Shifting AI Readiness Benchmarks from In-School Use to Thriving in an AI World
A brief proposal for three new benchmarks
TLDR
Instead of counting AI usage, we must benchmark (1) whether students and educators understand the emerging world; (2) whether they’ve developed the collaborative and metacognitive skills needed to thrive in it; and (3) whether K-12 schools reconceptualized as educational communities support life-long learning.
A lot of ink is spilled about how to use AI *in* education. How can we use it to generate lesson plans and quizzes, grade papers, help students build projects, etc. ?
And there is a lot of, “I told you so. Teachers aren’t using this.” Those who make this point argue we don’t need AI in education, and we should stop trying to push it in. They imagine that education can just ignore the AI world around them
But measuring “AI in school” against these benchmarks — how/how much is AI being used by teachers in the current educational system, and then a related benchmark: How many teachers have been given two hours of training to use AI to generate a lesson plan and related materials. These benchmarks miss the mark.
The important question is how are we going to prepare students for a world where computer intelligence is close to, meets, and exceeds (these will vary by task) human intelligence? If we don’t answer this question of how we need to change to help students thrive in this rapidly changing world, how we use AI in lesson planning and student assignments is not even relevant.
And if we think AI isn’t changing “education” because it isn’t changing what is happening inside brick & mortar school buildings, we are very mistaken.
Students are learning to use AIs on their own; they are learning about all types of “real-world/relevant to them” topics on TikTok, Twitter, and other social media forums. Those with means are hiring tutors to teach about AI and entrepreneurship.
AI is also supporting the rise of non-traditional schooling. Home-schooling and micro-schooling are growing, at least across the US. Formats such as “2-hour school” are rapidly spreading.
AI is changing education from the oustide, and the longer traditional education waits to change, the more these alternatives will grow among those with the resources to support them. Parents care about whether schools are preparing students to thrive in an AI world; they don’t care about whether students are meeting traditional benchmarks that will leave them behind.
In this post, I propose three new conceptual benchmarks.
Are we preparing students and educators to understand what is coming in a world of AI and other advanced technologies? This would measure how well education systems are building a useful understanding of AI with other emerging technologies like quantum computing and synthetic biology. Rather than treating these as separate subjects, the benchmark would evaluate whether students understand the convergent nature of these technologies and their combined potential impact on fields ranging from medicine to environmental science. It would measure institutional agility in updating curricula, retraining educators, and maintaining relevance in a rapidly changing technological landscape. The focus would be on building systems that can continuously adapt rather than simply responding to change after it occurs.
Are we prioritizing the skills and abilities students need to thrive in this world?
This benchmark goes far beyond digital literacy or tool proficiency. Rather than competing with AI, students need to develop skills that complement artificial intelligence. This includes complex problem-solving that requires contextual understanding, ethical reasoning in ambiguous situations, and the ability to ask the right questions when AI provides answers. Students need to develop meta-cognitive skills—thinking about their thinking—to know when human intuition and judgment are irreplaceable.
Are we supporting lifelong learning in our communities? This benchmark evaluates whether communities have built accessible learning ecosystems that extend far beyond traditional schools, recognizing that we can no longer assume graduating high school—or even college—prepares someone for a world that changes wholesale every few years or more. It examines community-wide infrastructure like libraries as learning hubs, flexible community college programs, and maker spaces, while assessing how well communities facilitate intergenerational knowledge exchange where older adults learn digital skills while sharing experiential wisdom.
The benchmark critically evaluates workforce adaptation systems—whether local reskilling programs respond quickly to AI-driven job market changes and whether employers invest in employee development rather than replacement. Beyond infrastructure, it measures whether communities cultivate a learning culture that celebrates continuous growth across age groups and provides social support for risk-taking and experimentation. Most importantly, it examines equity and accessibility, ensuring that lifelong learning opportunities reach all community members, including those facing systemic barriers related to disabilities, language, work schedules, or caregiving responsibilities, recognizing that in an AI-transformed world where technological disruption accelerates, the ability to continuously learn and adapt becomes a fundamental community resource that must be actively maintained throughout life.
Fundamental Shift in Educational Benchmarking
The current obsession with measuring AI adoption rates in classrooms—tracking how many students use ChatGPT or how often teachers integrate AI tools—misses the deeper transformation required for educational success in an AI-dominated world.
These surface-level metrics tell us nothing about whether students are developing the critical thinking skills to evaluate AI outputs, the emotional intelligence that remains uniquely human, or the adaptability to navigate careers that will be repeatedly disrupted by technological change.
Instead of counting AI usage, we must benchmark whether students understand when human judgment trumps algorithmic recommendations, whether educators are preparing learners for jobs that don't yet exist, and whether communities are building the lifelong learning infrastructure necessary when a high school diploma can't possibly prepare someone for four decades of accelerating change.
The measure of educational success in the AI era isn't how seamlessly we integrate these tools into existing systems, but how fundamentally we transform our approach to learning, thinking, and human development. We need benchmarks that evaluate our readiness for a future where the only constant is change —not our proficiency with today's version of artificial intelligence.
Additional Food for Thought
Human-Centered Learning Benchmarks
What percentage of students can articulate their unique value proposition alongside AI capabilities?
How many learners demonstrate improved collaborative problem-solving when working with AI tools?
Are students developing stronger human-to--human empathy and emotional intelligence skills as AI handles routine tasks?
Adaptive Teaching Benchmarks:
How many educators are actively redesigning curricula to emphasize creativity, critical thinking, and human judgment?
What percentage of teachers feel confident facilitating AI-human collaboration rather than just using AI tools?
Are pedagogical approaches shifting from information delivery to wisdom cultivation?
Community Learning Ecosystem Benchmarks
How many intergenerational learning partnerships exist between schools and community members?
What percentage of educational institutions serve as lifelong learning hubs beyond traditional student populations?
Are local businesses, libraries, and community centers integrated into continuous learning networks?
Systemic Transformation Benchmarks
How many educational leaders are proactively experimenting with AI-integrated learning models?
What percentage of professional development focuses on human-AI collaboration rather than just AI literacy?
Are assessment methods evolving to measure metacognitive skills, adaptability, and creative problem-solving?
Equity and Access Benchmarks
How many underserved communities have equal access to both AI tools and human mentorship?
Are we measuring whether AI is reducing or amplifying educational inequalities?
What percentage of students from all backgrounds feel empowered to shape AI's role in their learning?