All books/Designing AI-Assisted Concept-Based Inquiry Classrooms

Chapter 1724 min read

AI as Your Design Partner

Name: Designing AI-Assisted Concept-Based Inquiry Classrooms
Author: Vivek M Agarwal

"The goal isn't to have AI teach for us, but to have AI help us become the teachers we aspire to be—designers of powerful learning experiences that transform how students understand their world."

Introduction

Throughout this book, you've seen AI prompts at the end of each chapter—ready-made tools for specific CBI design tasks. But AI's potential as a teaching partner extends far beyond using pre-written prompts. When educators understand how to collaborate effectively with AI, they gain an intellectual partner that can accelerate their growth, deepen their practice, and help them create learning experiences they might never have imagined alone.

This chapter explores how to build a productive partnership with AI for CBI design. You'll learn to craft effective prompts, iterate on AI responses, and develop workflows that combine your pedagogical expertise with AI's capacity for idea generation, analysis, and synthesis. The goal isn't to replace your professional judgment—it's to augment it, helping you move from good to exceptional CBI practice.

Think of AI not as an oracle that provides perfect answers, but as a thoughtful colleague who can brainstorm endlessly, offer alternative perspectives, and help you think through complex design challenges. Like any collaboration, this one requires learning to communicate effectively and critically evaluate contributions.

15.1 Understanding AI as a Collaborative Partner

What AI Brings to the Partnership

AI large language models bring distinctive capabilities that complement human expertise:

Breadth of Knowledge AI has been trained on vast amounts of text spanning disciplines, educational approaches, and content areas. It can draw connections across domains you might not have encountered and suggest resources you might not have discovered.

Tireless Brainstorming AI never tires of generating alternatives. Ask for ten different ways to frame a concept, and you'll get ten. Ask for ten more, and it will produce them. This capacity for divergent thinking can break you out of habitual patterns.

Rapid Drafting and Iteration AI can quickly produce first drafts of lesson plans, rubrics, student materials, and other artifacts. These drafts aren't perfect, but they provide starting points that you can refine rather than building from scratch.

Multiple Perspectives AI can simulate different viewpoints—how might a struggling student perceive this task? What might a skeptical administrator ask? How would an expert in this field approach this concept? These simulated perspectives can help you anticipate challenges.

Pattern Recognition AI can identify patterns and structures—in texts you provide, in curriculum documents, in assessment data—that might take humans much longer to notice.

What You Bring to the Partnership

Your expertise remains essential and irreplaceable:

Knowledge of Your Students You know your students—their backgrounds, interests, struggles, and strengths. AI doesn't know that Marcus needs additional scaffolding or that Aisha's passion for marine biology could anchor a unit on ecosystems.

Contextual Understanding You understand your school culture, community values, available resources, and practical constraints. AI doesn't know about your 45-minute periods, shared classroom space, or principal's priorities.

Professional Judgment You can evaluate whether an AI suggestion actually makes pedagogical sense, whether it's developmentally appropriate, and whether it aligns with your educational philosophy.

Ethical Responsibility You bear responsibility for what happens in your classroom. AI can suggest, but you decide. This responsibility requires critical evaluation of all AI contributions.

Relational Expertise Teaching is fundamentally relational. You build relationships, read the room, respond to unexpected moments, and create classroom culture. AI supports your planning; you provide the human connection.

The Nature of Effective Collaboration

Productive AI collaboration isn't passive—you don't just ask questions and accept answers. Instead:

Be Directive Tell AI specifically what you need. "Help me design a lesson" yields generic results. "Help me design an inquiry launch that will hook 7th graders into investigating the concept of scarcity using a real-world example from their community" yields much more useful output.

Iterate Actively First outputs are starting points. Push back: "This is too teacher-centered. Make it more inquiry-driven." "The reading level is too high for my students." "I need more emphasis on the conceptual transfer, not just the content."

Maintain Critical Stance AI can produce confident-sounding nonsense. Always evaluate output against your professional knowledge. Does this actually make pedagogical sense? Is the content accurate? Would this work with real students?

Document What Works Keep track of prompts and approaches that produce useful results. Build a personal library of effective AI collaboration strategies.

15.2 Crafting Effective Prompts for CBI Design

The Architecture of Effective Prompts

Well-crafted prompts dramatically improve AI output quality. Effective prompts typically include:

Context Setting Tell AI who you are and what you're working on:

Your grade level and subject
Characteristics of your students
Where this fits in your curriculum
Constraints you're working within

Clear Task Specification Be specific about what you want AI to produce:

The type of output (lesson plan, discussion questions, rubric)
The scope (how long, how detailed)
The format (bullet points, narrative, template)

Quality Criteria Specify what makes a good response:

Pedagogical approach (inquiry-based, student-centered)
Conceptual focus (emphasize transfer, avoid mere coverage)
Practical constraints (available time, resources, student reading level)

Examples When Helpful If you have a model of what you want, share it. AI can learn from examples what you're looking for.

