How AI Is Making Us Rethink What Counts as Learning

After a week of talking about AI and assessment, one truth keeps surfacing: the purpose of education isn’t to outsmart machines; it’s to empower humans.

Generative AI has forced us to hold up a mirror to our system and ask difficult questions. For decades, we’ve measured learning by how well students could recall, summarize, and perform under pressure. But in 2025, recall is cheap and automation is everywhere. If a chatbot can ace your test, maybe the test was never measuring what truly mattered.

The emergence of AI doesn’t make human learning obsolete; it reveals what’s essentially human about it.

 Critical thinking. Creativity. Empathy. The ability to connect ideas across disciplines, to collaborate, and to make meaning in uncertain contexts.

These are not easily quantified by standardized rubrics, but they are exactly what the future demands.

I’ve seen incredible progress from educators who are embracing AI as a partner in pedagogy rather than an adversary. They’re asking students to explain their reasoning, to annotate their process, to use AI transparently and ethically. They’re turning assessment into dialogue — between learner, teacher, and technology.

This is where the real transformation happens.

 AI isn’t changing what we value in education. It’s revealing whether our assessments ever aligned with those values in the first place.

If we can design assessments that honor curiosity, reflection, and mastery, then AI won’t undermine education, it will elevate it.

The next evolution of assessment isn’t about control. It’s about trust.

 Poll on LinkedIn

Building a Culture of Mastery over Memorization

The most powerful shift AI can inspire isn’t technological; it’s philosophical. It challenges us to move from grading performance to nurturing mastery.

For too long, education has rewarded correctness over growth. We’ve built grading systems that favor perfection on the first try instead of perseverance through feedback. But AI gives us a rare opportunity to rethink what mastery looks like.

Imagine a classroom where students use AI to explore, revise, and reflect. Producing multiple iterations of a project until they achieve genuine understanding. In this model, teachers aren’t gatekeepers; they’re mentors guiding the process. AI provides instant feedback; the educator provides context, wisdom, and direction.

Mastery learning reframes assessment as part of learning, not the end of it. It emphasizes competence, creativity, and confidence, qualities that matter far more than any single test score.

AI won’t make teachers obsolete. It will make traditional grading obsolete. And that’s a change worth embracing.

Ask yourself: Does my grading system reward improvement or perfection?

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AI-Resistant vs. AI-Resilient Assignments

There’s a growing movement to make assignments “AI-proof.” Unfortunately, that approach often leads us backward, toward surveillance, suspicion, and outdated methods. Instead, educators should aim for AI-resilient assessments: tasks that remain meaningful even when AI is part of the process.

AI-resistant assessments try to lock the doors.  AI-resilient assessments open new ones.

Here’s what that looks like in practice:

•  Require iterative drafts and reflections — ask students to explain how their ideas evolved.
  
  
•  Encourage use of AI but demand transparency: What did you prompt it to do, and how did you verify its accuracy?
  
  
•  Assess the process as much as the product.
  
  
•  Shift from recall tasks to reasoning tasks.

Resilience means acknowledging that AI is here to stay, and teaching students to use it responsibly. It’s not about catching dishonesty; it’s about cultivating intellectual integrity.

We want learners who can work with AI without losing their own voice. That’s not resistance, that’s resilience.

Ask yourself: If AI disappeared tomorrow, would this task still matter?

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Authentic Learning and Real-World Tasks

When students use AI, they’re mirroring what professionals already do: integrating technology as a partner in problem-solving. Instead of banning those tools, we should leverage them to design authentic assessments that connect classroom learning to the real world.

A business major shouldn’t just define “competitive advantage.” They should analyze a real company, use AI to summarize market data, and then critique those AI findings with their own insights.

 A nursing student might use AI to review patient-care case studies, but must identify ethical gaps or missing human context.

In each case, AI is not the shortcut; it’s the starting point.

