Imagine two students receive AI-generated feedback on the same assignment.

The first student accepts the comments without question. The system sounds confident, the feedback appears specific, and the teacher has approved the tool.

The second student notices that the feedback overlooks an important piece of evidence. They compare the comments with the success criteria, explain the mismatch, and ask the teacher to reconsider the judgment.

Which student is better prepared to learn in a world shaped by artificial intelligence?

The answer is not simply the student who knows how to use AI. It is the student who knows how to evaluate a judgment—including a judgment made by AI—and respond with evidence.

That distinction is at the heart of a recent scholarly perspective in Frontiers in Education. The article, “Beyond Passive Rights-Holders: Toward Student Moral Agency in Assessment Fairness,” argues that students should be more than recipients of assessment systems designed by adults. They should learn to reason about what makes assessment fair and participate meaningfully in creating fair assessment practices.

This is a particularly important challenge as AI becomes part of feedback, grading, tutoring, and instructional decision-making.

Student voice is important—but it is not the same as agency

Many schools already invite students to participate in assessment. Students may:

• Choose a format for demonstrating learning
• Use a rubric to assess their own work
• Give feedback to a peer
• Reflect on a grade or learning goal
• Report an assessment they believe was unfair

These are worthwhile practices. But participation can remain superficial if the rules, criteria, and definitions of fairness are still determined entirely by adults.

There is a meaningful difference between giving students a voice and helping them develop agency.

Student voice asks: How did this assessment feel?

Student agency asks: What should count as evidence of learning, and why?

Student choice asks: Which format would you like to use?

Student agency asks: Does this format preserve the intended learning goal and level of challenge?

AI compliance asks: Did you declare your use of AI?

Moral agency asks: Was the use of AI transparent, responsible, and fair to everyone involved?

The phrase moral agency may sound theoretical, but the classroom idea is practical. It means helping students notice fairness questions, consider competing interests, justify decisions, and act responsibly.

Why AI raises the stakes

AI-generated feedback can look authoritative even when it is incomplete, biased, or wrong. A polished explanation may hide weak reasoning. A recommendation may overlook context that a teacher or student can see immediately. A system may reward one kind of language, cultural reference, or demonstration of knowledge while undervaluing another.

This does not mean AI has no place in assessment. It means students need more than rules about what is permitted.

An AI-use declaration can support transparency, but it cannot answer every important question:

• Did the tool understand the intended learning outcome?
• Did its feedback match the success criteria?
• What evidence did it use—or fail to notice?
• Could bias or missing context have shaped its judgment?
• Was AI support used to remove an unnecessary barrier, or did it replace the thinking students were expected to do?
• Who is responsible when an AI-supported judgment is wrong?

Students should learn to examine these questions rather than treating technology-generated feedback as neutral or final.

Fair does not always mean identical

Assessment fairness is often confused with sameness. If every student receives the same task, time, format, and instructions, the assessment may appear impartial.

But identical conditions do not necessarily create equitable opportunities to demonstrate learning.

A student may use speech-to-text to express sophisticated reasoning. Another may demonstrate scientific understanding through a labelled model and oral explanation. A multilingual learner may need language support that does not change the targeted concept. These pathways can be different while the learning goal and intellectual demand remain consistent.

The critical question is not, “Did every student do exactly the same thing?”

It is, “Did every student have a legitimate opportunity to provide valid evidence of the intended learning?”

Students can participate in answering that question. In doing so, they develop assessment literacy: an understanding of goals, criteria, evidence, feedback, and quality.

Five classroom moves that build assessment agency

Teachers do not need to hand over every assessment decision to students. The goal is shared reasoning, not the removal of professional judgment. These five moves offer a manageable starting point.

1. Explain the reason behind the criteria

Do more than distribute a rubric. Help students understand why each criterion matters and how it connects to the learning outcome.

Ask:

• Which feedback is best supported by evidence?
• Which comment is most useful for improving the work?
• What did each reviewer notice or miss?
• Does either judgment make an unsupported assumption?

This shifts students from passively receiving feedback to examining how judgments are made.

