Benjamin L. Stewart

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tagsframework, teacher-education
notes

22 min read

NDAA — Notice → Decide → Assess → Adapt

Core idea

NDAA is a framework for developing professional judgment in teachers.

Rather than defining good teaching as the ability to follow a method, use a particular technology, or reproduce a set of techniques, NDAA treats teaching as a continuous process of:

noticing meaningful evidence → making a principled decision → assessing its effects → adapting future action.

The cycle then begins again.

Why NDAA Matters

Language teaching is becoming increasingly difficult to define in terms of particular techniques or technologies.

Teaching methods change.

Curricula change.

Learners change.

Generative AI changes what students can produce independently, what teachers can delegate to technology, and what counts as convincing evidence of learning.

For this reason, an increasingly important goal of teacher education is to develop professional judgment.

A teacher with professional judgment can:

  • recognize what is happening in a learning situation;
  • distinguish important evidence from irrelevant information;
  • interpret learner behavior cautiously rather than jumping to conclusions;
  • connect classroom evidence with pedagogical knowledge;
  • choose among several possible courses of action;
  • explain why a particular choice is appropriate;
  • collect evidence about whether the choice worked;
  • reconsider assumptions when the evidence contradicts expectations;
  • use technology without surrendering professional responsibility;
  • preserve learner agency and intellectual ownership;
  • and continually improve future decisions.

NDAA organizes these capabilities into four interconnected capacities:

NOTICE → DECIDE → ASSESS → ADAPT → NOTICE again

1. 👁️ NOTICE — Professional Vision

Learn to see what matters.

Teaching begins with attention.

Classrooms contain far more information than a teacher can process at once. Learners are speaking, writing, hesitating, collaborating, misunderstanding, solving problems, avoiding tasks, asking questions, using technology, and responding emotionally and socially to one another.

The professional task is therefore not simply to observe everything.

It is to learn what deserves attention.

Important

Noticing is the disciplined movement from “something happened” to “here is evidence worth examining.”

Professional vision develops as teachers become better able to recognize patterns that novice teachers may initially overlook.

Central question

What is happening here that matters for learning?

Questions for noticing learners

  • What are learners actually doing rather than what did I expect them to be doing?
  • Who is participating?
  • Who is not participating?
  • Who is doing most of the cognitive work?
  • Who appears dependent on the teacher?
  • Who appears dependent on classmates?
  • Who appears dependent on AI or another tool?
  • Where are learners hesitating?
  • What questions are learners asking?
  • What questions are they not asking?
  • What misconceptions are appearing repeatedly?
  • What errors seem systematic rather than accidental?
  • What can learners already do without assistance?
  • Where do learners still require scaffolding?
  • Where might a learner know more than the task allows them to demonstrate?
  • What appears easy for some learners but difficult for others?
  • Are learners transferring previous learning into the present task?
  • Are learners merely completing the activity, or are they demonstrating the intended learning?

Questions for noticing language

  • What linguistic patterns are learners using successfully?
  • What grammatical choices are creating difficulty?
  • What vocabulary limitations are affecting communication?
  • What discourse patterns are learners using?
  • Are learners producing isolated correct sentences but struggling to sustain discourse?
  • How are learners organizing information?
  • What cohesion problems are appearing?
  • What pragmatic choices are working or failing?
  • Does the learner’s language accomplish the intended communicative purpose?
  • What does the learner’s output reveal about their current interlanguage?

Questions for noticing classroom interaction

  • Who initiates interaction?
  • Who responds?
  • Who receives follow-up questions?
  • How long do learners have to think before answering?
  • Are the same students consistently controlling discussion?
  • Are quieter learners disengaged, processing internally, or being excluded?
  • Does teacher talk encourage further thinking or close down discussion?
  • Are students responding to one another or primarily to the teacher?
  • What patterns of collaboration are emerging?
  • Where does communication break down?
  • What happens immediately before a successful learning moment?
  • What happens immediately before confusion appears?

Questions for noticing emotion and engagement

  • When does learner energy increase?
  • When does it decrease?
  • What seems to generate curiosity?
  • What seems to produce frustration?
  • Is difficulty productive or overwhelming?
  • Are students reluctant because they do not understand, because they fear making mistakes, or for another reason?
  • Does the task feel meaningful to learners?
  • Are learners making meaningful choices?
  • Do students appear to understand why they are doing the activity?

