Pre-prompting is the practice of igniting student thinking before they use AI tools. Instead of banning AI outright, you can make part of a student’s grade based on their pre-prompt work. This way, you’re assessing both:

1. Their thinking: the observations, questions, and hypotheses they generate.

2. The quality of the prompt itself: how well they set up the AI to give meaningful, usable answers.

This shifts assessment away from “Did you give a good answer?” toward “How well did you design your interaction with AI?”

👉 Pre-prompting positions AI as a partner in inquiry rather than a shortcut. By requiring students to surface observations, questions, and hypotheses first, you make their cognitive process visible and assessable before they ever type into a chatbot.

When educators use pre-prompt assessments—asking students to share their reasoning, framing, or intentions before engaging with AI—they gain a window into the thought processes that shape how students interact with the tool. This approach foregrounds students’ judgment, creativity, and ethical discernment, allowing teachers to track not just what the AI produces, but how learners are orienting themselves in relation to it. 

By contrast, when there is no pre-prompt assessment, or worse, when AI is banned altogether, students’ interactions with AI remain hidden, reducing opportunities for reflection and dialogue. 

In such contexts, AI use tends to move underground, framed by secrecy rather than accountability, and educators are left evaluating polished outputs without insight into the relational and cognitive pathways that produced them. Pre-prompting, then, shifts assessment from policing to accompaniment: instead of controlling whether students use AI, it supports them in becoming more intentional and transparent about how they use it.

Why a Rubric for Pre-Prompting?

When students engage in pre-prompt activities, much of the work is thinking, not a finished product. Without a clear framework, this thinking can remain invisible. The Cognitive Ignition Rubric makes those processes observable and assessable. It allows teachers to grade the quality of the student’s reasoning and the strength of the prompt they produce, rather than just the essay or the AI-generated content.

How to Apply the Rubric

1. Assign the Pre-Prompt Task
   Example: “Before you ask AI about WWII and women, write down three observations, two questions, one hypothesis, and a prediction about how AI might answer you.”

2. Collect Student Responses
   These can be quick notes, bullet points, or short paragraphs. They don’t need to be polished essays.

3. Score with the Rubric

   • Observation: Did the student notice specific, relevant details?

   • Questioning: Were the questions thoughtful and original, or basic and predictable?

   • Hypothesis: Did the student form a logical, evidence-based prediction?

   • Metacognition: Did they reflect on how they approached the task or what they expect from AI?

4. Give Feedback

   • Highlight one strength: “Your observation of postwar pushback on women’s roles is excellent.”

   • Suggest one growth area: “Try to form a hypothesis that connects your observation to long-term social change.”

5. Close the Loop
   Have students compare their pre-prompt thinking with what AI actually provided. This builds critical reading of AI output and shows whether their initial reasoning holds.

Student Pre-Prompt Samples (WWII & Women Essay)

Assignment:

Essay: “Discuss World War II and its impact on the place of women in society.”

1. Beginning (Level 1)

• Observation: “Women worked while men fought.”

• Question: “What did they do?”

• Hypothesis: “I think they just did men’s jobs.”

• Anticipation: “AI will tell me women worked in factories.”

Why this is Beginning:
Observations are vague (“worked”), questions are surface-level (“what did they do?”), hypothesis is generic (“did men’s jobs”), and anticipation is minimal.

2. Proficient (Level 3)

• Observation: “Lots of women started working in factories, like the posters of Rosie the Riveter. They also joined the army but not in combat.”

• Question: “Did women want to stay in those jobs after the war, or were they forced out?”

• Hypothesis: “Probably many were forced out when men returned, but some changes stayed, like more women wanting to work.”

• Anticipation: “AI will probably say women helped in factories and then men took over again. It might not explain how that affected women’s rights after.”

Why this is Proficient:
Observation is concrete (factories, military service, posters). Question shows curiosity about long-term effects. Hypothesis is logical but not deeply layered. Anticipation shows some critical awareness of AI’s limits.

3. Exemplary (Level 4)

• Observation: “Before WWII, many women were housewives or did domestic work. During the war, they worked in factories, farms, and even joined military support roles. After the war, there was pressure for them to return home, but not all did.”

• Question: “Did WWII create permanent change in women’s roles, or was it more of a temporary shift that just delayed until the women’s movement in the 1960s?”

• Hypothesis: “I think it was both: the war didn’t instantly change women’s rights, but it showed society that women could handle jobs outside the home, which later helped movements for equal pay and equality.”

• Anticipation: “If I ask AI, it will probably give a balanced answer about short-term vs long-term changes. I’ll need to ask it to compare different countries, because otherwise it might just focus on the U.S.”

Why this is Exemplary:
Observation is nuanced (pre-war vs wartime vs postwar). Question is layered (temporary vs permanent change). Hypothesis is reasoned and forward-looking. Anticipation anticipates AI’s strengths and gaps (U.S.-centric).