Imagine two people at the starting line of a marathon. One laces up their shoes and begins to run, eventually reaching the finish line. The other hops in a car, drives the route, and arrives at the finish line very quickly.
Both made it to the end. However, only one built strength, stamina, and resilience along the way.
Using AI for learning is like completing a marathon in a car. Students may reach the end, but they did not do the work and are not any stronger for the next race.
What is going on when students use AI? Are they learning or just taking the easy way out? As an education leader, you want to make sure your students are getting the most out of their education. That is why it's crucial to have a plan in place when it comes to AI in the classroom. We will examine how you can make the most of this technology and help your students reach their potential.
The Leadership Challenge: Understanding AI's Complex Impact on Learning
As a leader, you face an unprecedented challenge with navigating AI's integration into your systems. Recent research consistently demonstrates that AI tools can provide significant short-term performance improvements. Still, these immediate gains may mask underlying concerns about long-term learning development and critical thinking skills.
Multiple studies have identified what researchers call a "dependency effect," where students show improved performance while using AI tools but demonstrate weaker outcomes when those tools are removed. This pattern suggests that immediate gains can mask fundamental learning deficits, creating a false sense of educational progress. (Gerlich et al., 2025; Mihalache et al., 2024; Zhai et al., 2024)
For example, a 2025 study in the journal Societies by Gerlich et al. found a significant negative correlation between AI usage and critical thinking, with younger participants showing higher dependency and lower scores. The mechanism appears to be "cognitive offloading” - the transfer of mental effort to external tools that should remain internal cognitive processes essential for skill development (Gerlich et al., 2025).
However, the same research offers significant hope for education leaders. Studies comparing different AI implementations show that carefully designed AI systems with built-in learning safeguards can maintain educational benefits while preserving cognitive development. The key difference lies in AI tools that refuse to provide direct answers and instead focus on guided questioning, supporting the learning process rather than completing it for students. Salman Khan, founder of Khan Academy, has championed this approach, warning against an "automation trap" where students receive immediate answers without understanding the underlying reasoning (Khan, 2025).
For education leaders, the rapid, unguided adoption of AI presents a critical challenge. The knowing and doing gap between official policy and classroom practice is widening, creating both urgency and opportunity. A recent Gallup-Walton Family Foundation study paints a clear picture of this disconnect: while 60% of teachers used AI tools for their work in the 2024-2025 school year, a striking 68% reported receiving no formal training on how to use them.
A "learning on the fly" approach is not without its benefits, as the survey found weekly AI users saved an average of nearly six hours per week. However, it also brings substantial risks. The same study revealed that a majority of teachers believe students' use of AI will decrease their independent thinking (57%) and critical thinking (52%). This chasm between informal, time-saving adoption and formal, strategic preparation creates substantial risks for student learning and development.
The Foundation: High-Yield Instructional Strategies as AI's Anchor
Before effectively integrating AI, it is important to strengthen the foundation of proven instructional practices. At Learning-Focused, we understand that technology should amplify effective teaching, not replace it. The most successful AI implementations build upon High-Yield Instructional Strategies that have consistently improved student outcomes.
Consider summarizing, one of the most powerful strategies for learning. Yet, vulnerable in the age of AI. When students learn to summarize effectively, they engage in complex cognitive work that includes distinguishing between main ideas and supporting details, identifying author's purpose and perspective, making connections between concepts, and encoding information into their understanding. This process builds the very thinking skills that make students stronger learners.
However, AI poses a direct threat to this cognitive development. When a student simply plugs a website or article into an AI tool and receives an instant summary, they bypass all the mental work that makes summarizing valuable for learning. They get the product without engaging in the process that builds their capacity to think, analyze, and synthesize information independently.
This is the same as completing a marathon in a car vs running it. This represents the core challenge education leaders face throughout AI integration. A systematic review by Zhai et al. (2024) documented how over-reliance on AI dialogue systems negatively impacts critical cognitive capabilities, noting that students who frequently rely on AI-generated content become less engaged in developing their own ideas and problem-solving skills. The thinking work, not the final summary, is where learning happens.