Prompt Formulas for CBI Design Tasks

Formula 1: Conceptual Unit Design

I teach [subject] to [grade level] students at [school context]. I'm designing a concept-based inquiry unit on [topic].

Student context: [relevant details about students—background knowledge, reading levels, interests, challenges]

The key concepts I want students to understand are:
- [Concept 1]
- [Concept 2]

I want students to develop these generalizations:
- [Generalization 1]
- [Generalization 2]

Please help me design:
1. A compelling inquiry launch that hooks students and surfaces prior knowledge
2. A sequence of learning experiences that build toward conceptual understanding
3. Formative assessment checkpoints to monitor understanding
4. A summative assessment that requires conceptual transfer

Throughout, ensure the unit is genuinely inquiry-based (students investigating, not just receiving information) and emphasizes transfer of conceptual understanding to new contexts.

Formula 2: Questioning Sequence Development

I'm planning a [lesson/discussion/seminar] on [topic] where students are investigating the concept of [concept] and working toward understanding that [generalization].

Please help me develop a questioning sequence that:
1. Opens with accessible questions that engage all students
2. Progressively moves from factual through conceptual to debatable questions
3. Includes questions that surface student misconceptions
4. Pushes students toward the target generalization without simply telling them
5. Ends with transfer questions that apply understanding to new contexts

For each question, briefly explain its purpose in the sequence.

My students are [grade level] and [relevant characteristics].

Formula 3: Differentiation Planning

I've designed this CBI learning experience: [describe the activity/lesson]

The conceptual target is: [concept and generalization]

In my class, I have:
- Students who struggle with [specific challenges]
- English Language Learners at [proficiency levels]
- Advanced students who [relevant characteristics]
- Students with [specific learning needs]

Please suggest specific modifications that maintain the conceptual focus and inquiry approach while making the experience accessible and appropriately challenging for each group. Don't water down the conceptual expectations—help all students access deep understanding.

Formula 4: Assessment Design

I need to assess whether students have developed transferable understanding of [concept/generalization], not just accumulated information about [specific content].

Grade level: [grade]
Unit topic: [topic]
What students have studied: [brief summary]

Please design:
1. A transfer task using an unfamiliar context that requires applying the conceptual understanding
2. Criteria that distinguish surface-level knowledge from deep conceptual understanding
3. Scaffolding for students who struggle with the transfer
4. Extensions for students who demonstrate advanced understanding

The assessment should be clearly [formative/summative] and take approximately [time constraint].

Prompt Refinement Strategies

Your first prompt rarely produces optimal results. Use these strategies to refine:

Specify What's Missing "This is good, but it needs more focus on the conceptual transfer. Currently it emphasizes content knowledge. Revise to foreground the generalization that [X]."

Adjust Level and Tone "The reading level is too high for my 4th graders. Simplify the language while maintaining the conceptual depth." "This feels too formal. Make it more conversational while keeping the intellectual substance."

Request Alternatives "Give me three different approaches to this same goal so I can choose the best fit for my students." "What's a completely different way to achieve this same conceptual target?"

Ask for Rationale "Explain why you structured it this way and what alternatives you considered." "What are the potential weaknesses of this approach and how might I address them?"

Build on What Works "I like elements 2 and 4 but not the others. Rebuild the rest to match the quality and approach of those elements."

15.3 AI-Assisted Workflows for CBI Planning

Workflow 1: Unit Design from Concept to Completion

Phase 1: Conceptual Foundation (AI-Assisted)

Start with your curriculum standards and content requirements. Use AI to help identify concepts and generate potential generalizations:

I need to teach [standards/content]. Help me identify:
1. The key concepts embedded in this content
2. Potential generalizations that capture transferable understanding
3. Connections to concepts students already know
4. How these concepts appear in students' lives and in other disciplines

Critically evaluate AI suggestions against your professional knowledge and select concepts/generalizations that resonate.

Phase 2: Inquiry Design (AI-Assisted)

With concepts selected, use AI to brainstorm inquiry questions and hooks:

For the concept of [X] and generalization [Y], help me design:
1. A compelling question that drives genuine inquiry
2. Multiple possible inquiry hooks (scenarios, problems, phenomena)
3. Questions at different cognitive levels (factual → conceptual → debatable)

Choose and refine the options that best fit your students and context.