Authentic tasks are inherently anti-cheating because they are context-rich and personal. They demand critical thinking, reflection, and communication skills that AI alone cannot simulate. They ask learners to show their reasoning, not just their results.

When we design learning this way, we teach transparency, ethical use, and digital fluency; the hallmarks of the modern professional. The result isn’t just better assessments; it’s better thinkers.

Ask yourself: Would this assignment make sense outside of school?

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The Problem with Traditional Assessment

 Generative AI has exposed what many educators already suspected, much of our assessment system is built on the wrong foundation.

For over a century, we’ve measured learning by how well students can recall information on command. Essays, quizzes, and timed exams all reward quick recall and compliance. But in an age when ChatGPT can produce grammatically perfect essays and summarize any topic in seconds, these measures no longer tell us what students actually understand.

This isn’t a crisis. It’s a wake-up call.

AI forces us to confront the difference between knowledge reproduction and knowledge creation. Memorization and formulaic writing can be automated, but curiosity, synthesis, ethical reasoning, and original thought cannot. If our goal is to prepare learners for a future where AI is ubiquitous, we must focus on assessing what remains uniquely human, judgment, creativity, empathy, and discernment.

Rather than designing tests to catch cheaters, we can design experiences that make cheating meaningless. Assessments should ask: Can the learner evaluate, apply, and extend knowledge in new contexts?

We’re not losing control of learning; we’re rediscovering its purpose.

Ask yourself: Does this assessment measure memory or meaning?

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Focus and Future: The European Approach to Early Career Direction

In many European education systems, students are encouraged, or even required, to choose a career pathway earlier than their American peers. At first glance, that can seem restrictive. But look closer, and it’s often empowering.

Through apprenticeships, vocational schools, and academic “tracks,” students explore real-world applications of their interests as early as age 15 or 16. By the time they reach university, they already possess both clarity and competency.

This structure doesn’t just serve engineers or tradespeople; it extends to healthcare, education, and the arts. The goal is to align learning with purpose, rather than just checking boxes toward a generic diploma.

In the U.S., we often delay career exploration until late college, or worse, after graduation. We prize openness, but sometimes that openness leads to drift. Imagine if our high schools and community colleges integrated genuine career exploration earlier, through mentorships, internships, and guided reflection.

Europe’s model reminds us that direction can be liberating, not limiting. When students see a tangible link between learning and livelihood, motivation soars.

Question:

 How can American schools introduce purposeful pathways earlier, without narrowing opportunity too soon?

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Affordability and Access: The European Lesson in Higher Education

Across Europe, higher education is viewed as a public good, not a private commodity.

 Tuition is free or minimal in countries like Germany, France, and the Nordic nations. Even where fees exist, they’re often a fraction of what American students face. As a result, debt doesn’t define adulthood, opportunity does.

When education is affordable, students make choices based on passion and aptitude, not on financial survival. They can take intellectual risks, pursue the arts, or study fields that contribute to society without being crushed by loans. In many ways, that freedom fuels innovation rather than stifling it.

The U.S. once embraced a similar ideal, public universities built to democratize learning, but decades of disinvestment shifted the burden to students and families. Today, the average American graduate leaves college with over $30,000 in debt. That’s not a pathway to a stronger workforce; it’s an economic trap.

What Europe demonstrates is that affordability and excellence are not opposites. Public funding and accountability can coexist with world-class universities.

If we want to stay competitive globally, we must reimagine higher education as infrastructure, an investment in the nation’s intellectual capacity, not a luxury for those who can afford it.

Question:

 What would it take for America to treat education as essential infrastructure again?

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Specialization and Speed: Why European Degrees Get Students Working Sooner

In many European universities, a bachelor’s degree takes three years, not four.

 That’s not just about efficiency, it’s about focus. Students select a discipline early and pursue it in depth, often bypassing the broad general education requirements standard in the U.S.

By the time an American student finishes a freshman composition course, a European counterpart may already be conducting specialized research in their chosen field.