Questions for noticing AI use

  • What part of the task is the learner completing?
  • What part is AI completing?
  • Is AI functioning as a scaffold or a substitute?
  • Can the learner explain the AI-generated output?
  • Can the learner modify it intelligently?
  • Can the learner identify weaknesses in the output?
  • Does AI use make learner thinking more visible or less visible?
  • Is the learner gaining independence through AI use or becoming more dependent?
  • What evidence remains that allows me to see the learner’s own thinking?
  • Would I interpret this work differently if I knew AI had produced most of it?

Evidence teachers might notice

Possible evidence includes:

  • learner questions;
  • drafts;
  • annotations;
  • oral explanations;
  • hesitation;
  • repair strategies;
  • peer interaction;
  • exit tickets;
  • changes between drafts;
  • errors and error patterns;
  • discourse choices;
  • student reflections;
  • think-alouds;
  • AI interaction logs;
  • prompt histories;
  • explanations of why an AI suggestion was accepted or rejected;
  • performance without technological assistance.

Example

During a writing activity, several students submit grammatically polished paragraphs.

Instead of immediately concluding that the learning objective has been achieved, the teacher notices that several students cannot explain why their topic sentence effectively controls the paragraph.

The important evidence is therefore not simply the quality of the paragraph.

The important evidence is the mismatch between product quality and demonstrated understanding.

From Notice to Decide

Noticing alone is insufficient.

The next question becomes:

Given what I am seeing, what should I do?

2. 🧭 DECIDE — Pedagogical Decision Making

Make principled choices.

Teachers make hundreds of decisions.

Some are planned:

  • learning objectives;
  • materials;
  • grouping;
  • assessment;
  • technology;
  • sequencing.

Others occur during instruction:

  • whether to explain again;
  • whether to ask another question;
  • whether to intervene;
  • whether to allow productive struggle;
  • whether to change groups;
  • whether to abandon an activity;
  • whether to use AI;
  • whether to continue with the lesson plan.

Professional judgment requires teachers to move beyond:

“What activity should I use?”

toward:

“Given these learners, this objective, this evidence, and this context, what response is most defensible?”

Central question

What should I do next, and why?

Questions about instructional purpose

  • What is the actual learning objective?
  • What should learners know, understand, or be able to do?
  • Which part of the objective is most important right now?
  • Does the activity directly serve that objective?
  • Am I teaching the objective or merely completing the planned activity?
  • What would successful learning look like?
  • What evidence should the activity produce?

Questions about intervention

  • Does this problem require teacher intervention?
  • Could learners resolve it themselves?
  • Would peer interaction be more useful than teacher explanation?
  • Should I provide a hint, model, question, explanation, or example?
  • How much support is necessary?
  • Am I removing productive struggle too quickly?
  • Is continued struggle likely to produce learning or merely frustration?
  • Should I slow down?
  • Should I move on?
  • Should I reteach?
  • Should I change representation or modality?

Questions about task design

  • Why am I choosing this task?
  • What cognitive work does it require?
  • Who is doing the thinking?
  • Does the task allow learners to make meaningful decisions?
  • Does it require recall, interpretation, application, analysis, evaluation, creation, or some combination?
  • Could students complete it successfully without understanding?
  • Could AI complete it successfully without the learner understanding?
  • How could the task make reasoning more visible?
  • Is there a more authentic way to achieve the same objective?

Questions about differentiation and UDL

  • What barriers are inherent in the learning objective?
  • What barriers are accidental products of the task design?
  • Can learners access the content in more than one way?
  • Can learners demonstrate equivalent learning through different forms of expression?
  • Am I changing the learning objective or simply removing unnecessary barriers?
  • Which learners need additional scaffolding?
  • Which learners need greater challenge?
  • Can learners make meaningful choices without undermining the objective?

Questions about AI delegation

  • What should the learner do?
  • What could AI appropriately support?
  • What could AI appropriately automate?
  • What must remain visible for me to evaluate learning?
  • Would delegation remove a useful cognitive process?
  • Would delegation remove only low-value administrative work?
  • Does AI free the learner to engage in more meaningful thinking?
  • Does AI eliminate the very thinking the assignment was designed to develop?
  • What must the learner be able to do without AI?
  • What might the learner reasonably do with AI in authentic professional practice?
  • Should AI use occur before, during, or after independent work?
  • Should the learner disclose how AI was used?
  • What verification should accompany AI-generated information?