Phase 3: Learning Sequence (Collaborative)

Design the sequence of learning experiences, using AI for specific elements:

For this inquiry sequence, help me design:
- An opening activity that surfaces prior knowledge and misconceptions
- A progression that builds from exploration to concept formation
- Discussion protocols that develop student thinking
- Scaffolded practice applying the concept to new cases

Integrate AI suggestions with your knowledge of pacing, classroom management, and student needs.

Phase 4: Assessment Development (AI-Assisted)

Use AI to help create aligned assessments:

Create formative checkpoints that assess understanding of [concept] at these stages:
- Initial exploration phase
- Concept formation phase
- Application phase

Then design a summative transfer task that requires applying understanding to an unfamiliar context.

Review and refine to ensure assessments actually measure conceptual understanding.

Phase 5: Materials Creation (AI-Accelerated)

Use AI to rapidly draft student materials:

Reading guides and note-catchers
Discussion protocols and reflection prompts
Graphic organizers
Parent communication

Always review and customize drafts for your specific context.

Workflow 2: Lesson-Level Design

Quick-Planning Protocol:

For efficient lesson planning, use this structured conversation:

You: "I'm planning tomorrow's 50-minute lesson. Students are working toward understanding [generalization]. Yesterday they [what happened]. Tomorrow I want them to [specific goal]. My constraints are [time, materials, room setup]. Suggest a lesson structure."

Review AI suggestion, then: "I like [elements]. Revise to [specific changes] and add [what's missing]."

Continue until satisfied: "Now give me the key questions I should ask, in order, with suggested wait times and follow-up moves."

Final check: "What could go wrong with this lesson and how should I be prepared to respond?"

Workflow 3: Responsive Adaptation

After teaching, use AI to analyze and adapt:

I just taught [lesson description]. Here's what happened:
- Students successfully [X]
- Students struggled with [Y]
- An unexpected opportunity arose when [Z]

The conceptual target was [concept/generalization].

Help me:
1. Analyze why the struggle occurred
2. Design tomorrow's lesson to address the gap
3. Think about how to leverage the unexpected opportunity
4. Consider whether my conceptual framing needs adjustment

Workflow 4: Curriculum Mapping

For longer-range planning, use AI to map conceptual progressions:

I'm mapping a year-long [subject] curriculum for [grade level]. The major content areas are:
[List topics/units]

Help me:
1. Identify concepts that recur across multiple units
2. Suggest a conceptual progression (which concepts build on which)
3. Identify opportunities for transfer between units
4. Find places where cross-curricular connections could strengthen understanding
5. Note potential gaps in conceptual development

15.4 Critical Evaluation of AI Outputs

Recognizing AI Limitations

AI outputs require critical evaluation because:

AI Can Sound Confident While Being Wrong AI generates plausible-sounding text regardless of accuracy. It may invent facts, misattribute ideas, or produce pedagogically unsound suggestions delivered with apparent certainty.

AI Doesn't Know Your Context AI suggestions are generic by nature. They don't account for your specific students, school culture, available resources, or community context.

AI May Perpetuate Conventional Thinking AI is trained on existing text, including conventional (sometimes outdated) educational practices. It may suggest approaches that seem reasonable but don't reflect best practices in CBI.

AI Lacks Pedagogical Judgment AI can't evaluate whether something will actually work with real students in real classrooms. It may suggest activities that are theoretically sound but practically impossible.

Quality Check Protocol

Before using any AI output, run through this evaluation:

Accuracy Check

Is the content accurate? (Check key facts, especially for academic content)
Are any educational concepts misrepresented?
Does it align with current best practices in CBI?

Alignment Check

Does this actually target conceptual understanding, or does it slip back to content coverage?
Is it genuinely inquiry-based, or is it traditional instruction dressed up in inquiry language?
Does the assessment actually measure transfer, or just recall?

Feasibility Check

Will this work in my actual classroom with my actual students?
Do I have the time and resources required?
Does the reading level match my students?

Coherence Check

Does each element actually serve the conceptual goal?
Does the sequence make developmental sense?
Are the assessments aligned with the learning experiences?

Values Check

Does this reflect my educational philosophy?
Is it culturally responsive and inclusive?
Does it serve my students' best interests?

Common AI Pitfalls in CBI Design

Watch for these frequent problems:

Concept-Content Confusion AI may conflate content topics with concepts. "The Civil War" is a topic, not a concept. "Conflict," "power," and "change" are concepts. Ensure AI responses maintain this distinction.

