Addressing AI Challenges in Education

𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: 𝐄𝐝𝐓𝐞𝐜𝐡 𝐢𝐬𝐧’𝐭 “𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠”...𝐢𝐭’𝐬 𝐞𝐧𝐭𝐫𝐞𝐧𝐜𝐡𝐞𝐝. AI is already making decisions in your schools (grading, flagging, tracking), often without oversight. $ 1.6 M+ in lawsuits tied to AI-related issues in K–12 education (Langreo, 2024). Only 14.13% of districts have formal AI policies in place (Eutsler et al., 2025). This isn’t theoretical. It’s happening now. 𝐓𝐡𝐞 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐞𝐨𝐩𝐥𝐞. 𝐑𝐞𝐰𝐫𝐢𝐭𝐞 𝐭𝐡𝐞 𝐒𝐲𝐬𝐭𝐞𝐦. Refuse to reinforce what’s broken. We’re here to build something better– intentionally, transparently, and together. 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐈𝐟 𝐈 𝐰𝐞𝐫𝐞 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐚 𝐬𝐜𝐡𝐨𝐨𝐥 𝐭𝐨𝐝𝐚𝐲, 𝐈’𝐝 𝐝𝐨 3 𝐭𝐡𝐢𝐧𝐠𝐬 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲: 1. 𝑳𝒐𝒄𝒌 𝑫𝒐𝒘𝒏 𝑷𝒐𝒍𝒊𝒄𝒚 No AI use without clear, community-driven guardrails. Write policies that protect students and educators from day one. 2. 𝑻𝒓𝒂𝒊𝒏 𝑩𝒆𝒇𝒐𝒓𝒆 𝒀𝒐𝒖 𝑰𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒆 By fall 2024, only 48% of districts had trained teachers on AI use (Diliberti et al., 2025). You can’t lead what you don’t understand. 3. 𝑨𝒖𝒅𝒊𝒕 𝒀𝒐𝒖𝒓 𝑻𝒆𝒄𝒉 Most school tools already use AI, and few districts know how. Run an audit. Review contracts. Ask hard questions. Fix what’s hiding in plain sight. P.S. School leaders still have the chance to shape the narrative. This is a rare window of opportunity. You have time to set the guardrails. But that door won’t stay open forever. 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐩𝐮𝐫𝐩𝐨𝐬𝐞. 𝐎𝐫 𝐠𝐞𝐭 𝐥𝐞𝐝 𝐛𝐲 𝐫𝐢𝐬𝐤. 𝐘𝐨𝐮𝐫 𝐦𝐨𝐯𝐞. #Superintendent #EducationLeaders #AIinEducation #EdTechStrategy #FutureReadySchools #K12Leadership #DistrictInnovation #StudentCenteredLeadership #PolicyDrivenChange

AI is revolutionizing education, offering tools that personalize learning and break down barriers. But with great power comes great responsibility. Let’s unpack the ethical challenges facing AI-EdTech: → Algorithmic Transparency How does AI decide which student gets which resources? Companies must ensure transparency, enabling educators and learners to understand and trust the system. → Combating Algorithmic Bias AI learns from data, but data isn’t always neutral. To prevent discrimination, algorithms need regular audits and updates. Equity isn’t optional—it’s essential. → Data Privacy & Security Student data is sensitive. From complying with regulations to protecting against breaches, EdTech companies must make privacy a top priority. Clear communication about data usage builds trust. → Balancing Profit with Purpose Profitability drives innovation, but it should never overshadow the mission to educate. Purpose-driven innovation focuses on solving real challenges, not just riding the AI wave. → Engaging Stakeholders Teachers and students know the classroom best. Collaborating with them ensures AI tools meet genuine needs while maintaining ethical integrity. → Navigating Regulatory Challenges The laws governing AI in education are still catching up. Companies should advocate for clear, ethical guidelines while proactively ensuring compliance. The bottom line? Corporate responsibility in AI-EdTech isn’t just good PR—it’s a necessity. By aligning profit with purpose, we can create tools that truly serve learners, uphold ethical standards, and build a future where education and innovation thrive together. The Bertrand Education Group (B.E.G) What are your thoughts on balancing ethics and business in AI-EdTech? Let’s discuss!