The European model assumes that secondary education provides the foundational literacy, numeracy, and cultural knowledge we often leave to college. University is about developing professional and intellectual mastery, not exploration.

The upside is that graduates enter the workforce sooner, often with less debt and more specialized expertise. The trade-off? Less flexibility to change direction mid-stream.

The U.S. system prizes breadth and adaptability, which has real value, but perhaps we’ve swung too far. Many students spend thousands of dollars “discovering” a major they could have explored through structured advising, internships, or gap-year experiences.

If we blended the best of both systems, we might front-load exploration before university and streamline specialization within it.

Question:

 Could earlier career guidance and flexible pathways help students find purpose sooner while still preserving choice?

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Rethinking Priorities: What Europe Gets Right About Academic Focus

In much of Europe, school is, first and foremost, about learning.

 That sounds obvious, but the cultural priorities are strikingly different from those in the U.S. European schools emphasize academic mastery and independent study over extracurricular activities. Sports and clubs exist, but they’re not the centerpiece of school identity; the emphasis is on intellectual growth, not athletic rivalries.

Students are expected to read widely, analyze deeply, and engage in sustained inquiry. Independent study isn’t an afterthought, it’s a skill deliberately taught and assessed. The result? Students often leave secondary education with stronger study habits, higher reading comprehension, and greater resilience when faced with complex problems.

In the U.S., by contrast, extracurriculars are woven into the social and financial fabric of schools. Football games fill stadiums, not libraries. The energy and resources poured into non-academic programs sometimes eclipse the academic mission itself.

The lesson isn’t to abandon extracurriculars, but to rebalance. We can learn from Europe’s focus on depth, which prioritizes reading, writing, and research skills, extends project-based learning, and fosters a culture that celebrates academic excellence alongside athletic achievement.

We talk about producing “lifelong learners.” Perhaps it starts with creating environments that reward learning itself.

Question:

 How might your school or institution elevate academic focus without losing the sense of community that extracurriculars provide?

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Learning from Europe: A Week of Reflection

Next week, I’m exploring what European education systems do better—and what the U.S. can learn from them.

While no system is perfect, European models often deliver strong academics, focused study, affordable access, and clear career pathways. Each day this week, I’ll share one key lesson—along with ideas for how we can adapt those strengths to our own classrooms, campuses, and training programs.

•  Monday: Academic Focus – depth over distractions
  
  
•  Tuesday: Specialization – shorter, sharper degrees
  
  
•  Wednesday: Affordability – education as a public good
  
  
•  Thursday: Early Focus – purposeful pathways for students

Join the conversation, share your experiences, and let’s imagine what a more balanced and equitable American education system could look like.

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AI + Language Learning: Where Do We Go from Here?

This week, I’ve explored how AI is reshaping language learning across K-12, higher education, and corporate training.

Each space has shown the same pattern: enormous potential, matched by equally important cautions.

•  In K-12, AI tools can personalize practice and feedback, but only when guided by teachers who preserve curiosity and play.
  
  
•  In Higher Ed, AI expands access to conversation and writing support, but professors must safeguard authenticity and assessment integrity.
  
  
•  In Corporate Learning, AI scales multilingual communication, but leadership must ensure cultural understanding doesn’t get lost in translation.

Across all three, one truth remains: AI doesn’t replace the teacher, it amplifies them.

 It turns repetition into readiness, feedback into fluency, and data into direction, but only if we design learning experiences that keep the human at the center.

What research says

Recent reviews in Language Learning & Technology and the British Journal of Educational Technology highlight four pillars of effective AI use in language learning:

•  Guided autonomy – learners benefit most when AI offers independence with oversight.
  
  
•  Cultural framing – AI outputs need contextual explanation to prevent shallow learning.
  
  
•  Ethical literacy – students must understand where AI helps and where it hallucinates.
  
  
•  Iterative reflection – educators should treat AI interactions as data for continuous improvement, not as finished products.

