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CAIL Sandbox Docs

Sample Activities

Each activity includes a learning objective, model configuration, student instructions, and notes for instructors. Instructors need Workspace access to build and test these configurations; students use the models shared with their course group. Follow Student Onboarding to arrange access and prepare the resources.

1. Source Evaluation Workshop

Learning Objective. Students assess AI-generated claims against verified sources.

Model Configuration

  • Base model. Any Sandbox model (DeepSeek V3.2, Kimi K2.5, GLM 5)
  • Tools. Web Search enabled
  • Knowledge Base. None; do not attach course documents for this exercise.
  • System prompt
You respond to research questions for an undergraduate seminar.
When students ask about a topic, provide detailed claims with
specific dates, names, and statistics. Do not hedge or qualify
your responses. State everything confidently.

Why this prompt. The prompt asks for confident claims without qualifications so students can practice checking whether those claims are accurate. Instructions for Students

  1. Ask the model a factual question about your research topic
  2. Copy the response into a document
  3. Identify every specific claim (dates, statistics, names, quotations)
  4. Verify each claim using library databases and primary sources
  5. Mark each claim as Verified, Unverifiable, or Incorrect
  6. Write a 200-word reflection. What patterns did you notice in the errors? What made some claims harder to verify than others?

What to Watch For. Ask students to explain why a specific number or citation can make a claim seem credible, then check the claim and citation against other sources.

Variations

  • With Knowledge Base. Repeat the exercise with a grounded model (course readings uploaded). Compare error rates. Discuss what grounding changes and what it does not.
  • Cross-model. Run the same question through two different models. Compare what each gets right and wrong.

2. Peer Review Rehearsal

Learning Objective. Students practice giving constructive feedback by critiquing AI-generated writing before reviewing each other’s work.

Model Configuration

  • Base model. Any Sandbox model (DeepSeek V3.2, Kimi K2.5, GLM 5)
  • Tools. None
  • Knowledge Base. Upload your assignment rubric and 2-3 sample papers (anonymized)
  • System prompt
You are a student in {{COURSE_TITLE}}. Write a first draft
responding to the assignment below. Include some strong points
and some clear weaknesses. Your writing should be uneven —
good ideas with mediocre execution in places.

Assignment: {{ASSIGNMENT_DESCRIPTION}}

Why this prompt. An intentionally uneven draft gives students safe material to practice on. They can be honest and direct in their feedback without worrying about a classmate’s feelings.

Instructions for Students

  1. Generate a draft from the model
  2. Read it carefully against the assignment rubric
  3. Write feedback on three things
    • One strength (cite a specific passage)
    • One structural weakness (suggest a revision)
    • One claim that needs more support (recommend a source)
  4. Compare your feedback with a partner’s in response to the same draft
  5. Discuss. Where did you agree? Where did you differ? What does that tell you about the rubric?

What to Watch For. Students who are new to peer review often default to vague praise (“good job”) or surface-level criticism (“needs more detail”). Use the comparison in step 5 to help students connect their feedback to specific passages and rubric criteria.

3. Concept Translation Exercise

Learning Objective. Students deepen their understanding of a concept by explaining it to audiences with different levels of expertise.

Model Configuration

  • Base model. Any Sandbox model (DeepSeek V3.2, Kimi K2.5, GLM 5)
  • Tools. None
  • Knowledge Base. Upload course readings covering the target concept
  • System prompt
You are a learning partner in {{COURSE_TITLE}}. When a student
explains a concept to you, ask clarifying questions. Point out
gaps or ambiguities in their explanation. Do not explain the
concept yourself. Your job is to help the student refine their
own understanding through questioning.

Instructions for Students

  1. Pick a key concept from this week’s readings
  2. Explain it to the model as if talking to a classmate who missed class
  3. Respond to the model’s questions and revise your explanation as needed.
  4. Now explain the same concept for a general audience (no jargon, no assumed background)
  5. Finally, explain it as if presenting to an expert in the field
  6. Submit all three versions with a reflection. How did your understanding change across the three explanations?

What to Watch For. Compare how students explain the concept to each audience. If the versions differ only in vocabulary, ask students to consider what each reader needs explained and revise accordingly. Discuss how those revisions changed their understanding of the concept.

4. Data Interpretation Lab

Learning Objective. Students analyze data and evaluate whether AI-generated interpretations align with statistical evidence.

Model Configuration

  • Base model. A model with strong quantitative reasoning (DeepSeek V3.2, Qwen3 235B)
  • Tools. Code Interpreter enabled
  • Knowledge Base. Upload a clean dataset (CSV) relevant to your course
  • System prompt
You help students with data analysis for {{COURSE_TITLE}}.
When given a dataset, run exploratory analysis and present
findings with visualizations. Explain statistical concepts
in plain language. Always show your code.

Instructions for Students

  1. Ask the model to describe the dataset (variables, size, structure)
  2. Request a specific analysis relevant to the course topic
  3. Read through the code the model generates. You do not need to understand every line, but try to follow the logic.
  4. Examine the visualization. Does it represent the data accurately?
  5. Ask the model to interpret the results. Do you agree with its interpretation?
  6. Write a one-page analysis that includes your research question, the analysis you requested, one thing the model got right, and one thing you would change or investigate further.

What to Watch For. Students may accept the model’s interpretation without checking it against the actual output. Ask students, individually or in groups, to annotate the visualization with their observations and explain how it supports or complicates their analysis.

5. Multilingual Close Reading

Learning Objective. Students engage with primary sources in languages they are still acquiring, using AI as a bridge to deeper analysis.

Model Configuration

  • Base model. A multilingual model (Kimi K2.5, GLM 5, DeepSeek V3.2)
  • Tools. None
  • Knowledge Base. Upload primary source texts in the target language
  • System prompt
You are a language learning partner for {{COURSE_TITLE}}.
Help students read texts in {{TARGET_LANGUAGE}}. When they
ask about vocabulary or grammar, explain in context. When they
ask about meaning, push them to form their own interpretation
first. Respond in {{TARGET_LANGUAGE}} unless the student
requests English.

Instructions for Students

  1. Select a passage (200-300 words) from the uploaded texts
  2. Read it on your own first and note what you do not understand—be it vocabulary, grammar, or anything that feels unclear.
  3. Ask the model about specific words or grammatical structures that are giving you trouble
  4. After working through the language, identify a content question about the passage.
  5. Share your interpretation of the passage along with your question, then ask the model to respond.
  6. Write a response (in the target language or English, per your instructor’s guidelines) analyzing one aspect of the passage that surprised you or challenged your first reading.

What to Watch For. If students ask “What does this passage mean?” before examining the language, direct them to the vocabulary and grammatical structures they marked in step 2. The system prompt also asks them to offer an interpretation first.

Adapting These Activities

Every activity above follows the same pattern

  1. Configure the model to create a specific learning situation
  2. Give students a structured task with clear steps
  3. Build in reflection by asking students to explain how they evaluated the output
  4. Review students’ work and discuss steps they omit or find difficult

You can adapt any of these activities by changing the system prompt, swapping the knowledge base, or modifying the student instructions. Adjust the configuration and student instructions together so the model supports the assignment’s learning objective.

Callout

Share what works. Send successful adaptations to the CUNY AI Lab team at ailab@gc.cuny.edu. With your permission, the team may include them on this page.

Additional Resources

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