Questions about human judgment

Some decisions deserve particular protection from indiscriminate automation.

Ask:

  • Does this decision require knowledge of this particular learner?

  • Does it involve ethical consequences?

  • Does it involve interpretation of ambiguous evidence?

  • Does it affect learner opportunity?

  • Does it require empathy?

  • Does it require understanding institutional or cultural context?

  • Does it require professional accountability?

  • Would I be willing to defend this decision publicly?

  • Am I using AI to inform my judgment or replace it?

Example

Students struggle to identify the controlling idea of an academic paragraph.

The teacher could:

  1. explain topic sentences again;

  2. provide another model;

  3. compare strong and weak examples;

  4. ask students to reconstruct a paragraph;

  5. let students use AI to generate examples;

  6. have students explain why one sentence works better than another.

NDAA does not identify one universally correct technique.

Instead, the teacher asks which response is most appropriate given the evidence, objective, learners, and context.

A simple decision rule

Before acting, ask:

Evidence → Objective → Options → Rationale

Evidence

What did I notice?

Objective

What learning am I trying to support?

Options

What could I reasonably do?

Rationale

Why is this option preferable here?

From Decide to Assess

A pedagogical decision is ultimately a hypothesis:

I believe this action will improve learning in this situation.

Assessment determines whether that hypothesis was supported.

3. 🔎 ASSESS — Assessment Literacy

Look for evidence that learning has actually occurred.

Assessment should not be reduced to grading.

Assessment is the systematic process of gathering and interpreting evidence in order to answer:

What does the learner currently understand and what can the learner currently do?

Generative AI makes this question even more important because a high-quality product no longer necessarily demonstrates high-quality learning.

A learner can submit work that is:

  • fluent;

  • grammatically accurate;

  • logically organized;

  • visually polished;

  • appropriately formatted;

without necessarily possessing the knowledge or skill apparently represented by the product.

For this reason, NDAA emphasizes evidence of learning rather than evidence of task completion.

Central question

What evidence supports the claim that learning occurred?

Questions about alignment

  • What learning outcome am I assessing?

  • What evidence would demonstrate that outcome?

  • Does my assessment actually elicit that evidence?

  • Am I evaluating what matters or what is easiest to score?

  • Does the rubric reflect the stated objective?

  • Are students aware of what counts as evidence?

  • Does the assessment require students to use the targeted knowledge or skill?

Questions about evidence

  • What can the learner now do?

  • What can the learner explain?

  • What can the learner recognize?

  • What can the learner produce?

  • What can the learner revise?

  • What can the learner justify?

  • What can the learner transfer to a new situation?

  • What can the learner do independently?

  • What can the learner do only with support?

  • What evidence is missing?

  • Is one piece of evidence sufficient?

  • Would another form of evidence lead me to the same conclusion?

Performance versus learning

Ask:

  • Did the learner produce a successful product?

  • Does the learner understand why it is successful?

  • Can the learner reproduce the underlying performance?

  • Can the learner perform in a slightly different context?

  • Can the learner explain the strategy?

  • Can the learner identify and correct an error?

  • Can the learner apply the learning without the original scaffold?

Important

Performance is something the learner did once.

Learning implies some degree of durable, transferable capability.

Questions for formative assessment

  • What do learners understand now?

  • What remains unclear?

  • Which misconception should be addressed first?

  • What feedback is likely to change the next attempt?

  • Does the learner know how to use the feedback?

  • What should happen immediately after this assessment?

  • Should I reteach, regroup, extend, scaffold, or move forward?

  • What information does this evidence give the learner?

  • What information does it give me?

Questions about AI and assessment

  • Could AI produce the artifact being assessed?

  • If so, what does the artifact actually prove?

  • What learner thinking remains visible?

  • Can learners explain their process?

  • Can learners justify choices made with AI?

  • Can learners identify inaccurate or inappropriate AI suggestions?

  • Can learners revise AI output using disciplinary knowledge?

  • Can learners reproduce key capabilities without AI?

  • Does the assessment distinguish AI-supported performance from independent capability?

  • Would a short oral explanation provide useful triangulation?

  • Would comparing an AI-assisted task with an independent baseline provide stronger evidence?

  • Should process evidence accompany the final product?

  • Should the learner submit drafts, annotations, prompts, decisions, or reflections?

  • Does the assessment reward effective tool use without confusing tool capability with learner capability?

