As a longtime educator, I’ve spent a great deal of time designing assessments that promote student choice and voice and enable students to truly show what they know and can do. However, with the rise of artificial intelligence in education, deciding how to assess students has become more challenging.
Students may have access to AI both in and outside of our classrooms. It can generate summaries, write essays, provide explanations, create outlines, and offer feedback within seconds for them. AI can help learning, but it requires educators to think beyond traditional assessments that worked well before AI.
A major concern is that students will cheat or that assessment is broken. Rather than focusing only on how to stop students from using AI, we need to design tasks that make students' thinking more visible. When an assignment asks students to explain their decisions, reflect on the process, make connections, analyze feedback, defend their thinking, and revise their work, learning becomes harder to outsource.
Making this shift matters because if students are only assigned to choose an answer, write a paragraph, create a presentation, or design a final product, AI can do these tasks for them.
Providing guidance for students on how to responsibly use AI is also critical.
Moving Beyond Product-Only Assessment
For years, assessments in my classroom were often built around a final submission such as an essay, project, test, or presentation. I would evaluate it, provide feedback, and move on. These all have value because students build skills in communication, organization, critical thinking, and reflection.
However, what students submit may not fully demonstrate their learning. A well-written essay does not necessarily show understanding. A correct answer does not reveal whether the student can explain the reasoning or simply guessed or used AI. A well-designed presentation does not prove that the student made the design choices on their own. For these reasons, the assessment design needs to be more intentional.
Educators need to look at what the student created, consider how they got to that point, what choices they made in the process, what information was used, how they revised their work, and what I think is important, is what can the student share about their learning. Did the student use AI (if permitted), and how?
Using these reference points will shift assessments from being a simple, quick check of understanding or task completion to showing deeper learning and content retention.
Shifting to More Visible Thinking
Finding ways to make thinking visible takes a little time at first, but it is how to truly see a student’s learning journey. To start, educators can set up a series of checkpoints. Rather than asking students to only submit a final essay, for example, teachers might ask for various artifacts from their work. Students could provide their initial question, a claim, a draft, a peer feedback reflection, revisions made, and a final explanation of what changed. These components encourage students to notice their own thinking.
Educators can also ask students to explain their thinking and choices made. Why did they select this source? Why did they organize the project in this way? Why did they reject one idea and keep another? Why did they make the revisions they did?
These explanations can provide greater insight than the final product alone.
In their work, students can show their thinking through annotations, peer discussions, drafts, concept maps, note-taking, reflections, and even demonstrations. The format may vary depending on the task, but the goal is to have students explain their learning.
Rethinking AI Use and Academic Integrity
A valid concern is that AI makes it harder to understand what students actually know. However, if we ban the use of AI entirely, it won't prepare students to use these tools responsibly.
Instead, educators should create clear expectations around when AI is and is not permitted and model this for students to show how it can be an impactful part of their learning experience. Not every task should involve AI. Students still need opportunities to think, write, create, and problem-solve without relying on a tool. However, students also need guidance in how to use AI thoughtfully, ethically, and transparently.
I have found it to be helpful to design assignments with different levels of AI use. Teachers can build AI expectations directly into assignments. Directions might say, “AI may be used for brainstorming but not for drafting,” or “You may use AI feedback, but you must submit your original paragraph, the feedback, and an explanation of your revisions.” This turns AI use into part of the learning process rather than a hidden behavior.
Establishing a structure such as this helps students understand that responsible AI use is not “all or nothing” but instead depends on the purpose of the task they are being assigned.
Transparency is key. If students use AI, they should be able to say how they used it. Did they ask for topic ideas? Did they use it to clarify vocabulary? Did they compare explanations? Did they request feedback on the organization of their writing? Did they ask for a revision?
Requiring a short AI use note can help students reflect on their choices and help teachers better understand the learning process.
Assessments Should Include Conversations
A powerful assessment tool is conversation. When students talk about their work, teachers can sense understanding, confusion, confidence, and growth in ways that are not always visible on paper.
Not every assignment needs a formal presentation. Having short conferences with students can be meaningful. When a teacher asks a student to talk about the strongest part of their work, an area where they struggled, or even what they would change if they had more time, these questions provide more insight into student thinking. And these are also great ways to build relationships that support student growth.
Students could also record short audio or video reflections explaining their process. I’ve had students do this using Padlet, and it was great for building many skills. Students can engage in peer discussions, small-group activities such as debates, or do gallery walks. These moments create opportunities for students to demonstrate more ownership in learning.
When students know they will need to explain their thinking, the assignment changes. The focus moves from simply producing something to understanding it well enough to discuss it.
Designing Assignments AI Cannot Easily Replace
No assignment is completely “AI-proof,” and that should not be the goal. A better goal is to design learning experiences that are meaningful, contextual, personal, and connected to students’ thinking.
When assignments include local context, classroom experiences, personal reflection, student choice, and live discussion, these become more meaningful and more likely to promote content retention. For example, instead of asking students to write an essay on a historical event, ask them to connect the event to a class discussion, analyze a specific primary source used in class, compare interpretations, and explain how their thinking changed. Or instead of asking for a general science report, ask students to collect observations, analyze class data, explain unexpected results, and reflect on the process.
The more connected the work is to the classroom learning experience and real-world events, the harder it is for students to lean on AI without truly engaging in the learning experience.
It should not be about making assignments more difficult just to “catch” students. By making assignments better so that students have to think, create, question, explain, and reflect, they engage in the process itself and focus less on one final product.
The Importance of Reflection
Reflection may be one of the most important assessment practices in an AI-enabled world. When students reflect, they make their thinking visible. They explain what they tried, what worked, what did not, what they learned, and what they would do differently. They see the process of learning more.
Reflection also helps students develop metacognition. They begin to understand themselves as learners. They notice when they relied too heavily on a tool. They recognize when AI helped them think differently. They learn to evaluate whether a suggestion improved their work or weakened it.
Simple reflection prompts can make a big difference:
- What part of this work best shows your thinking?
- Where did you struggle?
- What feedback helped you improve?
- How did you use AI or other tools responsibly?
- What would you change next time?
The Most Important Outcome of All
Assessment after AI is not about fear, but rather about intentional and purposeful design.
Educators do not need to forget everything they believe about assessment. Many strong instructional practices are already in place: formative assessment, conferencing, portfolios, project-based learning, performance tasks, peer feedback, revision, self-assessment, and reflection. Now, with the availability of AI, these practices are even more important.
The future of assessment will require us to find a better balance between a final learning product and the learning process. Students should still have opportunities to create what they have always created, but they should be able to explain their thinking behind it. They should know when AI is appropriate, when it is not, and how to use it in ways that support rather than replace learning.
For students to be well-prepared for the future, we have to assess how they think as well as what they produce. To do this means designing assignments and assessments that push students to question, decide, explain, revise, reflect, and take ownership of their learning.
AI can generate answers. But students still need to develop judgment.
And that may be the most important outcome of all.