Looking ahead

As models like ChatGPT continue to evolve, the line between “conversation partner” and “learning environment” will blur. Our challenge, and opportunity, is to make that boundary a pedagogical feature, not a flaw.

Poll posted to LinkedIn

AI + Language Learning, Corporate Training: From Compliance to Culture

Or Tales of a Marginally Successful Polyglot

A few years ago, I had a huge writing project that prompted me to learn some Irish Gaelic. I loved the language, but it was so complex. Since I was learning it on my own long before the days of AI tools, I am sure that there were fundamentals I missed out on, and I know for a fact there are many words I do not pronounce correctly at all.  For that project, it was all about me connecting with my audience.  

In corporate and professional education, language learning is less about grammar charts and more about connection: closing deals, supporting customers, or building inclusive teams.

AI is helping organizations make that happen at scale:

• Just-in-time translation and subtitling tools reduce friction in global collaboration.
  
  
• Custom chat tutors can model company-specific terminology or customer scenarios.
  
  
• Adaptive microlearning delivers five-minute refreshers before client meetings.

Apple Education (1) highlights how multilingual AI assistants can “extend access to global opportunities,” and recent research in Language Learning & Technology shows AI chatbots boosting pronunciation accuracy for adult learners. Yet both sources stress the same caveat: human context still matters. Tone, empathy, and cultural nuance remain stubbornly human domains.

For learning leaders, the opportunity lies in integration, not automation. Pair AI modules with live coaching, peer conversation, or intercultural workshops. Use data from AI interactions to identify skill gaps, then design targeted follow-ups.

Corporate education succeeds when technology disappears behind human improvement. The goal isn’t fluency in a language; it’s fluency in understanding one another.

How are you, or your organization, using AI to support multilingual collaboration?

(1) Apple Education. (2024). AI in education: Enhancing language learning through intelligent technology. Apple Inc https://education.apple.com/story/250014607

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AI + Language Learning, Higher Ed: The New Language Learning

When I was at University, I picked German back up, but it was not as easy as I had hoped. Later on in grad school, I tried Japanese. I can still count to 11, and that is about it. Even then, I knew there had to be a better way.

Universities are discovering that AI can extend immersion beyond scheduled class hours:

• 24/7 conversational practice. Students can simulate dialogue with a virtual partner or rehearse interviews in the target language.
  
  
• Context-aware feedback. AI tutors can explain grammar in English, then switch seamlessly into Spanish or Mandarin to model tone and phrasing.
  
  
• Adaptive writing assistance. Generative models can suggest alternate word choices, idioms, or cultural nuance,  if guided responsibly.

A 2024 British Journal of Educational Technology (1) study found that AI-assisted writing tasks increased fluency and learner confidence, but also risked “over-automation”,  students editing less critically when the machine appeared authoritative.

The Princeton Review (2) notes the same tension: AI lowers the barrier to practice but raises questions about authenticity,  how much of a student’s progress is truly their own voice?

That’s where instructional design becomes crucial. We can use AI as a reflection tool rather than a replacement: have students critique an AI’s translation, or compare outputs from multiple systems to deepen cultural understanding.

AI isn’t replacing language professors; it’s expanding their toolkit. The new language lab is wherever learning happens, as long as educators anchor it in feedback, reflection, and ethics.

How are your institutions addressing AI use in language courses or assessment policies?

(1) British Educational Research Association (BERA). (2024). The impact of artificial intelligence on language learning and teaching. British Journal of Educational Technology, 55(3).

(2) Princeton Review. (2024). Language learning with AI: How artificial intelligence is changing education. The Princeton Review.

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AI + Language Learning, K-12: Support, Not Substitution

In high school, I took four years of German. I loved it, I loved learning the culture, the language, and being able to converse with others. Though as the years have gone by, my grasp of the language has weakened. 

Then language labs were rooms full of headsets and cassette decks. Today, the “lab” can fit in a pocket.

