How to Transform Academic Institutions

🎓 Bullshit Universities: The Future of Automated Education This sharp and provocative essay by Sparrow and Flenady challenges the utopian narratives surrounding AI in higher education. The authors argue that AI outputs—lacking truth, meaning, and moral accountability—are unfit for replacing human teaching. While automation promises efficiency and access, it risks hollowing out the essence of education: learning by example, dialogue, and critical inquiry. To defend education’s social and transformative role, universities must reinvest in people, not platforms. ⚖️ 5 Key Trends, Trade-offs, and Contradictions: 1. 🚀 EdTech Hype vs. Pedagogical Reality History shows that "assistance" is often the first step toward labor displacement. Once AI designs lessons and grades essays, the rationale for keeping educators weakens. The tech utopia may actually be a cost-cutting dystopia. 2. 📦 Content Delivery vs. Human Formation AI excels at packaging and distributing content, but real education involves identity, ethics, and intellectual rigor. Teachers inspire, challenge, and mentor—not just instruct. 3. 🌍 Access vs. Quality AI can extend access to learning, especially in underserved areas—but what kind of learning? If AI replaces meaningful teacher interaction, we risk offering a second-class education to marginalized groups. 4. 🤖 Automation Bias Once AI systems become routine, users tend to trust them too much—even when they’re wrong. Teachers may stop reading student work critically, while still being held responsible for errors. Over-reliance on machines erodes professional judgment. 5. 🧠 Learning that vs. Learning how Knowing facts (“that”) is not enough—students must develop skills and judgment (“how”). Writing, critical thinking, and discussion require human modeling and feedback. 🛠️ 5 Policy Recommendations 1. 🧑🏫 Reinvest in Human Teachers: Fund smaller classes with passionate, expert human teachers. Teachers are not content deliverers—they are mentors, models, and guides. Smaller classes mean more dialogue, personalized feedback, and intellectual engagement. 2. 🧰 Use AI Only in Dedicated Skills Units: Let students learn how to use AI tools responsibly—just like learning to use a library or a bibliography. But don’t let AI replace disciplinary teaching or feedback. 3. 📋 Protect Assessment Integrity: Avoid AI-based grading; protect integrity through human assessment. AI lacks the judgment, context, and accountability that grading demands. 4. 🔁 Prioritize Human Mentorship and Feedback: Mentorship builds trust, motivation, and deep thinking. 5. 🎓 Resist the Temptation to Mass-Produce Education: Incentivize deep learning, not scalable content delivery platforms. https://lnkd.in/eE9Vvni3

There's something deeply satisfying about watching educational institutions slowly recognize that their most pressing technological crisis might actually be a pedagogical opportunity in disguise—though it has taken the arrival of generative AI to force this particular moment of clarity. For months, I've been observing students navigate what can only be described as a false binary: either resist AI entirely as academic contamination, or embrace it uncritically as the solution to all educational challenges. Both approaches miss the point entirely. What students actually need—and what our educational frameworks have been remarkably slow to provide—is what I've come to call "possibility literacy": a way of engaging thoughtfully with AI's paradoxical nature without surrendering their intellectual agency to algorithmic convenience. Harvard Business Publishing has just released my piece on this approach, which explores three essential skills I've been cultivating in my classrooms: Pattern recognition - teaching students to become "algorithmic archaeologists" who can uncover the invisible biases shaping AI outputs Directed divergence - learning to push AI systems beyond their conventional patterns through strategic constraints Reflective synthesis - developing the critical judgment to know when AI enhances versus shortcuts their thinking The most gratifying moment came during a recent final presentation, when a student who had initially avoided AI entirely explained how she had systematically documented patterns across multiple AI systems analyzing dystopian fiction. She had moved from fearful resistance to what she called "a conscious relationship with algorithmic tools." In that moment, I saw the transformation our education systems must nurture—one that centers student agency rather than technological efficiency. The article includes specific assignments for creating "possibility-rich learning environments" where students learn to navigate AI's productive paradoxes rather than resolve them into neat categories. Because the real question isn't whether AI will reshape education, but whether we'll use this moment to finally prioritize the intellectual capacities that make humans irreplaceable. Link in comments. #AIEducation #PossibilityLiteracy #CriticalThinking #HumanCapacity Mike Kentz Amanda Bickerstaff Alfonso Mendoza Jr., M.Ed. Aco Momcilovic Dr. Lance Cummings Lance Eaton, PhD France Q. Hoang Pat Yongpradit Vriti Saraf Claire Zau Michel Faliski David H.

Excited to share my latest article, “Reimagining Higher Education: AI is the Catalyst We Can't Ignore,” now live in the ASU Learning Enterprise newsroom. https://lnkd.in/gfCcbEin As AI reshapes every corner of our world, it’s time for higher ed to get back to first principles—rethinking what learning could and should be instead of simply retrofitting technology into traditional models. AI challenges us to ask: How can we transform education to meet the demands of an AI-driven future? Here’s where we might start: ♦ How can universities keep up when technology is evolving 10x faster? Gradual change isn’t an option. ASU is embracing this shift with enterprise-level AI partnerships to lead in personalized education and future-ready learning. ♦ Self-driving cars: why are students still in the back seat of learning? Like self-driving cars that still have back seats out of habit, universities are adding AI within old models, keeping students as passengers. What if AI enabled students to take the wheel, actively steering their own learning journey? ♦ Neural networks: connecting ideas across fields to drive insights – AI is breaking down traditional academic silos, enabling cross-disciplinary learning that can drive innovative solutions for complex challenges. ♦ Generative AI: an invitation to greater human creativity – More than a tool, generative AI is a creative partner that sparks new ideas, empowering students to explore, experiment, and push boundaries. ♦ Moving beyond physical campuses: redefining the boundaries of learning – AI and tools like digital twins mean that learning is no longer confined by geography. From virtual labs to immersive environments, students everywhere can access hands-on, meaningful experiences. I’d love to hear your thoughts. What role do you see for AI in transforming education? And what would you add to the conversation? Drop me a comment!