Key Challenges in Education Technology

Today Common Sense Media released their new white paper on "Generative AI in K–12 Education: Challenges and Opportunities." It takes a deep dive into the complexities of AI adoption in education and I was fortunate to share some of our experiences from AI for Education's work in schools and districts with one of the authors, Bene Cipolla . The white paper is definitely worth a read and we love the emphasis on responsible implementation, the importance of building AI literacy, and the need for clear guidelines to ensure AI enhances rather than undermines learning experiences. Key Highlights: Current State of AI in Education: • Though familiarity is increasing, there is still a lack of fundamental AI literacy • Only 5% of districts have specific generative AI policies, which reflects what we have seen in the field • Students are using AI tools, often without clear guidelines Opportunities for AI adoption: •  Student-focused: Adaptive learning, creativity enhancement, project-based learning, and collaborative support •  Teacher-focused: Lesson planning assistance, feedback on teaching, and productivity gains •  System-focused: Data interoperability, parent engagement, and communication Risks and Challenges: •  Inaccuracies and misinformation in GenAI outputs •  Bias and lack of representation in AI systems •  Privacy and data security concerns •  Potential for cheating and plagiarism •  Risk of overreliance on technology and loss of critical thinking skills What Students Want: •  Clear guidelines on AI use, not outright bans •  Recognition of both potential benefits and ethical concerns of the technology •  More education on AI's capabilities and limitations Recommendations: •  Invest in AI literacy for educators, students, and families •  Develop standardized guidelines for AI use in schools •  Adopt procurement standards for AI tools in education •  Use participatory design to include diverse voices in AI development •  Center equity in AI development and implementation •  Proceed cautiously given the experimental nature of the technology Make sure to check out the full report and let us know what you think - link in the comments! And shoutout to all of our EDSAFE AI Alliance and TeachAI steering committee members featured in the white paper. #aieducation #GenAI #ailiteracy #responsibleAI

A key challenge I'm seeing in K-12 schools: the rush to adopt AI tools is creating an equity crossroads. The pressure to "do something with AI" is intense, but how we implement these tools today will shape educational equity for years to come. K-12 leaders are facing a critical tension. Wait too long to adopt AI tools, and you risk leaving teachers and students behind in the AI revolution. Move too quickly without systematic implementation, and you risk embedding inequities that could take years to unravel. Here's the current landscape: Individual teachers sign up for free tiers of educational AI platforms Districts consider institutional licenses for system-wide implementation Most schools end up with a mix of both, creating uneven implementation Individual teacher signups (free tiers of MagicSchool, Khanmigo) offer: Teachers can start using AI tools immediately No budget approval needed for basic features Limited functionality compared to institutional licenses No way to track which student populations are using (or avoiding) the tools Students' access varies based on which teachers adopt them District-wide implementations (institutional licenses) provide: Systematic tracking of usage and outcomes Built-in FERPA compliance and safety features Consistent experience across classrooms Significant budget impact Long procurement cycles that slow innovation Why this matters for long-term equity: Data tracking: Without systematic data collection, schools can't see which student populations are actually benefiting from AI tools and which aren't Teacher support: Individual adoption creates pockets of AI expertise rather than systematic capability Achievement gaps: When AI implementation is random, so are students' opportunities Resource allocation: Usage data is crucial for targeting future investments where needed most At the Ohio Education Technology Conference next week, I'll share our complete decision framework, but start with this question: Are you choosing tools based on immediate availability, or building for long-term equity? #K12Education #EdTech #EducationalEquity Amanda Bickerstaff Daniel Kosta Mike Kentz Alfonso Mendoza Jr., M.Ed. David H. Andy Lucchesi Nigel P. Daly, PhD 戴 禮 Joel Backon Sabrina Ramonov 🍄Saleem Raja Haja Phillip Alcock

Silicon Valley ≠ Education Valley. Move fast and break things doesn't work when 'things' are student futures. People often ask why education technology moves "slowly." The better question... Why should changing education move at the speed of software updates? Silicon Valley operates in cycles of months. Launch, iterate, pivot. A failed app means lost investment dollars. Education operates in cycles of years. A curriculum change affects an entire generation. A failed implementation means lost learning - time students can never get back. There’s a lot at stake for even small changes in education. When Facebook tests a new algorithm, they measure engagement drops. When we test new edtech, we measure comprehension gaps, learning outcomes, and student confidence... ...things that compound over a lifetime. The "move fast" mindset assumes: → Quick iterations improve the product → Early adopters provide useful feedback → Bugs can be fixed in the next update But in education: → Quick iterations can interrupt learning sequences → Students aren't beta testers → "Bugs" might mean a student learns incorrect concepts To be fair, we know we're at the precipice of radical opportunity — and doing nothing clearly isn't an option. This isn't a call to move slowly. It's a call to move deliberately.