Stronger evidence in an AI-rich environment

Evidence becomes stronger when it is triangulated.

For example:

Final artifact + process evidence + explanation + independent transfer

A writing assessment might therefore include:

  1. a polished AI-supported final text;

  2. selected drafts;

  3. a record of important revisions;

  4. explanation of major rhetorical choices;

  5. disclosure of AI assistance;

  6. a brief independent writing sample.

No single component tells the entire story.

Together they make learner capability more visible.

Example

A student uses AI to revise an argumentative paragraph.

The final paragraph is excellent.

Instead of asking only, “Is this paragraph good?”, the teacher asks the student to:

  • identify two AI suggestions they accepted;

  • identify one suggestion they rejected;

  • explain each decision;

  • rewrite one section independently;

  • transfer the same principle to a new paragraph.

Assessment now focuses on judgment and learning, not simply on ownership of generated words.

From Assess to Adapt

Assessment should produce consequences.

If the evidence does not affect subsequent instruction, much of its formative value has been lost.

The next question is:

What should change because of what I have learned?

4. 🔄 ADAPT — Professional and AI Adaptability

Learn how to keep learning.

Adaptation means responding intelligently to evidence and change.

It is broader than changing a lesson plan.

Teachers adapt:

  • instruction;

  • tasks;

  • materials;

  • scaffolds;

  • assessment;

  • feedback;

  • grouping;

  • technology use;

  • policies;

  • assumptions;

  • professional knowledge;

  • and sometimes their understanding of the problem itself.

AI adaptability should therefore not be confused with mastering a collection of tools.

Specific AI tools will disappear, merge, improve, become inexpensive, become embedded in other systems, or be replaced.

The durable capability is learning how to evaluate unfamiliar technologies and reconsider practice responsibly.

Central question

Given what I now know, what should change?

Questions about instructional adaptation

  • What should I continue doing?

  • What should I stop doing?

  • What should I modify?

  • What should I try next?

  • Which learners require a different scaffold?

  • Which part of the sequence should change?

  • Should I provide more modeling?

  • Should I remove support?

  • Should learners receive additional practice?

  • Is the task too difficult?

  • Is it too easy?

  • Does the problem lie in instruction, assessment, materials, prior knowledge, or something else?

  • What would I do differently if I taught this lesson tomorrow?

Questions about assumptions

Adaptation may require more than modifying an activity.

Ask:

  • Was my original interpretation correct?

  • Did I misdiagnose the problem?

  • What surprised me?

  • What contradicted my expectations?

  • Which assumption did the evidence challenge?

  • Did learner behavior reveal something I had not considered?

  • Do I need additional evidence before acting?

  • Am I changing the learner when I should be changing the task?

  • Am I changing the task when the objective itself needs reconsideration?

Questions about technology

  • Did the technology improve learning?

  • Did it simply make the task faster?

  • Did it create new dependencies?

  • Did it create new opportunities?

  • Did it make learner thinking more visible?

  • Did it hide learner thinking?

  • What became easier?

  • What became harder?

  • What became possible that was previously impractical?

  • What important human activity might be displaced?

  • Should AI use increase, decrease, or change?

  • What should learners now learn because this technology exists?

  • What no longer deserves substantial instructional time because technology can reliably perform it?

  • What becomes more important precisely because AI can perform other tasks?

Questions about professional development

  • What do I need to learn next?

  • What evidence suggests this is a worthwhile area of development?

  • Which capability will remain useful even if a particular tool disappears?

  • What aspects of my practice are becoming more valuable?

  • Which routines are becoming obsolete?

  • What new ethical responsibilities are emerging?

  • What should novice teachers now learn that previous generations of teachers did not need to learn?

  • Which traditional teacher capabilities remain indispensable?

  • What should teachers know about AI?

  • What should teachers know because AI exists?

Adaptation and human comparative advantage

As AI capability improves, teacher education should continually revisit the question:

Where does human professional judgment add distinctive value?

Likely areas include:

  • understanding individual learners;

  • interpreting incomplete evidence;

  • navigating ambiguity;

  • establishing relationships;

  • motivating learners;

  • recognizing emotional and social dynamics;

  • making context-sensitive decisions;

  • ethical judgment;

  • negotiating classroom communities;

  • designing meaningful learning experiences;

  • evaluating whether technology is educationally appropriate;

  • determining what counts as convincing evidence of learning;

  • and accepting responsibility for consequential decisions.

