffold and a crutch. An AI tool that reads text aloud for a student with dyslexia is a scaffold, temporary support that keeps a skill accessible while it devel- ops. An AI tool that generates all of a student's written responses when that student is capable of typing is a crutch, something that replaces a skill rather than building toward it. The same dis- tinction applies to organizing a graphic organizer for a student to fill in, which supports independence, versus letting AI write IEP goals without a teacher's clinical judgment behind them, which erodes it. For each AI use with a given student, the practi- cal question is whether the tool builds a bridge toward indepen- dence or a wall around it. AI INSIDE THE UDL FRAMEWORK Alongside the ethical framework, special education increas- ingly discusses AI use through the lens of Universal Design for Learning. UDL's most recent version, UDL 3.0, organizes instruc- tion around three principles: engagement, or the why of learn- ing; representation, or the what of learning; and action and expression, or the how of learning. Specific AI uses map onto each principle. On the engagement side, AI can generate inter- est-based word problems, culturally responsive scenarios, and student choice menus. On the representation side, it can pro- duce text-to-speech output, image descriptions, reading-level adjustments, and language translations. On the action and ex- pression side, it can support speech-to-text, writing scaffolds, multimodal output options, and organizational supports such as visual schedules. A related question carries legal weight: can generative AI be written into a student's Individualized Education Program as as- sistive technology? The answer is yes, when the tool functions as AT for that particular student. The federal definition of assistive technology under the Individuals with Disabilities Education Act is broad enough to encompass many AI tools, provided the tool addresses a documented need, the student uses it consistently, it is named in the IEP's assistive technology section, and staff is
(See Table 1: PAUSE Framework)
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