Other High-Yield Instructional Strategies face similar challenges and opportunities with AI integration. Scaffolding becomes more precise when AI helps teachers create differentiated supports, but the gradual release of responsibility remains essential for student development. Research indicates that AI-generated scaffolding materials can be highly effective, but success requires teachers maintaining control over pedagogical decisions while using AI to enhance strategy implementation. For instance, a 2025 Stanford study found that AI-generated scaffolds for math teachers were highly effective, but only when teachers used the tool as a thought partner to create a framework that aligns with their specific classroom needs.
Formative Assessment transforms learning when enhanced by AI's real-time feedback capabilities. Studies suggest that AI-enabled assessment tools can improve student learning achievement and self-regulated learning while maintaining instructional effectiveness. However, the essential formative assessment cycle, eliciting evidence, interpreting results, and taking instructional action, remains fundamentally work that requires teacher expertise and professional judgment.
Strategic Implementation: The "Struggle-First" Principle
The most crucial insight for education leaders comes from research on "productive struggle" and "desirable difficulties." These concepts describe cognitive challenges that may feel frustrating in the moment but improve learning over time. Educational research by Robert and Elizabeth Bjork has demonstrated that tasks like retrieving information, generating summaries, and working through errors build lasting understanding. When students allow AI to take over these tasks, they skip the mental work that strengthens their thinking.
This aligns with research by Manu Kapur on "productive failure," which found that students who attempt to solve problems before receiving instruction develop deeper understanding than those who are shown solutions from the start. If AI provides solutions too early in the learning process, students lose the opportunity to grapple with material in ways that build genuine comprehension.
Successful districts implement what we might call a "struggle-first" principle that engages students in significant independent work before providing AI assistance. This ensures that productive confusion moments, those essential cognitive challenges that build understanding, remain part of the learning process rather than being eliminated by AI shortcuts. Research by Mihalache and colleagues (2024) identified a statistically significant negative correlation between reliance on AI for assignments and students' problem-solving skills, suggesting that excessive dependence on AI can hinder the development of independent problem-solving abilities.
As education leaders, we must guide our teachers to recognize when AI scaffolding supports versus undermines these beneficial challenges. The goal is to preserve the judgment work that builds critical thinking skills while using AI strategically to enhance learning. This requires sophisticated professional development that goes far beyond technology training to focus on pedagogical decision-making.
Research suggests that the most effective AI implementations follow patterns where AI systems refuse to provide direct answers and instead focus on guided questioning. This approach maintains the cognitive work essential for learning while providing the personalized support that AI can offer most effectively.
Summarizing Enhanced, Not Replaced
Let’s examine how summarizing, a fundamental High-Yield Instructional Strategy, can be enhanced rather than undermined by thoughtful AI integration. Effective summarizing instruction already provides powerful frameworks for developing critical thinking skills, reading comprehension, and information synthesis. AI should amplify these benefits while preserving the cognitive work that makes summarizing so valuable for student development.
Consider implementing a "Read-Summarize-AI-Evaluate" model that maintains the essential cognitive processes while strategically introducing AI as a learning tool rather than a replacement.
In the first phase, students independently engage with source material, employing active reading strategies, like graphic organizers, to process and understand the content, identify key concepts, and recognize the author's purpose and perspective. This individual engagement ensures that students do the foundational cognitive work of comprehension and initial analysis.
During the second phase, students create their summary, engaging in the mental work of distinguishing main ideas from supporting details, making connections between concepts, and translating complex information into their own words. This summarization process builds critical thinking skills that transfer to many other academic and professional contexts.
The third phase introduces AI strategically. Students input the same source material into an AI tool to generate a summary, but only after completing their cognitive work. This timing ensures that AI enhances rather than replaces the learning process.
Finally, students critically compare their summary with the AI-generated version, analyzing differences in focus, accuracy, and perspective while reflecting on their own comprehension and synthesis skills. This evaluation phase teaches students to work alongside AI thoughtfully while building confidence in their analytical abilities.