Example

A teacher discovers that AI can generate excellent grammar exercises in seconds.

The appropriate adaptation may not be to prohibit AI-generated exercises.

Instead, the teacher delegates routine item generation to AI and invests more professional attention in:

  • diagnosing learner difficulties;

  • selecting appropriate examples;

  • deciding sequence;

  • monitoring learner responses;

  • interpreting errors;

  • giving feedback;

  • and determining whether learners can transfer the grammar into meaningful communication.

AI changes where professional effort is invested.

NDAA as a Continuous Cycle

NDAA should not be understood as four boxes completed once.

It is recursive.

NOTICE
What is happening?
        ↓
DECIDE
What should I do, and why?
        ↓
ASSESS
What evidence shows what happened?
        ↓
ADAPT
What should change?
        ↓
NOTICE AGAIN
What is happening now?

Every adaptation creates a new situation worth noticing.

Every assessment may reveal something that changes the original interpretation.

Every decision creates new evidence.

Professional development therefore occurs through repeated cycles of increasingly sophisticated judgment.

The NDAA Logic

A compact version of the framework is:

StagePrimary QuestionProfessional Capacity
NoticeWhat is happening that matters?Professional vision
DecideWhat should I do, and why?Pedagogical judgment
AssessWhat evidence shows whether learning occurred?Assessment literacy
AdaptWhat should change because of the evidence?Professional adaptability

A more complete sequence is:

Evidence → Interpretation → Decision → Action → Evidence → Revision

This makes an important distinction:

NDAA is not merely a teaching cycle.

It is a professional reasoning cycle.

What NDAA Is Not

NDAA is not:

  • a prescribed teaching method;

  • a four-step lesson plan;

  • a checklist that guarantees good teaching;

  • a rejection of teaching methods;

  • a rejection of educational technology;

  • an argument that teachers should make every decision without AI;

  • or a claim that experience alone produces expertise.

Instead, NDAA asks teachers to make their reasoning increasingly visible, evidence-informed, defensible, and revisable.

NDAA and Generative AI

Generative AI makes all four elements more important.

NOTICE

Teachers must notice the changing relationship among:

  • learner capability;

  • AI capability;

  • task demands;

  • and visible evidence of learning.

DECIDE

Teachers must decide:

  • when AI use supports learning;

  • when it undermines the learning objective;

  • what should be delegated;

  • and what requires human judgment.

ASSESS

Teachers must distinguish:

quality of output

from

quality of learning.

ADAPT

Teachers must redesign curricula, tasks, assessments, and professional roles as AI capabilities change.

This leads to an important principle:

Quote

The goal is not to prepare teachers to compete with AI. The goal is to prepare teachers to recognize where human professional judgment matters and to use AI in ways that increase rather than diminish that judgment.

NDAA Example 1 — AI-Assisted Writing

NOTICE

The teacher notices that students submit sophisticated essays but struggle to explain their rhetorical choices.

Questions:

  • Who produced the reasoning?
  • What role did AI play?
  • Can students explain the argument?
  • Can students reproduce the skill independently?

DECIDE

The teacher decides not to ban AI.

Instead, future writing tasks require students to document selected decisions and explain revisions.

ASSESS

Evidence includes:

  • final essay;
  • drafts;
  • AI disclosure;
  • explanation of revisions;
  • oral questioning;
  • independent paragraph writing.

ADAPT

The teacher shifts assessment from:

“Produce a polished essay.”

toward:

“Demonstrate the reasoning, decisions, and language knowledge involved in producing and revising an essay.”

NDAA Example 2 — Listening Comprehension

NOTICE

Students identify many individual words in an audio text but misunderstand the speaker’s main point.

DECIDE

The teacher decides that additional vocabulary exercises are unlikely to address the real problem.

Instead, instruction focuses on:

  • discourse markers;
  • stress;
  • tone groups;
  • main versus supporting information;
  • prediction;
  • and reconstructing meaning across stretches of discourse.

ASSESS

Students listen to a new passage and explain:

  • the main idea;
  • supporting information;
  • important discourse signals;
  • and how those signals shaped interpretation.

ADAPT

Future listening lessons include more explicit attention to discourse-level processing rather than treating listening primarily as word recognition.

NDAA Example 3 — Classroom Participation

NOTICE

The same four students dominate whole-class discussion.