If students don’t learn how to think with AI, they’ll let AI think for them. Last Thursday at Shanghai American School, I got to "beam in" to give a keynote presentation on one of the most urgent conversations in education today: How do we integrate AI without losing what makes learning human? Here are the key takeaways from our time together: • Generative AI can amplify learning—or weaken it. Studies show that when students engage critically with AI, they learn more. But when they rely on it to do the work for them, learning declines. The key? Teach students to think with AI, not just use it. • Confidence in AI can lower critical thinking. Research suggests that when people trust AI too much, they question it less. The best educators will teach students how to balance trust and skepticism when using AI tools. • Ethical AI use starts with values. We discussed how every school needs guiding principles for AI integration—beyond just policies. What should we protect? What should we enhance? These questions shape AI’s role in education. We concluded with "Three Ts" for responsible AI use: 1. Talk – Normalize generative AI discussions with students and teachers. I shared my "Generative AI Guidelines Canvas" to support conversations. https://lnkd.in/gyjTkK7d 2. Teach – Build generative AI literacy into the curriculum. I shared Cora Yang and Dalton Flanagan's C.R.E.A.T.E. framework for teaching students to prompt. https://lnkd.in/g-KYt4Uy 3. Try – Teachers should experiment with generative AI tools in meaningful, ethical ways. I shared Darren Coxon's Hattie Bot to let teachers experiment with building lessons that have high effect size. https://lnkd.in/g44gZzA3 This conversation isn’t over—it’s just beginning. Critical thinking isn't optional if machines do the easy thinking for us. Much gratitude to Alan Preis & Scott Williams for crafting such a great experience. Photo Credit Alex McMillan 🙏 P.S. I asked everyone at Shanghai American School: What values should guide our approach to AI in education? What's your answer? #generativeAI #guidelines #teachers #ethics

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

*** Using AI in education ... not such a straightforward application as we may believe! At D'Amore-McKim School of Business at Northeastern University, we launched an AI strategic initiative for the school, which is housed now in what we call DASH (led by Kwong Chan): https://lnkd.in/eTDk8nPY The goal of the initiative is to bring #AI into our #teaching, #research, #corporateoutreach and #societalfunctioning. As part of this ambition, we recently launched a series of projects in the classroom to see how students and professors respond to the employment of AI. And, the findings so far have been interesting! Common belief is that AI can optimize certain educational support processes. One such process is providing #feedback to students to improve their future #performance. We find that, first of all, students appreciate receiving feedback, but it cannot be too much. If the feedback is too long, they don't read it. In fact, the lower their grade, the less motivated they feel to look at the feedback. We found that those receiving a 5/10 are much less motivated to look at the feedback compared to those who received a 7/10, mainly because the former think that they don't have the abilities to integrate the feedback and improve. Now, what happens in this feedback context, is that AI writes longer feedback memos than human evaluators do. Human evaluators focus on one or two themes that they want to emphasize and empathize that feedback cannot be too long. AI, in contrast, aims to optimize all its observations and as such provides longer, more detailed and consistent feedback across multiple themes. The consequences are clear: although AI may be more accurate, more structured and providing feedback that is set up for success, the reality is that students will likely not use it. Even worse, the really useful feedback provided by AI is likely to be used only by high performers, as such widening the gap between high and low performers in the classroom. Solution: AI in the classroom has to be designed and used in behavioral human-centered ways that take into account the behavioral habits of students in how they deal with feedback and as such make it more accessible to all students. Northeastern University National University of Singapore Academy of Management EY Sufian Hwedi Jess Zhang Dirk Boghe Jay Narayanan Andre Spicer Ethan Mollick Alexandros Papaspyridis Julie Anne McNary