This structure preserves the cognitive benefits of summarizing while teaching students essential skills for working with AI tools responsibly. Students learn to evaluate AI responses critically, understand AI's strengths and limitations in summarization tasks, and develop confidence in their analytical abilities. For education leaders, this model provides clear guidance for teachers while maintaining the instructional integrity that ensures lasting learning.
Professional Development That Transforms Practice
Education leaders face a critical challenge in preparing teachers for effective AI integration. Large-scale professional development initiatives are emerging to address this need, emphasizing teacher-driven development and educator agency rather than top-down technology implementation approaches.
However, the most effective professional development models go beyond technology training to focus on pedagogical decision-making. Teachers need to develop what researchers call "professional vision," the ability to notice classroom cues and engage in knowledge-based reasoning about when AI supports versus undermines learning goals.
Successful programs follow experiential learning cycles that include concrete experience with AI tools in classroom settings, reflective observation of student interactions and learning data, abstract conceptualization connecting experiences with pedagogical theory, and active experimentation implementing refined approaches. This cyclical process develops teacher capacity to make nuanced judgments about AI integration based on student needs and learning objectives.
The TPACK framework, enhanced for AI integration, shows particular promise with helping teachers develop technological knowledge of AI capabilities and limitations, pedagogical knowledge of teaching strategies that incorporate AI appropriately, and content knowledge enhanced by AI tools. The focus remains on pedagogical appropriateness rather than technological novelty.
For education leaders, this means investing in comprehensive professional development that addresses both the technical aspects of AI tools and the pedagogical wisdom of High Yield Strategies required for effective implementation. Teachers need time to experiment, reflect, and refine their practice within supportive learning communities that prioritize student learning outcomes over technological adoption.
Leadership Insights: Building Sustainable AI Integration
Successfully integrating AI is not just a technical challenge; it is an adaptive (human) one. Effective leaders know that teachers and staff may be resistant to new technology, and for good reason. They worry about job displacement, student data privacy, and the potential for AI to harm student thinking. These are not minor concerns; they are legitimate fears that require a thoughtful and transparent response.
Instead of dismissing these fears, effective leaders use them as a foundation for building trust. They promote AI not as a replacement for human judgment but as a powerful tool to enhance it. This means being transparent about AI’s limitations, providing comprehensive training, and creating low-stakes environments where teachers can experiment with tools and ask questions without pressure.
A Principal's Guide to AI Integration: Five Actionable Tips
For a principal, the challenge of AI integration can feel overwhelming. The key is not to have all the answers, but to start with a clear, strategic vision that builds trust and supports your teachers.
Here are five practical tips for leading AI integration in your school:
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Create a "Struggle-First" Culture, Starting with Yourself. Before asking teachers and students to grapple with AI, you need to do the same. Start by using AI tools yourself for low-stakes tasks, such as drafting emails, summarizing meeting notes, or creating professional development agendas. This hands-on experience will help you understand the tools' capabilities and limitations. Your firsthand knowledge will build credibility with your staff and allow you to model productive struggle in a way that goes beyond a simple policy memo.
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Listen First, Then Plan.
Don't roll out a top-down AI mandate. Begin by listening to your teachers' concerns, which often center on job displacement, student plagiarism, and data privacy. A RAND Corporation study found that in 2024, only about one in five principals reported their schools provided guidance on AI use, leaving most teachers feeling unsupported. Create an AI "Action Team" with a diverse group of teachers and staff to identify needs and concerns. Their insights will be far more valuable than a generic district-wide policy.
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Prioritize AI for Administrative Efficiency.
One of the most effective ways to build buy-in is by demonstrating how AI can give teachers back their most precious resource: time. According to a recent Gallup-Walton Family Foundation survey, teachers who use AI weekly save nearly six hours per week. Encourage teachers to use AI for tasks like generating differentiated practice questions, creating rubrics, or drafting parent communications. By offloading these administrative tasks, you free them to focus on the human-centered work of building relationships and providing personalized instruction.
