AI in Education Innovation

A look at how CS50 has incorporated artificial intelligence (AI), including its new-and-improved rubber duck debugger, and how it has impacted the course already. 🦆 https://lnkd.in/eb-8SAiw In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had "a personal tutor." Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education. Paper at https://lnkd.in/eZF4JeiG. Slides at https://lnkd.in/eDunMSyx. #education #community #ai #duck

Some thoughts on how we integrate AI into education: We first need to start by recognizing which skills are becoming more valuable and designing new ways to teach them. We all remember the effort it takes to write a paper—revising, structuring arguments, and refining our points. With AI, everyone will have a writing co-pilot to handle the mechanics, making the process more efficient. So, what if we redirected that effort into helping students develop higher-order skills like critical thinking, prompt design, and iterative analysis? A thought experiment: Imagine an assignment where students submit not just their essays but also the prompts they used to get AI-generated critiques. Their task wouldn’t be just to write and submit—it would be to argue, analyze, refine, and iterate. In less time than it takes to write a traditional paper, students could engage in deeper intellectual exercises—interrogating their own arguments, considering counterpoints, and strengthening their reasoning. For teachers, AI can streamline grading while amplifying feedback—providing broad insights that help shape targeted, meaningful commentary. This means students receive richer, more personalized guidance, making learning more interactive and impactful.

Anthropic launched three new AI fluency courses alongside research analyzing 74k educator conversations. The data provides concrete insights into how AI is being used in higher education, while the courses address some of the challenges and opportunities these findings reveal. Key findings from the research: • 57% of faculty chats with Claude focus on curriculum development and instructional design • Faculty show a preference for using Claude to augment not automate their work • While faculty rate grading as AI’s least effective application in their practice, nearly half of grading conversations were fully automated • Faculty expressed concerns about "cognitive offload" and students becoming overly dependent on AI, emphasizing the need for students to develop foundational AI literacy skills The newly released courses demonstrate a commitment to higher education and begin to bridge the gap between current practices and effective AI integration. Here are some of the course highlights: • Created three courses—for students, for teachers, and for teaching AI fluency • Strong "human in the loop" approach that aligns with the augmentation patterns educators prefer • 4D framework (Delegate, Describe, Discern, Diligence) offers structured decision-making for when to collaborate vs. automate • Focus on responsible collaboration and academic integrity, particularly relevant given the grading automation concerns • Content designed for college-level learners, though not suitable for K-12 audiences The alignment between the research findings and course principles is encouraging. However, the data also reveals why targeted training matters—educators are innovating in curriculum development, but may need additional support around assessment practices. It's promising to see how these frameworks align with approaches like our SEE model (Safe, Ethical, Effective AI use), pointing toward more consistent standards across the field. Link to the courses and the research in the comments. AI for Education #AIfluency #AILiteracy #AIeducation #K12

AI isn’t just the future, it’s now mandated to be part of the curriculum. A new executive order, "Advancing Artificial Intelligence Education for American Youth", is pushing to embed AI education across all levels of learning While preparing students for an AI-driven future is necessary, I'm deeply concerned about implementation without careful consideration. History offers cautionary tales. From "No Child Left Behind" to standardized testing mandates, we've seen educational reforms create unintended consequences. Now, we risk prioritizing AI fluency over human development which can reshape curriculum around technology rather than the learner. As both a tech advocate and parent, I'm troubled by the nuanced questions being overlooked: 1️⃣ Data Sovereignty: Every interaction our children have with AI systems creates valuable data. Who owns it? How is it protected? Are our classrooms becoming extraction grounds for tech companies building proprietary systems? 2️⃣ Truth Discernment: AI makes confident assertions regardless of accuracy. We're asking children to develop critical thinking skills while simultaneously introducing tools that blur the line between fact and fabrication. 3️⃣ Human Intelligence: Teaching isn't merely content delivery – it's relationship-building, emotional intelligence, and personalized guidance. What irreplaceable human elements are we sacrificing at the altar of technological efficiency? 4️⃣ Power Dynamics: Private corporations develop most educational AI systems with profit motives and proprietary algorithms. Are we embedding corporate interests into the fabric of public education? The contradiction is striking: an administration advocating for local educational control (ala DOE dismantling) while imposing sweeping federal directives on AI integration. Technology can transform education positively, but implementation requires deliberate care, not rushed mandates. This is just the beginning of many conversations we need to be having. While the answers aren't crystal clear today, I'm committed to navigating this landscape alongside you. Through Mother AI, I'm dedicated to keeping parents informed and empowered to engage meaningfully with school systems and local policymakers about AI in education. In tomorrow's newsletter (link to join in comments), I'll be diving deeper into practical ways parents can start these conversations with educators and administrators. The questions we ask today will determine whether technology amplifies human potential or diminishes it. What, if any, conversations are happening in your child's school about AI implementation? What are you most concerned about when it comes to AI and its impact on your child's education? #FutureOfEducation #AIEthics #DigitalChildhood #MotherAI #ShePowersAI

