Critical thinking, a crucial skill for our students’ career success, involves analyzing information, evaluating its reliability, and making judgments based on it. However, mastering critical thinking requires a deep understanding of the qualities of what we are evaluating and the criteria appropriate to the evaluation of those qualities (Paul, 1993). Without knowing why and how we are thinking critically, we risk falling into the trap of bias and faulty thinking (Nelson, 2024).
As an expert in both content and critical thinking in your field or industry, you are likely to identify the shortcomings of generative AI (AI) outputs easily. However, as novices students are more likely to struggle to notice patterns, gaps, assumptions, and errors in thinking (Nelson, 2024). Students may shortcut the AI output review process if they lack a critical thinking framework to apply. A 2024 research study found that while AI use for research reduced cognitive load and simplified answer-finding for university students, AI use weakened reasoning and reduced in the depth of engagement that comes with searching through diverse sources and critically evaluating them (Stadler, Bannert, & Sailer, 2024).
How can AI be used to foster rather than replace students’ critical thinking? The answer may lie in providing worked examples demonstrating the steps of critical thinking. This approach uses AI not as an answer tool but a thinkingprocess tool. Making the critical thinking process visible using AI can also show students its capabilities and weaknesses as a tool that predicts but cannot “think” critically (MIT Horizon, 2024).
See 3 different analogies for promoting critical thinking with AI in your classroom:
The Navigator’s Map: This approach involves analyzing an AI output by observing its details and then comparing it with a reliable outside source. In this analogy, an AI output is the map being tested or reviewed, the AI user is the journeyer, and the terrain or geographical space is the verified or credible source against which the AI output is compared.
The Sculptor’s Stone: This approach involves establishing quality criteria and then iteratively improving an AI output by refining it to meet specific criteria, specifications, requirements, or standards. In this analogy, the AI user is the sculptor, the AI output is the stone, and AI prompts are the tools that “chisel” the stone to become the desired sculpture.
The Gardener’s Tree: This approach involves generating ideas, making connections between them, and extending them into broader applications. In this analogy, the AI user is the gardener, the initial AI outputs are the seeds, the connections made between ideas are the roots and branches, and the extended or specific applications of these ideas are the blossoms and fruits.
3 In-Class Models to Promote Critical Thinking with genAI: The Navigator’s Map, The Sculptor’s Stone, the Gardener’s Tree
The Navigator’s Map: Observe, Question, Compare (OQC)
This in-class approach involves analyzing an AI output by observing its details and comparing its information with an authoritative source, such as a trusted textbook, article, or website. This approach helps students to analyze information carefully and recognize that AI cannot reliably check or validate its own outputs.
Here are the three steps to show in a worked example in class:
Observe – Identify the features of the AI output, even if it feels self-evident. For example, how many points are there? What are the points? How long are the points?
Question – Brainstorm questions to ask and possible answers based on initial impressions – no question is a bad one! For example, Is this true and accurate? Is it specific and relevant? Is it fair? Is it usable? What might be missing?
Compare/Check – Find an authoritative source to review or compare with the AI output. This could be a trusted family member; laws, legislation, or policy; market or research data; standards and best practices; webpage or professional association information; textbook or journal article. Or, you may create your own resource.
Example in Practice
Here is an example of using the OQC approach in class, using the topic of creating a healthcare plan.
Step
Instructions
AI Script/Prompt
Notes
Observe
Identify the features of the AI output.
Let’s use AI to “Generate a brief patient care plan for a 65-year-old patient with diabetes.” Let’s make a list of what we notice about what is in the plan.
Encourage students to note the number of points, their content, and length in the AI-generated care plan.
Question
Brainstorm questions based on initial impressions.
Let’s ask ourselves: “What questions arise from the AI-generated care plan? What do we like? What do we not like? Write them down.”
Remind students that no question is a bad one. Questions could be about the accuracy, relevance, fairness, usability, or any missing information.
Check
Find a source to review or compare with the output.
Now, let’s compare the AI-generated care plan with established care guidelines for diabetes. Note the differences and similarities. What do the guidelines tell us that are not incorporated into the AI-generated plan?”
Students can use established care guidelines as the authoritative source. Discuss the importance of cross-verifying AI outputs with reliable sources.
Example of the Observe, Question, and Compare Approach using a healthcare topic.
When to use OQC: This critical thinking approach may be useful when AI provides biased, inaccurate, or hallucinated answers, when human oversight is needed to interpret and verify outputs, or when students must practice using authoritative sources.
The Sculptor’s Stone: Review, Evaluate, Re-prompt (RER)
This second approach focuses on defining what quality is for a particular task and then evaluating an AI output against that criteria. It also invites students to use the established criteria to improve what the chatbot initially created. For example, in a career development class, students could use AI to generate a cover letter, then refine the prompt based on specific criteria such as values alignment, research about the organization, and personal details to get a nuanced letter.
Here are the three steps to show in a worked example in class:
Review Criteria – What is a quality, desirable, standard, or “good” output? This could include specificity, context, organization, clarity, usability, format, authenticity, or emotion.
Evaluate – What are the gaps between the criteria and the output? What could AI add, delete, or modify? What advice does AI give for improving, based on the feedback it provides? What might AI not be able to improve, given its inherent limitations?
Refine the Prompt – Give further instructions to AI using identified criteria. Regenerate the output with a more detailed or specific prompt, or decide to stop prompting and make human changes.