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Establish a Clear AI Usage Policy and Monitor Its Impact.
As a school leader, you must move beyond simply banning AI. Instead, establish a clear policy that outlines when AI use is acceptable (e.g., as a research tool or for initial brainstorming) and when it is not (e.g., for direct submission of work). But a policy is only as good as its implementation. Systematically monitor AI usage to understand how students and teachers are actually using the tools. Conduct regular check-ins with your AI Action Team, review student work for signs of both productive and unproductive AI use, and gather qualitative feedback from teachers on the impact of the policy. The goal is to use this data to refine your approach, ensuring that AI enhances, rather than undermines, learning.
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Start Small with a Focus on Equity.
Intentional AI integration is crucial for preventing the technology from widening existing equity gaps. Ensure that any AI pilot programs are accessible to all teachers and students, not just those in well-resourced departments or classrooms. Provide targeted professional development and technology support to schools that need it most. Start with a single grade level or subject area, and then build on what you learn. The goal is to make AI a tool for empowering every student, not a new source of inequality.
Questions Worth Asking
As education leaders, we should be asking more than whether our students and teachers are using AI. The more important questions focus on the quality and purpose of that use.
Are they using it to train or just to ride?
Are we building systems that strengthen thinking or that make thinking unnecessary?
Are we preparing students for a future where they can work alongside AI while maintaining their essential human capabilities?
Effort is not an obstacle to learning, but rather the path to learning. While AI can be a powerful tool, it should never replace the mental work that helps students grow intellectually. We must model and teach the difference between using AI as a thinking partner versus using it as a thinking replacement.
Because the next time our students show up to the race, whether that is college, career, or life, they may not be able to bring the car. They will need the strength, stamina, and resilience that come only from doing the cognitive work themselves.
The Learning-Focused Advantage
At Learning Focused, we understand that effective AI integration builds upon proven instructional practices. Our High-Yield Instructional Strategies provide the foundation for thoughtful technology integration that enhances rather than replaces effective teaching.
When education leaders ground AI implementation in evidence-based instructional practices like summarizing, formative assessment, questioning techniques, and scaffolding, they create a system that amplifies both technological capabilities and pedagogical effectiveness. This approach ensures that AI serves learning goals rather than driving them.
Our comprehensive professional development programs help education leaders and teachers navigate the complex decisions required for effective AI integration. At Learning-Focused, we focus on developing the pedagogical wisdom necessary to use AI strategically while preserving the cognitive processes essential for student growth and development.
Works Cited
- Bjork, Robert A., and Elizabeth L. Bjork. "A New Theory of Disuse and an Old Theory of Stimulus Fluctuation." *Essays in Honor of William K. Estes*, edited by Alice F. Healy et al., Lawrence Erlbaum Associates, 1992, pp. 35–67.
- Gerlich, Robert, et al. "Frequent AI Tool Usage and Critical Thinking: A Study on Cognitive Offloading and Generational Differences." *Societies*, vol. 15, no. 1, 2025, pp. 101–118.
- Kapur, Manu. "Productive Failure in Learning Math." *Cognitive Science*, vol. 38, no. 5, 2014, pp. 1008–1022.
- Luckin, Rose, et al. *Intelligence Unleashed: An Argument for AI in Education*. Pearson Education, 2016.
- Mihalache, George, et al. "The AI-Student Symbiosis: An Exploration of AI Reliance, Problem-Solving Skills, and Academic Performance." *Journal of Educational Technology*, vol. 22, no. 3, 2024, pp. 45–62.
- Khan, Salman. "The Automation Trap: Rescuing Student Learning in the Age of AI." *[Name of Publication]* 2025, pp. [page range].
- Zhai, Xiaoming, et al. "Over-Reliance on AI Dialogue Systems and Its Negative Impact on Critical Cognitive Capabilities: A Systematic Review." *Smart Learning Environments*, vol. 11, no. 1, 2024, pp. 1–20.
