Rethinking the Climb: Education in the Age of AI

Across classrooms in America—from kindergarten through university—an old ritual continues quietly: students pack their bags with books, turn through chapters, absorb formulas and lists, and are told this is learning. And in many ways, it is. This is the architecture of formal education—the visible structure we’ve built over generations to transmit knowledge. But it’s not the whole story.

A deeper view sees something more: not just the materials of education, but the movement of the learner through them. A way to understand this process is as a learning ascent—a full-spectrum journey that takes a student not only through content, but toward transformation. It might begin with books and lessons, but it deepens through struggle, through dialogue, through questions that don’t have simple answers. At its highest point, it isn’t about what someone knows, but who they’re becoming.

This kind of ascent has always existed. It shows up in every culture and tradition—as apprenticeship, as initiation, as study. But today, it faces new pressure. Automation, distraction, rigid systems, and algorithmic shortcuts threaten to collapse the journey into fragments. If we’re not careful, we’ll trade meaningful learning for rapid interaction with machine outputs—and call it progress.

The truth is: a learner’s direction is shaped by the map they’re handed. And not all maps lead upward. Some flatten the path. Some rush students toward goals that aren’t worth reaching—credentials without depth, compliance without understanding, information without wisdom.

So the questions become urgent: Are we preparing students to become more capable humans, or more efficient tool-users? Are we teaching discernment, or just navigation? Are we helping them climb—or just move quickly?

To think more clearly about these questions, I began working with ChatGPT—not to automate the answers, but to explore them. I tested assumptions, compared perspectives, and refined ideas through an ongoing exchange. The goal wasn’t to arrive at a perfect answer, but to better frame the problem: what should today’s students still learn deeply, and what can we afford to let go?

That process led to what I’ve called the urgency index—a list of forty waypoints in the learning journey that deserve renewed attention, rethinking, or release. It’s not definitive. It’s not institutional. It’s just a starting point—created with the help of AI, shaped by human judgment, and offered in the hope that it helps parents and educators reorient the climb.

Because the future will not wait. And neither should we.

Let’s revisit the ascent. Let’s check the map. Let’s change the path—before our students climb brilliantly in the wrong direction.

The Urgency Index

Quadrant I: Human Core Learning
High Human Urgency, Low AI Proximity
This quadrant includes the foundational capacities that AI cannot replicate—judgment, ethics, interpretation, interpersonal insight, and purpose-driven reasoning. These are the summit skills that make us uniquely human. In the traditional learning ascent, these areas have often been treated as secondary—tucked into electives, or marginalized in favor of testable outputs. But in an AI-saturated world, this is where humans must go deeper, not shallower.

Urgently Emphasize:

🟩 Ethical Reasoning
Students must learn how to navigate ambiguity, weigh competing values, and take responsibility for consequences—skills AI can’t internalize or model.

🟩 Rhetoric and Persuasive Writing
The ability to craft arguments, anticipate objections, and move an audience is an irreplaceable human art, essential for leadership, citizenship, and influence.

🟩 Critical Reading of Texts
Close reading, interpretation, and understanding subtext are crucial skills for navigating a world full of information, noise, and subtle manipulation.

🟩 Philosophical Inquiry
Students should explore big questions: What is justice? What is the good life? What does it mean to know something? This develops intellectual humility and clarity.

🟩 Listening and Dialogue
The art of inquiry-driven conversation—asking better questions, disagreeing respectfully, and synthesizing multiple viewpoints—is a core civic skill.

🟩 Self-Awareness and Reflection
Courses that teach journaling, introspection, and metacognition build habits of mind essential for lifelong learning and wise judgment.

🟩 Cultural and Historical Literacy
Deep understanding of different cultures, traditions, and historical contexts fosters empathy and reduces the flattening effects of algorithmic information streams.

🟩 Improvisation and Play
Drama, storytelling, and improvisation develop mental agility, emotional range, and comfort with uncertainty—traits critical in unpredictable futures.

🟩 Public Speaking
Speaking with clarity and presence to real people in real rooms builds confidence, coherence, and conviction—things that don’t arise from writing alone.

🟩 Long-Form Thinking and Synthesis
Pushing past shallow answers and training students to integrate knowledge across disciplines helps them resist surface-level AI outputs and see deeper patterns.

Quadrant II: Human–AI Synergy
High Human Urgency, High AI Proximity
This quadrant includes fields where AI is deeply embedded—but where human guidance, oversight, and ethical integration are essential. Students still need to learn these areas, but in fundamentally new ways. The traditional ascent often overemphasizes manual execution. The new ascent requires understanding how to work with AI, not compete with it.

Urgently Reframe and Emphasize Differently:

🟦 Data Literacy and AI Interpretation
Students must learn to read, question, and interpret outputs from AI systems—not just generate them. Understanding bias, model limitations, and appropriate use cases is essential.