Important new evidence on ChatGPT in education: Wang & Fan's (2025) meta-analysis of 51 studies shows we're at an inflection point. The technology demonstrably improves learning outcomes, but success depends entirely on implementation. The research reveals optimal conditions: sustained use (4-8 weeks), problem-based contexts, and structured support for critical thinking development. Effect sizes tell the story; large gains for learning performance (g=0.867), moderate for critical thinking (g=0.457). Quick fixes don't work. Thoughtful integration does. Particularly compelling: ChatGPT excels in skills development courses and STEM subjects when used as an intelligent tutor over time. The key? Providing scaffolds like Bloom's taxonomy for higher-order thinking tasks. As educators, we have emerging empirical guidance for AI adoption. Not whether to use these tools, but how to use them effectively - maintaining rigor while enhancing accessibility and engagement. The future of education isn't human or AI. It's human with AI, thoughtfully applied.

🎓 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

🚨 Big moment for AI in U.S. education — and your voice is needed! 🚨 The U.S. Department of Education just released proposed priorities and definitions focused on Advancing Artificial Intelligence in Education (a follow-up to the April executive order). This language will shape how future federal grants invest in AI-related teaching and learning across K–12 and higher ed. 📘 Read the full proposed rule in the PDF below. Here’s some of what stood out to me: ✅ The definition of AI literacy emphasizes not just technical skills but also “durable skills” and “future-ready attitudes.” It’s about empowering students to not only use AI but also to shape it, create with it, manage it responsibly, and evaluate its impact on society. ✅ I also appreciate how computer science is portrayed — not just coding, but computational thinking, data analysis, machine learning, and interdisciplinary problem-solving. Note: I recommend changing "data analysis" to "data science" because the road to AI literacy goes through a fundamental data science experience. This framing aligns beautifully with how we at Code.org and many in the #TeachAI community think about preparing the next generation of AI-literate citizens and creators. ➕ As a parent and educator, I feel like there is one missing element: AI's impact on social and mental health. I propose this edit to an existing priority "(ix) Support dissemination of appropriate methods of integrating AI into education, INCLUDING THE IMPACT OF AI ON SOCIAL AND MENTAL HEALTH." Sorry for the caps. Many education leaders recognize that schools dropped the ball a decade ago when it came to teaching kids about social media. But hope is not lost. We can address the growing social and mental health impact of AI companions and unsupervised chatbot usage early and often, before it becomes an even bigger problem than social media. 🏫 The rest of the proposed priorities include: • Integrating AI and CS into K–12 and higher ed curricula • Providing professional development for teachers • Using AI tools to personalize learning and support diverse learners • Building evidence around what really works 🗳️ The comment window is open until August 20, 2025, and I encourage everyone in the AI and education space — from researchers to teachers to policymakers — to submit your thoughts. It’s an opportunity to influence how federal dollars are allocated for one of the most significant transformations in education today. ✍ See the link in the comments below for how to submit comments on the proposal. #AIEd, #CSforAll, #AILiteracy

Published this week, final version: “The Ends of Tests: Possibilities for Transformative Assessment and Learning with Generative AI” In "The Ends of Tests," Cope, Kalantzis, and Saini propose a transformative vision for education in the era of Generative AI. Moving beyond the limitations of traditional assessments—especially multiple-choice and time-limited essays—they advocate for AI-integrated, formative learning environments that prioritize deep understanding over rote recall. Central to their argument is the concept of cybersocial learning, where educators curate AI systems using rubric agents, knowledge bases, and contextual analytics to scaffold learner thinking in real time. This reconfigures the teacher’s role: not diminished by AI, but amplified through new pedagogical tools. The authors call for education systems to abandon superficial summative assessments in favor of dynamic, dialogic, and multimodal evaluations embedded in everyday learning. Importantly, this model aims to redress structural inequalities by personalizing feedback within each learner’s “zone of proximal knowledge.” Rather than automating outdated systems, the paper imagines AI as a medium for epistemic justice, pedagogical renewal, and educational equity at scale. Full text and video here: https://lnkd.in/efhjt6jf

