Key Takeaways:

• Writing is thinking, not output. The point of a writing course is not to produce more papers in the world. It is to give students the experience of working through evidence, weighing ideas, and figuring out what they actually believe.
• Editing skills are not a substitute for drafting. The argument that students can skip the first draft and learn to polish AI output assumes a skill that develops only through drafting. Jane has not seen evidence that students who never write a first draft can revise their way to something meaningful.
• Feedback is relational. A writing tutor often does not know where the paper will end up, and that shared uncertainty is the point. A chatbot can work on what is already on the page, but it cannot build a bridge to the idea a student has not yet had.
• Feedback on demand undermines productive struggle. When students can revise and resubmit to a chatbot at 1 a.m., the friction that makes them reconsider what they think disappears. The decision to skip that friction is being made for reasons other than learning.
• Integrating AI into every course is not a solution. Students can distinguish between AI uses designed to push their thinking and how they will actually reach for the tool under a deadline. Teaching productive uses does not prevent the unproductive ones.
• The deeper challenge is equity, not just pedagogy. A real risk is that students at well-resourced institutions continue to learn how to think while students elsewhere have their instructors replaced with chatbots. Aligning incentives so grades and learning point in the same direction is the work ahead.

Key Takeaways:

• Generative AI is being litigated as a defective product. Tech Justice Law has filed cases tying ChatGPT to suicides, suicide attempts driven by AI delusions, and even a school shooting in Canada. The legal theory treats the chatbot itself as a product whose harms were foreseeable and whose deployment was negligent.
• Foreseeability is doing a lot of the work. A book that contributes to a mental health crisis is hard to litigate; a chatbot designed to mimic human emotion and used by a 12-year-old is not. When a company knows or should have known that a product can cause specific harms, the law has tools to respond.
• Disclaimers do not erase liability. A "this may hallucinate" warning, or Copilot's "for entertainment purposes only" terms, do not get a company out from under strict product liability when people are losing their lives. Courts will ask whether the company did enough, not whether it checked a box.
• States are doing the work Congress is not. State attorneys general are opening investigations, state legislatures are passing AI-specific laws, and California recently moved to block the "the agent did it" defense. Federal action is unlikely in the next two to three years.
• The harms cut across demographics. Unlike the social media cases, which centered on minors, AI chatbot cases involve children, older adults, people with disabilities, and even tech-savvy users. The speed and scale of impact is what makes generative AI different.
• Agentic AI raises the stakes again. When a single company can deploy 200 autonomous agents instead of one rogue employee, the scale of potential harm changes the legal calculus. Insurance products are emerging, but Tiffany is skeptical that liability can be outsourced to the agent itself.

Key Takeaways:

• FERPA was written for filing cabinets, not cloud platforms. Passed in 1974, FERPA still grants parents a right to access every record a school maintains about their child, including data held by ed tech vendors. But it has never been enforced by the Department of Education, and individuals cannot sue under it, which leaves most of the work to proactive parents.
• "FERPA compliant" on a vendor website is a marketing slogan. There is no Department of Education certification program. The obligation falls on schools to ensure their vendors actually limit data use to educational purposes, and parents should ask schools how they define "school official" and what contracts allow.
• COPPA stops at the thirteenth birthday. The Children's Online Privacy Protection Act applies only to sites directed at children under 13, leaving teenagers in what Venzke describes as a regulatory wild west. The ACLU argues that data minimization and affirmative consent should be extended to everyone, not gated by age.
• Flat bans on minors using social media will likely lose in court. The Supreme Court has held that minors' First Amendment rights are largely coterminous with adults'. Venzke predicts that age-based bans will be struck down as overbroad, and argues that regulating how platforms collect and use data is a more constitutionally durable approach than restricting speech.
• School AI surveillance is being deployed without testing. Facial recognition, weapons detection, and communication monitoring tools are sold to schools without proof they work as advertised. Venzke cites cases where students have been outed by large language models that misread diary entries as bullying, and argues that high-impact AI uses should require state-level vetting requirements.
• Removing a student's name from a ChatGPT prompt does not make it FERPA safe. Identifying details like "the only Native American student in fifth grade" can still trace back to an individual. Venzke argues teachers should not be left to vet AI tools on their own; districts, states, and procurement processes need to do that work.