In K-12 classrooms, language learning has always thrived on interaction ,  songs, stories, role-play, peer conversation. But as AI tools like ChatGPT, Duolingo Max, or Microsoft Copilot enter the classroom, a key question emerges: Can they help without replacing that human spark?

AI can be a powerful supporting tool for teachers:

•  Personalized feedback. Students can practice vocabulary or pronunciation with an AI “conversation partner” that never tires or judges.
  
  
•  Scaffolded differentiation. AI can rephrase directions, translate complex text, or provide simpler explanations, helping multilingual learners stay engaged.
  
  
•  Instant formative assessment. Teachers can prompt AI to generate reading-comprehension questions or quick grammar drills aligned to state standards.

According to research in Language Learning & Technology (2024) (1), students who used AI chat systems for vocabulary practice reported higher motivation and retention ,  but success depended heavily on teacher guidance and oversight. Without it, accuracy slipped and misconceptions spread quickly.

The Sanako report (2), Will AI Make Language Learning Obsolete?, makes the same point: tools are only as good as their integration. The best results appear when educators combine AI’s adaptability with the empathy and structure that only teachers provide.

In short, AI can make language learning more accessible, but it can’t make it automatic. Young learners need curiosity, play, and patience ,  things no algorithm can replicate.

If you teach in K-12:

 How are you balancing AI’s efficiency with authentic communication?

 Has it freed time for deeper learning, or added new layers of management?

(1) Wang, H., & Kim, J. (2024). Exploring learner engagement with AI-based tools in second language acquisition. Language Learning & Technology, 28(2), 26–45.

(2) Sanako. (2023, October 5). Will AI make language learning obsolete? Sanako Blog.

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AI + Language Learning, Introduction: My Experience and Why Educators Should Pay Attention

I’ve been using ChatGPT as my new learning companion, mostly notably for Python, but also occasionally for Spanish. When I'm stuck or want to try a different explanation, I’ll prompt the model: “Explain this Python function as if I were 12,” or “In Spanish, how would you say …?” The responses help me solidify concepts (and occasionally catch mistakes). It’s not perfect, but it’s a flexible, on-demand thinking partner. I can also ask why something is this way. For example, what are PANDAS in Python, or why is a baguette “el baguette” in Latin America but “la barra de pan” in Spain?

My hope this week is to explore how tools like ChatGPT (and other AI platforms) are entering the language-learning space, and to spark a thoughtful conversation among educators about how we can harness them responsibly, creatively, and ethically.

Why this matters now

• Rapid advances in generative models have brought language capabilities to AI that feel more conversational, context-aware, and reliable than ever.
  
  
• Demand for scaling language education is rising, schools, universities, and organizations want more affordable, personalized support.
  
  
• Tension and opportunity coexist: there are valid fears (overreliance, accuracy, equity, ethics) but also real potential for scaffolding, feedback, and motivation.

Key themes I plan to explore this week

• Language Learning in K-12,  How might AI assist younger learners, and what concerns do teachers face (e.g. accuracy, scaffolding, digital divide)?
• Language Learning in Higher Ed,  In universities, language departments, or general ed courses: how AI can complement language labs, peer conversation, writing support, or assessment.
• Language Learning in Corporate/Training Settings,  Many organizations now train employees on industry-specific languages or cross-cultural communication; can AI help scale that?

Challenges, Ethical Boundaries, and Future Directions ,  Across all levels, how do we maintain quality, guard against misuse, and stay pedagogically grounded?

Some caveats from research & conversations

Studies in English Language Teaching suggest AI can support reading, writing, speaking, and self-regulation, but frequent challenges include technical breakdowns, over standardization, and trust in the model’s output.

A systematic review of generative AI in language learning (2023–24) shows increasing experiments, but also caution about novelty, methodological rigor, and replicability. 

Invitation to educators

Over the next few days, I’ll share concrete ideas and provocation for K-12, higher ed, and corporate training contexts. I’d love for you (especially educators) to comment, share your experiences using AI in language learning (or resisting it), and pose challenges or questions I should address.

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