DECIDE

Rather than assuming quieter learners lack knowledge, the teacher changes the participation structure.

Students first think individually, then discuss in pairs, and only afterward contribute to the whole class.

ASSESS

The teacher compares:

  • number of participants;
  • quality of responses;
  • learner confidence;
  • and the ideas produced under each participation structure.

ADAPT

Whole-class questioning remains part of instruction, but it is no longer the default mechanism for determining whether the entire class understands.

NDAA Example 4 — AI Lesson Planning for Novice Teachers

NOTICE

A novice teacher uses AI to generate a complete lesson plan.

The lesson looks professional, but the teacher cannot explain why particular activities appear in a particular sequence.

DECIDE

Instead of banning AI planning, the teacher educator changes the task.

The novice teacher may use AI to generate possibilities but must:

  • select among them;
  • reject inappropriate suggestions;
  • connect choices with learning objectives;
  • predict learner difficulties;
  • and justify the final sequence.

ASSESS

The teacher educator evaluates:

  • the lesson plan;
  • the novice teacher’s rationale;
  • rejected AI suggestions;
  • modifications;
  • and post-lesson reflection.

ADAPT

AI becomes a source of alternatives, while responsibility for pedagogical judgment remains with the teacher.

NDAA Reflection Protocol

This protocol can be used after a lesson, observation, teaching practicum, assessment, or professional-development activity.

👁️ NOTICE

  • What happened?
  • What specific evidence caught my attention?
  • What did learners say, write, do, or fail to do?
  • What surprised me?
  • What pattern appeared?
  • What might I have overlooked?
  • What am I assuming rather than actually observing?

Evidence:

Initial interpretation:

🧭 DECIDE

  • What problem or opportunity does the evidence suggest?
  • What is the relevant learning objective?
  • What options are available?
  • What are the advantages and disadvantages of each?
  • What does theory or previous experience suggest?
  • What contextual factors matter?
  • Which choice appears most defensible?
  • What evidence would make me reconsider?

Decision:

Rationale:

🔎 ASSESS

  • What happened after the decision?
  • What evidence changed?
  • What did learners understand?
  • What could they do?
  • What could they still not do?
  • What alternative explanation is possible?
  • Was the evidence strong enough to support my conclusion?

Evidence of learning:

Evidence still needed:

🔄 ADAPT

  • What should I retain?
  • What should I modify?
  • What should I remove?
  • What should I investigate further?
  • What will I do differently next time?
  • What new question has emerged?
  • What should I now notice?

Next adaptation:

New question for the next cycle:

A Compact NDAA Teacher-Education Routine

For lesson observations or practicum discussions, four questions may be enough:

1. NOTICE

What evidence did you notice?

2. DECIDE

What did you decide to do because of that evidence, and why?

3. ASSESS

What evidence tells you whether the decision helped learning?

4. ADAPT

What would you keep or change next time?

These questions shift post-observation discussion away from:

“Was the activity good?”

toward:

“What evidence informed your professional judgment?”

NDAA as a Teacher-Education Goal

A novice teacher may initially ask:

What should I do?

Developing expertise should gradually lead toward more sophisticated questions:

What am I noticing?

Why am I interpreting it this way?

What alternatives do I have?

Why am I choosing this response?

What evidence would indicate that it worked?

What would cause me to change my mind?

What should I do differently next time?

The goal of teacher education is therefore not merely to give future teachers increasingly large collections of activities.

It is to help them develop increasingly sophisticated ways of seeing, reasoning, evaluating, and adapting.

A Possible NDAA Principle

Quote

Professional teaching is the ability to notice meaningful evidence, make defensible decisions in response to that evidence, assess the consequences of those decisions, and adapt practice as new evidence emerges.

Related Concepts

NDAA connects naturally with:

  • formative assessment;
  • reflective practice;
  • teacher cognition;
  • professional vision;
  • adaptive expertise;
  • evidence-informed teaching;
  • Universal Design for Learning;
  • backward design;
  • learner agency;
  • metacognition;
  • reflective teaching;
  • assessment literacy;
  • AI literacy;
  • transparent AI practice;
  • cognitive ownership;
  • practitioner inquiry;
  • and teacher action research.

Related Notes

One-Sentence Version

NDAA develops teachers who can notice what matters, decide what to do about it, assess whether it worked, and adapt intelligently as learners, contexts, and technologies change.

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