Example in Practice
Here is an example of using the RER approach in class, using the topic of a marketing campaign.
Step
Instructions
AI Script/Prompt
Notes
Review
Identify the quality criteria for a marketing campaign.
Let’s list what makes a good marketing campaign, for example, for a new fitness product. What do we need to know to determine whether it’s a good campaign?
Discuss with students what makes a good marketing campaign. This could include specificity, details related to context or organization, clarity, usability, format, authenticity, and human emotional responses. AI may also be able to generate a starter list of criteria.
Evaluate
Identify the gaps between the criteria and the output.
Based on the criteria we established in step 1, let’s refine the marketing campaign idea to target Ontario millennials interested in fitness and wellness to build a healthy lifestyle and save money over a year. Please ask AI to regenerate the original draft, and add more criteria to improve the output. Now, let’s ask, “What’s changed? What’s still missing? What should we try again? When should we stop?”
Encourage students to think about what AI could add, delete, or modify. What should be selected, and what should be discarded? What advice does AI give for how to improve? What might AI not be able to improve?
Re-prompt
Give further instructions to AI using identified criteria.
Based on the criteria we established in step 1, let’s refine the marketing campaign idea to target millennials in Ontario who are interested in fitness and wellness to build a healthy lifestyle and save money over a year. Please ask AI to regenerate the original draft, and add more criteria to improve the output. Now, let’s ask, “What’s changed? What’s still missing? What should we try again? When should we stop?”
Discuss with students the importance of specific prompts in generating quality AI outputs. Include a discussion of what has improved and what has not improved. Discuss the limits of AI as a “thinking” tool, and the benefits of human thinking in improving outputs.
Example in practice of the Review, Evaluate, Reprompt (RER) model using the topic of marketing
When to use RER: This critical thinking approach may be useful when AI provides general, prototypical, exaggerated, embellished, superficial, or substandard outputs. This approach helps students to understand quality criteria and then evaluate or revise an output against those criteria.
The Gardener’s Tree: Ideas, Connections, Extensions (ICE)
The ICE model (Smith & Johnson, 2023) involves generating ideas, making connections between them, and extending them into new applications. For instance, students could use AI to brainstorm solutions for a community issue, connect these ideas to existing initiatives, and extend them into actionable plans. This helps students develop a holistic understanding and creatively apply their critical thinking skills.
Here are the three steps to show in a worked example in class:
Ideas – Generate initial ideas using AI. For example, what are some potential solutions to reduce plastic waste in the community?
Connections – Identify and explore connections between these ideas. For example, how can these solutions be integrated with existing recycling programs or community initiatives?
Extensions – Extend these ideas into broader applications. For example, how can these solutions be scaled up or adapted for other environmental issues?
Example in Practice
Here is an example of the ICE approach using the topic of reducing plastic waste.
Step
Instructions
AI Script/Prompt
Notes
Ideas
Generate initial ideas using AI.
Let’s use AI to “Generate a list of potential solutions to reduce plastic waste in our community.”
Encourage students to brainstorm freely and list all ideas generated by the AI.
Connections
Identify and explore connections between these ideas.
Let’s ask ourselves: “How can these solutions be integrated with existing recycling programs or community initiatives?”
Remind students to think about how different ideas can complement each other and create a more comprehensive approach.
Extensions
Extend these ideas into broader applications.
Now, let’s consider: “How can these solutions be scaled up or adapted for other environmental issues?”
Students can think about the long-term impact and potential for broader application of their ideas.
Example in practice of the Ideas, Connections, Extensions (ICE) model
When to use ICE: This critical thinking approach may be useful when AI outputs lack nuance, context, specificity, affect (emotion), interconnectedness, complexity, or ethical implications. This approach helps students to understand the unique human capabilities that can “do more” with AI outputs.
Final thoughts
Here are some ideas to consider if you decide to bring one of these activities to your in-class lessons (Walter, 2024):
Choose topics of interest and relevance to students
Describe what you are doing as you do it to show how critical thinking with AI works
Be ready to pivot if the output is different from what you expect
Encourage students to notice, decide, and recommend ideas during each step
Remind students of their unique human capabilities for solving complex, real-life problems
Relate examples to critical thinking in your field or professional area
By using AI not just as an answer tool but as a process tool, we can help students shift from being passive recipients of information to actively engaging with it, questioning it, and evaluating it.
References
MIT Horizon, (2024). Critical thinking in the age of AI.
Nelson, N. (2024). Inference ladder to develop students’ critical thinking. LinkedIn.
Paul, R. (1993). Critical thinking: What every student needs to survive in a rapidly changing world. Dillon Beach: CA: Foundation for Critical Thinking.
Smith, J., & Johnson, R. (2023). Integrating Ideas, Connections, and Extensions in Educational Practices. Journal of Educational Psychology.
Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://doi.org/10.1016/j.chb.2024.108386
Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(15), p.1-29.
AI Disclosure
Examples were created in draft format by Copilot, then edited and proofed.
Elan Paulson
Elan Paulson, PhD, has been an educator in Ontario's higher education system since 2004. Before joining Conestoga as a Teaching and Learning Consultant, Elan was on the executive team at eCampusOntario. She previously served as Program Director and as an instructor in professional education programs at Western University's Faculty of Education. With a Master's in Educational Technology, Elan specializes in technology-enabled and collaborative learning to support diverse learners. She has also conducted research on faculty participation in communities of practice for professional learning and self-care.