False Inquiry AI may suggest "inquiry" activities that are actually just discovery-of-predetermined-answers. Real inquiry involves genuine questions without predetermined conclusions.

Generalization as Fact AI may produce generalizations that are actually factual statements. "Photosynthesis converts light energy to chemical energy" is a fact. "Energy transformation enables life processes" is a generalization.

Assessment Misalignment AI may suggest assessments that measure information recall rather than conceptual transfer, even when you've requested transfer assessment.

Inappropriate Developmental Level AI may misjudge what's appropriate for different ages. Always verify suggestions against your knowledge of child/adolescent development.

Productive Pushback Strategies

When AI output falls short, push back specifically:

For Content Issues: "This contains an error. [X] is actually [Y]. Revise with accurate information."

For Conceptual Drift: "This is focusing too much on content knowledge. Bring the emphasis back to the conceptual transfer. Students should understand that [generalization], not just know about [content]."

For Fake Inquiry: "This isn't really inquiry—students are just finding answers you've predetermined. Redesign so students are genuinely investigating a question where multiple interpretations are possible."

For Level Mismatch: "This is too advanced/simple for my students. They are [description]. Adjust the complexity while maintaining the conceptual depth."

For Practicality Issues: "I only have 45 minutes and [constraints]. Make this feasible for my real situation."

15.5 Building Your AI-Assisted Design Practice

Developing Prompt Literacy

Like any skill, effective AI collaboration improves with deliberate practice:

Start Simple Begin with specific, bounded tasks: "Generate five discussion questions about this text" rather than "Help me design a unit." Build complexity as you learn what works.

Analyze What Works When AI produces something great, examine why. What did you include in your prompt? How did you frame the task? Save successful prompts for reuse.

Analyze What Doesn't When AI disappoints, diagnose the problem. Was your prompt too vague? Did you not specify important criteria? Did AI misunderstand something you assumed was clear?

Build Prompt Templates As you discover effective patterns, create templates you can reuse and adapt. Your template library becomes a valuable professional resource.

Share and Learn Exchange effective prompts with colleagues. What works for one teacher often works for others. Build collective prompt literacy.

Creating Your Personal AI Workflow

Develop routines that integrate AI into your planning practice:

Weekly Planning Routine Set aside time each week to use AI for upcoming planning. Batch your AI interactions rather than context-switching throughout the week.

Unit Launch Protocol Develop a standard process for starting new units that includes specific AI conversations for concept clarification, inquiry question development, and hook brainstorming.

Assessment Development System Create a consistent approach for using AI to develop aligned assessments, including standard prompts for formative checkpoints and transfer tasks.

Reflection Integration After teaching, use AI to process what happened and plan adaptations. This reflection-action cycle accelerates professional growth.

Maintaining Professional Agency

As AI becomes more capable, maintain clear boundaries:

AI Suggests, You Decide Never outsource professional judgment. AI provides options and ideas; you make choices based on your expertise and knowledge of your students.

Understand What You're Using Don't use AI-generated materials you don't understand. If you can't explain why a lesson is structured the way it is, you're not ready to teach it.

Develop Your Own Expertise AI is a tool for growth, not a substitute for it. Use AI collaboration to learn and develop your own capacity, not to avoid doing the thinking yourself.

Stay Current with AI Developments AI capabilities evolve rapidly. Stay informed about new developments, new tools, and evolving best practices for educational AI use.

Classroom Snapshot: AI-Assisted Unit Design in Action

Follow Ms. Rodriguez, a 6th-grade science teacher, as she uses AI to design a CBI unit on ecosystems.

Monday Evening: Conceptual Foundation

Ms. Rodriguez opens her AI assistant with her curriculum requirements.

Her prompt: "I need to teach 6th-grade science standards on ecosystems, interdependence, and energy flow. Help me identify the key concepts embedded in this content and suggest generalizations that capture transferable understanding—things students could apply beyond just ecosystems."

AI response: Offers several concepts (interdependence, energy transfer, equilibrium, adaptation) with potential generalizations for each.

Her evaluation: She selects "interdependence" as her core concept and refines the generalization: "Changes to one part of a system create effects throughout the system, often in unexpected ways." She notes this applies to ecosystems, but also to social systems, economic systems, and more—good transfer potential.

Tuesday Evening: Inquiry Design

Her prompt: "I've selected interdependence as my core concept with the generalization 'Changes to one part of a system create effects throughout the system.' I want to design an inquiry hook that will grab 6th graders. They're an urban school, diverse backgrounds, many haven't had much nature exposure. Give me five different possible hooks."

AI response: Offers options including a local urban ecosystem disruption, a simulation game, a mystery scenario, and more.