Key Takeaways:

• Build a philosophy, not a plagiarism policy. Joel draws an analogy to dress codes: the more granular the rule, the more the only thing you see is the violation, not the person. AI use is too varied across math, history, and English classrooms to codify the way schools codified plagiarism a generation ago, and a philosophy gives educators the room to make case-by-case judgments.
• Trust the team first, accelerate later. Joel chose a two-to-three year change trajectory anchored in building educator trust rather than racing to be first. His worry was falling behind by 2027, but the trust groundwork is what made the eventual rollout move quickly and made families comfortable with the rollout.
• Originality has always been a puzzle, and AI just forces the question. Joel pushes back on the assumption that pre-AI student writing was somehow more "original," pointing out that Shakespeare cribbed too and that brain science still cannot pin down what original thought really is. Schools have been asserting certainty they never had, and AI is making that hard to avoid.
• Students are no longer the experimenters they were two years ago. Joel sees the current generation as more anti-AI than in 2023, citing concerns about energy use, corporate ethics, and privacy. Teachers using AI sloppily and shipping obviously machine-generated lessons has accelerated that skepticism, which is why he tells teachers to disclose their AI use and how they checked it.
• Strident anti-AI students need to be interrogated too, not just validated. Joel argues schools should push back when students refuse to engage with AI, not to override their values but to ask whether their stance is rooted in privilege, fear, or genuine principle. The work of school is teaching kids to handle complexity, not to handle any specific tool.

Key Takeaways:

• Policy can legislate behavior but it cannot form character. Chuck argues that a clean ban or a strict acceptable-use document is the easy move, and the wrong one. Without a foundation underneath it, students will either ignore the rule or comply for the wrong reasons. The school's one-sentence AI policy treats the question as plagiarism, which misses most of what the technology actually changes.
• A Christian worldview has to address AI the same way it addresses every new tool. The Bible does not name AI any more than it names calculators or television, so the work is in applying an existing foundation to a new technology. Chuck wants students to be able to reason from that foundation themselves rather than relying on him to legislate each case, especially because they will leave the school and lose the legislator.
• Writing instruction was already in trouble before AI arrived. Texting has shifted how students communicate so far that sixth graders submitting "OMG" and "TY" in their papers is no longer surprising. AI does not start the decline in written reasoning; it accelerates a slide that started with the way students already talk to each other. Chuck plans to use handwritten baseline essays to anchor what each student can actually do without help.
• Demonstrating the tool in class is more honest than hiding it. Chuck plans to put ChatGPT on the classroom screen, show how fast it can produce an essay, walk through prompting, and surface the hallucinations and fabricated citations directly. The argument to students is that cutting and pasting cheats them out of the learning, and that integrity has to be taught, not assumed.
• The AI conversation has to include companions and cyberbullying, not just essays. Chuck wants the elective to cover Replika-style companions and image-manipulation tools alongside academic use, because those are the parts students are already encountering outside class. Putting head in the sand, especially in a Christian school context, leaves students to form a worldview about these tools on their own and usually badly.

Key Takeaways:

• Bloom's Taxonomy breaks down because AI collapses the distinction between output and thinking. The old model assumed a polished answer proved learning; AI now makes that assumption untenable, forcing educators to make thinking visible through process rather than relying on products as evidence.
• UnBlooms treats learning as recursive, not hierarchical—and starts with intentional friction. Rather than inverting Bloom's or banning AI, Tina's model requires students to show their initial thinking, engage critically with AI output, and revise with judgment; the shape shifts from a ladder to a spiral where learners don't return to the same place twice.
• Different disciplines protect different kinds of thinking, and AI policy should honor that variation. STEM faculty worry about problem-solving integrity; humanities faculty about voice and nuance; effective AI policy emerges from asking each discipline what thinking they need to safeguard, not from imposing one rule across all fields.
• The most productive AI use in classrooms builds critical skepticism, not efficiency. Having students critique AI-generated lecture summaries or debate where AI diverges from expert knowledge creates genuine engagement; offloading listening itself (via AI note-takers) removes a central learning function and trades visibility into thinking for marginal convenience.
• Higher education's crisis is not new, but AI has made it visible and urgent. Tenure and research incentives protect teaching practices that no longer serve; the opportunity now is to ask honestly whether courses are helping students develop judgment and prepare them for genuine uncertainty—not to add AI on top of unchanged structures.

Tina Austin is an AI educator, researcher, and policy advisor working at the intersection of education, healthcare, science, and emerging technology. Recognized as one of ASU+GSV's Leading Women in AI (2025), featured by OpenAI Academy, and interviewed by CNN, she is one of the most prominent voices guiding institutions toward responsible, human-centered AI adoption. She has led courses at UCLA, USC, CSU, and Caltech spanning critical thinking with AI, biomedical research, regenerative medicine, and ethics.