Here's a fascinating bit of history: the United States Military Academy at West Point has been using "AI" since the 1800s (although not the kind you may be thinking of).  "Additional Instruction" (AI) has been a cornerstone of cadet education, offering personalized 1:1 mentoring to those students struggling with complex subjects. Now, a forward-thinking West Point accounting professor has created "AI4AI" - ingeniously merging traditional Additional Instruction with modern artificial intelligence. 🔄Here's how AI4AI works: 1. Cadets must first consult an AI Tutor to explore their questions 2. They submit their AI conversation logs when requesting Additional Instruction from a professor 3. The professor analyzes the submitted AI dialogue before meeting the student 🌟 Why This Approach Is Innovative: This approach aligns perfectly with BoodleBox's three pillars of AI readiness: 1. Domain Expertise: - Students must actively wrestle with concepts using AI before getting Additional Instruction - This "productive struggle" builds deeper understanding 2. AI Enablement: - Students get hands-on experience learning when and how to use AI effectively - This develops critical AI enablement skills for future leaders 3. Human Excellence: - Student-professor interactions become laser-focused on advanced concepts, with AI handling foundational questions beforehand. - By reviewing the student’s AI interactions first, professors can focus their valuable time on what matters most: providing targeted mentorship, sharing deep insights, and building meaningful connections with students. 💡 Why AI4AI Resonates with Modern Education: - It keeps the “Professor in the Loop” ... AI is used as part of a collaboration not as a replacement - It maximizes instructor impact: the Professor can focus on deep engagement and transformative teaching moments - It creates a scalable model for personalized learning support: a professor can reach more students without sacrificing individual attention - It empowers student autonomy while reinforcing that they can and should reach out for guidance when needed 🚀 For Fellow AI in Education Innovators: This aligns well with the innovative approaches to responsible AI in education that we're seeing from over 10,000 faculty and students using BoodleBox: - It's a great example of teaching with AI (to create domain expertise) and teaching about AI (to develop AI enablement), while crucially maintaining the irreplaceable role of human educators - this isn't about AI replacing professors (teaching by AI), but rather empowering faculty to be even more effective and impactful while also being efficient. This innovative approach maintains West Point's tradition of educational excellence while readying cadets for an AI-powered future. It shows how historical teaching methods can be thoughtfully adapted with technology for the modern era. Totally Not Genuine AI Generated Photo Credit: Flux

The U.S. Department of Education proposes a new discretionary grant priority on AI in education. 30 day public comment period. Background & Rationale - AI is reshaping education and the workforce, making AI literacy crucial for students. - AI tools can support personalized instruction, engagement, and learning outcomes. - Computer science education is foundational to understanding and using AI responsibly. - Educator training and early exposure to AI concepts are key to workforce readiness and innovation. Proposed Priority Areas (a) Expand AI Understanding - Integrate AI literacy and misinformation detection into teaching. - Expand K-12 and higher education offerings in AI and computer science. - Embed AI into teacher preparation and professional development. - Support dual-enrollment and certification pathways in AI. - Build and share evidence for effective AI integration in education. (b) Expand AI Use in Education Use AI to support: - Gifted students or those needing advanced learning. - Students below grade level or needing additional support. - Students with disabilities and their families. - Deploy AI-powered personalized learning tools ("This integration may include, but is not limited to, adaptive learning technologies, virtual teaching assistants, tutoring, and data analytics tools to support student progress") - Promote AI in teacher training and operational efficiency. - Use AI technology to provide high-quality instructional resources, high-impact tutoring, and college and career pathway exploration, advising, and navigation to improve educational outcomes. Submit comments by August 20, 2025. The full Federal Register notice is here: https://lnkd.in/eC5jf-Dk FYI: Kim Smith, Alex Kotran, Pat Yongpradit, Claire Zau, Jean-Claude Brizard, Amy Chen Kulesa, Mary Wells, Cheryl Oldham, Caitlin Codella Low, Kyle Butler, Julia Freeland Fisher, Alex Swartsel, Rebecca Finlay, Daniel Correa, Reeve Bull, Cassandra Madison, Jennifer Anastasoff, Richard Culatta, Gabriela Lopez, Robin Lake, Bree Dusseault, Brent Orrell, Hayley S., Meg Evans, Angie Cooper, Krista Cupp, Elizabeth Pishny