Her evaluation: She likes the mystery scenario—"Why did all the fish die in Miller's Pond?"—because it's local, investigable, and genuinely puzzling. She asks AI to develop this hook further, specifying her 50-minute launch period.

Wednesday Evening: Learning Sequence

Her prompt: "For this Miller's Pond mystery unit, help me design a 2-week learning sequence. I have 50-minute periods, students can work in groups, I have access to basic lab materials and computers. The sequence should build from initial investigation through concept formation to transfer."

AI response: Outlines a day-by-day sequence with investigation phases, data analysis, concept building, and transfer activities.

Her evaluation: The sequence is solid but too content-heavy in the middle. She asks: "The sequence from days 5-8 feels like traditional instruction. Maintain the inquiry stance throughout. Students should be constructing understanding, not receiving it."

AI revision: Offers a redesigned middle section with student-led investigation, peer teaching, and guided concept formation.

Thursday Evening: Assessment Development

Her prompt: "Now I need assessments. Design formative checkpoints for days 3, 6, and 9 that assess understanding of interdependence—not just recall of ecosystem facts. Then design a summative transfer task where students apply the concept of interdependence to a system we haven't studied—NOT an ecosystem."

AI response: Provides formative checkpoints and a summative task about a supply chain disruption.

Her evaluation: The formative checkpoints are good. The summative task is interesting but too complex for 6th graders. She asks for alternatives and selects a simpler transfer context—how removing one member from a team affects the whole team.

Friday Evening: Materials Refinement

Ms. Rodriguez uses AI to draft:

Student investigation guides
Discussion questions for key conversations
A graphic organizer for tracking system relationships
Parent communication about the unit

She reviews all drafts, adjusting reading levels, adding her own examples, and ensuring everything aligns with her knowledge of her students.

Total AI collaboration time: About 3 hours across the week Result: A sophisticated CBI unit she couldn't have designed as well or as quickly on her own

Her reflection: "AI didn't design this unit—I did. But it helped me think through options I wouldn't have considered, draft materials faster than I could alone, and catch gaps in my planning. The unit is better because of the collaboration, but it's still mine."

Templates for AI-Assisted CBI Design

Template 1: Comprehensive Unit Design Prompt

AI PROMPT FOR CBI UNIT DESIGN

==CONTEXT==
Subject: _______________
Grade Level: _______________
School Context: _______________
Time Available: _______________ (weeks/periods)

Student Characteristics:
- Prior Knowledge: _______________
- Reading Level Range: _______________
- Cultural/Community Context: _______________
- Special Considerations: _______________

==CURRICULUM REQUIREMENTS==
Standards to Address:
- _______________
- _______________
- _______________

Required Content:
- _______________
- _______________

==CONCEPTUAL FRAMEWORK==
Core Concept(s): _______________

Target Generalization(s):
1. _______________
2. _______________

==DESIGN REQUEST==
Please help me design:

1. INQUIRY LAUNCH
- A hook that engages students and surfaces prior knowledge
- An authentic context or problem that creates genuine inquiry
- Opening questions that frame the investigation

2. LEARNING SEQUENCE
- A progression from exploration to concept formation
- Key learning experiences with estimated timing
- Points where formative assessment should occur

3. ASSESSMENT
- Formative checkpoints for monitoring understanding
- Summative transfer task using unfamiliar context

4. MATERIALS NEEDED
- Key resources and materials
- Scaffolds for struggling learners
- Extensions for advanced learners

==QUALITY CRITERIA==
Ensure the design:
- Is genuinely inquiry-based (students investigating, not just receiving)
- Emphasizes conceptual transfer over content coverage
- Provides multiple access points for diverse learners
- Is feasible given my time and resource constraints
- Positions teacher as facilitator, not information deliverer

Template 2: Prompt Refinement Tracker

Original Prompt:

AI Response Summary:

What Worked:

What Didn't Work:

Refinement #1: Request: _______________________________________________ Result: _______________________________________________

Refinement #2: Request: _______________________________________________ Result: _______________________________________________

Refinement #3: Request: _______________________________________________ Result: _______________________________________________

Final Output Quality: ☐ Excellent ☐ Good ☐ Acceptable ☐ Needs Work

Lessons for Future Prompts:

Save This Prompt Sequence? ☐ Yes ☐ No Category for Filing: _______________________________________________

Template 3: AI Output Evaluation Checklist

AI Output Being Evaluated:

ACCURACY CHECK ☐ Content is factually accurate ☐ Educational concepts are correctly represented ☐ Aligns with current best practices ☐ No concerning errors or misrepresentations