🟦 Computational Thinking
Rather than coding syntax, learners should understand systems logic, abstraction, automation, and iterative design thinking.

🟦 Applied Statistics and Probabilistic Reasoning
Interpreting uncertainty, causality, and risk is more vital than ever in a world driven by AI predictions.

🟦 Visual and Information Literacy
Students must learn to decode charts, models, infographics, and dashboards with nuance and skepticism—not just absorb them passively.

🟦 Collaborative Tool Fluency
Knowing how to effectively co-create and coordinate using AI-assisted platforms is a modern workplace necessity.

🟦 Scenario-Based Learning
Instead of isolated exercises, students should engage in simulations and real-world decision-making with AI as part of the toolkit, not the answer.

🟦 Responsible Use of Generative AI
Ethical and strategic use of tools like ChatGPT, Midjourney, and others should be modeled in assignments and instruction.

🟦 AI-Aided Writing and Editing
Rather than forbidding AI use in writing, educators should teach students how to use it critically—brainstorming, drafting, revising with eyes open.

🟦 Digital Research and Source Evaluation
With AI accelerating search and synthesis, students must develop advanced source-tracing and truth-evaluation habits.

🟦 Interdisciplinary Case Study Work
Students should solve problems that span disciplines, using AI as an aid—not a crutch—while practicing integration and judgment.

Quadrant III: De-emphasized Learning
Low Human Urgency, Low AI Proximity
These are legacy components of the ascent that no longer justify their time and cognitive load in an AI-accelerated world. They were once useful, but their prominence now diverts energy from more meaningful and durable learning.

Urgently De-emphasize:

🟨 Manual Long Division and Multi-Digit Arithmetic
Fluency matters—but deep hours spent on algorithmic computation are better used on conceptual understanding and estimation skills.

🟨 Obsolete Software Instruction
Teaching Microsoft Office menus or legacy desktop tools offers little long-term value in a cloud-based, AI-augmented ecosystem.

🟨 Isolated Fact Memorization (State Capitals, Taxonomies)
AI recalls facts instantly. Humans should focus on using facts to ask better questions and solve real problems.

🟨 Diagram Labeling for Its Own Sake
Labeling diagrams without applying the knowledge (e.g., cell parts, water cycle) should give way to dynamic, systems-based modeling.

🟨 Excessive Standardized Test Drills
Preparing for tests rather than for life misorients the learner and undermines long-term motivation and creativity.

🟨 Lecture-Based Note Transcription
Copying down content verbatim in a classroom of passive listeners is less effective than dialogical, participatory formats.

🟨 Surface-Level History Timelines
Teaching “what happened when” without understanding why it matters trains students to recall, not to reason.

🟨 Formula-Only Science Labs
Hands-on labs should foster curiosity, not just replicate known results. Rote labs waste precious opportunities for wonder.

🟨 Keyboarding as a Primary Skill
Typing speed will matter less in an age of multimodal input and voice-AI integration. Expression, not mechanics, should be the focus.

🟨 Overweighting Letter Grades
Grades offer feedback, but obsessing over GPA distorts motivation and learning. Better to emphasize growth, feedback, and iteration.

Quadrant IV: AI-Native Tasks
Low Human Urgency, High AI Proximity
This quadrant includes tasks AI already performs better and faster than most students ever will. Teaching students to master these tasks is a poor use of time. What matters is awareness, ethical usage, and the ability to move up the value chain.

Urgently Offload or Replace:

🟥 Summarization of Long Texts
AI can do this instantly and well. Teach students instead how to evaluate and refine AI summaries for accuracy and nuance.

🟥 Spelling and Grammar Correction
Students should write clearly—but learning to manually correct every comma misses the point. Focus on clarity, argument, and tone.

🟥 Formulaic Essay Structures
The five-paragraph essay should give way to more authentic, voice-driven forms of expression and argumentation.

🟥 Vocabulary Lists for Memorization
Learning words in isolation is outdated. Focus on reading, contextual usage, and expressive range.

🟥 Image Tagging and Metadata Labeling
Tasks once used in computer class can now be offloaded entirely. Better to use visual media for analysis, critique, or creation.

🟥 Automated Calculations and Conversions
Teaching students how to convert grams to ounces by hand may be interesting—but not at the cost of deeper quantitative literacy.

🟥 Repetitive Homework for Practice
AI-generated solutions make repetitive worksheets obsolete. Emphasize application, variation, and strategic thinking instead.

🟥 Basic Search and Retrieval Tasks
Locating a date, a definition, or a summary is no longer an educational milestone. The new skill is sensemaking, not locating.

🟥 Captioning and Simple Content Creation
Social media-style captions, slide decks, and templates can now be auto-generated. Focus on ideas and narrative structure instead.

🟥 Low-Level Language Translation
Basic translation is handled well by machines. Human learners should focus on nuance, idiom, intent, and cultural context when learning a second language.