How Should Education Evolve in the AI Era? Last night at Northwestern University’s SF campus, we had a fantastic opportunity to reconnect with MSL alumni and discuss the evolving challenges of education in the AI age. Huge thanks to Evan Goldberg and Leslie Oster for organizing, and to Professors Daniel B. Rodriguez and Emerson Tiller for their insightful discussion. 🔍 Key Challenges in Education: 📌 The Expertise Gap – If AI takes over routine tasks traditionally handled by juniors, how will juniors gain the foundational experience needed to grow into senior roles and become experts? 📌 Faculty Adaptation – Many professors were trained before AI, making faculty upskilling and curriculum updates essential. 📌 Industry Feedback – Prompt engineering courses are being introduced, but structured industry feedback mechanisms remain underdeveloped. 🌟 These shifts in education extend far beyond law—they impact medicine, business, engineering, and more. 🚀 Where Education Must Evolve: ✅ Mastering Prompting Skills – Becoming an expert in asking the right questions to guide and instruct AI effectively. ✅ Critical Thinking & Verification – Developing the ability to evaluate AI-generated content for accuracy, bias, and real-world application – essentially becoming a ‘judge’ of AI outputs. ✅ Redefining Expert – Seniority does not equal expertise. True expertise lies in specialized knowledge and ensuring AI outputs are accurate and verifiable. A junior with niche skills and knowledge can be an expert—and highly employable. ✅ Getting Industry Feedback Involved – Every prompt needs real-world testing, and industry collaboration is essential to ensure these skills translate into practical impact. 💡 The Good News? 1️⃣ Pioneering programs like Northwestern Master of Science in Law are already addressing this need. At the intersection of law, business, and technology, it offers courses in 🌟 prompt engineering 🌟. 📖 Learn more here: https://lnkd.in/eD4VFRtG 2️⃣ We’re also building solutions to help students / professionals create, refine, and test expert-level prompts, ensuring they can actively contribute in an AI-driven world and receive real-world feedback. This discussion highlighted both challenges and opportunities. If you’d like to keep the conversation going and contribute your insights to help education and talent development evolve, I’d love to hear your thoughts: 🎓 If you’re a higher education professional, how is your institution preparing students for an AI-driven future? 🚀 If you’re a student or job seeker, how are you preparing yourself to manage AI tools and models—key skills for the workforce of the future? 🎯 Let’s exchange ideas and rethink education and talent development together. #AIinEducation #FutureOfLearning #EdTech #AIandWork #HRTech

If you spend your day on LinkedIn in an effort to understand how teachers, school leaders, and district leaders feel about the rise of edtech and AI in classrooms, you would think there is overflowing excitement on the grounds about the future. If you spend your day actually talking to teachers, school leaders, and district leaders, there is a lot of fear. That fear seems both logical and alarming. It's logical because: 1. Kids are increasingly addicted to screens. 2. The efficacy of many of the edtech solutions out there is questionable. 3. The use case of the various tools isn't always clear, which creates a ton of decision anxiety and implementation chaos. It's alarming because the distance between how technology/AI is being used outside of K-12 versus in K-12 is getting longer and longer. That can't possibly be good for students.

The rush to implement AI into schools has hit a fever pitch. Much of this urgency is fueled by fears that taking a slow, contemplative approach to AI will “harm the most vulnerable students” who will fail to learn the AI skills and literacies needed to avoid being “left behind” and “further marginalized.” There is also hope that these tools will “level the playing field” and bring about educational excellence and opportunity for all students. This is because these tools are often positioned as less biased and more efficient than human educators, and thus better at supporting the diverse needs of our diverse student body. While I appreciate these altruistic assumptions, and share the goal of advancing educational opportunity and excellence for all students, the taken-for-granted assumptions about AI’s inherent ability to “revolutionize education,” and "level the playing field" for all students needs to be more thoroughly interrogated. In my recent keynote for the Berkeley Leadership Programs I unpacked the past decade or so of transdisciplinary research on AI in education, and showcase some of the disparate harms these tools have levied against some of our most vulnerable students - the very students we are told these tools will inherently support. Unfortunately, in many ways, AI is quietly automating educational inequity, exacerbating the school-to-prison nexus, further reinforcing tracking and within-school segregation, and creating “synthetic” gaps in achievement and opportunity (see attached slides for examples) It is my hope that we become bold enough to slow down, ask questions, and investigate these tools before adopting them at scale. We can’t keep repeating Big Tech’s approach of “move fast and break things” when the “things” that are at risk of being broken are our students, our educators, and our communities. If you’re interested in watching the full keynote, you can access it here: https://lnkd.in/gGjsJWM6

𝐀𝐈 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧—𝐁𝐮𝐭 𝐖𝐡𝐨’𝐬 𝐢𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥? For centuries, great teaching was about delivering knowledge. Now, AI can generate explanations, personalize learning, and provide instant feedback better and faster than humans. So what happens next? 📌 Here are 3 hard truths about teaching in the AI era: ✔ The best educators will be the best learning architects. AI can teach every subject, but it can’t replace the human role of guiding discovery, fostering curiosity, and shaping learning experiences. ✔ The most valuable skill for teachers isn’t learning AI—it’s unlearning outdated teaching methods. Education is shifting from memorization and content delivery to facilitating critical thinking, problem-solving, and AI literacy. The best educators will adapt, not resist. ✔ If educators don’t lead this shift, AI-driven companies will do it for them. AI isn’t just a tool—it’s actively shaping the future of learning. The risk isn’t that AI replaces teachers, but that tech companies dictate education in ways that prioritize engagement metrics over real student growth. The choice is clear: Educators can shape AI’s role in learning—or let algorithms do it for them. How do you see AI transforming education? Let’s discuss. 📌 Credit to Arafeh Karimi for the inspiration. #AIinEducation #FutureofLearning #EdTech #EducationLeadership #AIandEthics