3. Create a simple assessment appeal process

An appeal does not need to be adversarial. It can be a structured learning routine.

Invite students to identify the judgment they question, refer to the relevant criterion, provide evidence from their work, and suggest a fair resolution. The teacher still makes the professional decision, but the student learns to advocate through evidence and reasoning.

A short prompt can help:

I would like this judgment reconsidered because… The relevant criterion is… The evidence in my work is… A fair next step could be…

4. Evaluate alternative demonstrations of learning

When students have choices in how they show learning, involve them in testing whether the options are genuinely equivalent.

Ask:

• Does each option measure the same outcome?
• Does one pathway remove an irrelevant barrier?
• Does any option unintentionally reduce the required thinking?
• What common criteria should apply across all formats?

This makes inclusion more rigorous, not less. Students learn that flexibility and high expectations can coexist.

5. Build AI-use norms through cases

Instead of beginning with a list of prohibitions, give students realistic scenarios.

For example:

• A student uses AI to translate the task instructions.
• A student asks AI to generate three possible thesis statements, then develops one independently.
• A student submits AI-generated analysis as their own.
• A student uses AI feedback but discovers that it contradicts the class rubric.

Ask students what is fair, what should be disclosed, what evidence of learning is still needed, and who is responsible for checking the result.

These conversations make responsible AI use a matter of judgment—not merely compliance.

A simple fairness protocol

Students can use the following five-step routine when reviewing teacher, peer, or AI-supported assessment decisions:

• Notice: What judgment or decision was made?
• Connect: Which learning goal or criterion should guide it?
• Examine: What evidence supports the judgment? What may be missing?
• Consider: Could bias, context, access, or competing interests affect what is fair?
• Respond: Should we accept, question, revise, or appeal the decision?

Repeated across subjects and grade levels, this routine can help students become more thoughtful users of feedback and more responsible participants in assessment.

What this means for instructional design

Assessment tools—AI-supported or otherwise—should not merely generate questions, scores, and comments. Good instructional design should make the reasoning around assessment visible.

Students need opportunities to:

• Explain how their evidence connects to the criteria
• Compare human and AI-generated judgments
• Question or appeal feedback that appears inaccurate
• Identify possible bias or missing context
• Discuss whether different demonstrations preserve the intended rigor
• Help establish responsible norms for using AI

For Teacher Time Machine, this points toward a clear design principle: support the teacher’s professional judgment while giving students meaningful ways to understand and engage with assessment decisions.

An assessment can be outcome-aligned, accessible, and technically efficient yet still miss an important educational opportunity if students never learn how or why judgments are made.

A promising idea—with an important evidence caution

“Beyond Passive Rights-Holders” is a scholarly perspective and theory-building article. It is not a systematic review or an experimental study. It includes no participant sample and does not show that involving students in fairness decisions causes higher achievement, stronger motivation, or better assessment outcomes.

Its value is different. The article offers a conceptual lens that helps educators identify what may be missing from current approaches to student voice, inclusive assessment, and AI governance. It creates useful questions for future research and, more immediately, for classroom and product design.

That distinction matters. We should not present the article as proof of impact. We should use it as a well-reasoned challenge to examine our assumptions.

The goal is not less teacher judgment. It is better shared understanding.

Teachers remain responsible for designing valid assessments, interpreting evidence, and making professional decisions. Student agency does not eliminate that responsibility.

It strengthens the learning around it.

When students understand why criteria matter, how evidence is interpreted, when different pathways are fair, and how to question an unreliable judgment, assessment becomes more than a mechanism for measuring learning.

It becomes part of the learning itself.

And in an age when automated systems will make more recommendations about students—not fewer—that capacity may be one of the most important outcomes we can design for.

Research source

Beyond Passive Rights-Holders: Toward Student Moral Agency in Assessment Fairness. Frontiers in Education, published October 19, 2026. Read the full article: link

Evidence note: Peer-reviewed scholarly perspective and conceptual theory-building article. No empirical sample or causal claims.

Note AI assisted in the production of this post.

Suggested call to action

What would change if students were taught to examine assessment judgments—not simply accept them? Try one of the five classroom moves above, then share what your students notice.