Key Takeaways:

• American schools excel at fostering belonging and passion; German schools prioritize academic depth. The US system's emphasis on extracurriculars, personalized classrooms, and elective variety created a strong sense of community and identity, while Germany's more rigorous curriculum moved students through material years ahead—showing that schools can optimize for different values but rarely achieve both simultaneously.
• Technology in classrooms creates distraction rather than learning gains. Whether Chromebooks or iPads, digital devices enable both research efficiency and constant off-task engagement; Laura's choice to prioritize TKS work over classroom attention reveals that access to devices lets ambitious students opt out, while less motivated students simply drift.
• Project-based learning and standardized structures cannot coexist. Rigid schedules, subject silos, and grades as numbers fundamentally conflict with the flexible, exploration-driven learning Laura values—and attempting to layer PBL onto existing structures, or adding AI without rethinking foundations, misses the deeper architectural problem.
• School provides maturity and awareness that independent learning cannot. Laura credits high school with giving her the lived experience of education's shortcomings, which then motivated her own solutions; skipping formal education earlier wouldn't have accelerated her impact because she lacked the contextual understanding to see the problems that mattered.
• The students most prepared for the future are building it themselves alongside school, not through it. TKS, her project Passion Fruit, and her conference attendance are where Laura develops judgment, iteration, and genuine stakes—school becomes optional context rather than the primary engine of growth for students who have found their direction.

Laura Schroeder is a high school student driven by curiosity and a desire to create meaningful impact. As an Innovator at The Knowledge Society, she builds projects at the intersection of AI, project-based learning, and student agency. Laura is on a mission to reimagine secondary education by returning to first principles and the 'why' behind education - advocating for personalized, interdisciplinary, and foundational education that equips students to thrive in today’s world and the one ahead.

Key Takeaways:

• Being surrounded by technology is not the same as understanding it. Students who've grown up with devices don't automatically know what cookies are, how algorithms predict behavior, or what happens to their data—the access itself teaches nothing without deliberate instruction on how the systems actually work.
• Removing phones from the classroom improved student focus, and students embraced the restriction because it came from them. When administration asked students what they thought about a no-phone policy rather than imposing it, students volunteered the idea and enforced it themselves—suggesting that transparency and student agency can matter more than the rule itself.
• Communication is the foundational skill that makes everything else—including AI use—work. Whether students are writing essays, coding, or prompting AI, the core challenge is knowing how to articulate what they actually want; bad communication produces poor results regardless of the tool.
• AI should be a sparring partner that pushes back, not a butler that does the work. The distinction between using AI to clarify thinking through dialogue and using it to bypass thinking entirely shapes whether it's a learning tool or a shortcut, and teachers need to model and enforce that distinction explicitly.
• The "digital native" myth obscures what students actually need to learn. Today's students need basic digital literacy—not just access to technology—and they need adults to show them responsible use in real time, because peer pressure and the competitive advantage of shortcuts remain powerful forces.

Melvin D. Smith II’s path to tech instruction has been all but a clear one: first planning to be an astronaut to pilot the space shuttle, then changing to become a physician, then neuroscience researcher... 27 years ago he started his career in teaching (formal and informal) science. Adopting the philosophy of STEAM instruction before it became a thing, he fully embraced and utilized the disciplines for the learning environment- in and outside the classroom. Fast forward to his current position at Garrison Forest School in Maryland, Melvin still maintains that practical learning is the most salient and beneficial to developing soft skills and transferable knowledge. Whether in the Digital Thinking class, discussing and practicing the uses of technology to maintain a positive digital footprint; AP Computer Science Principles, where application development coincides with block and text coding; or a brand new course on the history and pedagogical use of AI, his coursework is still rooted in the idea that each student can be reached and succeed if they are given the correct tools, are willing to put forth the effort, and granted a little patience.

Key Takeaways:

• Understanding responsibility is not the same as practicing it. Students conceptually grasp that using AI to do their work for them is wrong, but when faced with pressure to get things done, they often choose the shortcut anyway—suggesting that knowing what you should do doesn't guarantee you'll do it.
• Self-paced, online environments create new accountability problems that have nothing to do with AI. The absence of in-person interaction makes it harder to detect cheating and easier to rationalize it, which means AI hasn't created the problem of student disengagement—it's simply made it more visible and more scalable.
• Your teaching intuition about whether something is AI-generated will become less reliable. As students grow up reading AI-generated text, their own writing will be shaped by those patterns, making it harder for teachers to distinguish between authentic voice and AI assistance based on stylistic markers alone.
• Presenting work through dialogue forces different stakes than submitting text alone. Requiring students to explain their thinking through presentations or discussion boards creates accountability that's harder to fake, even if the source material was AI-generated.
• The gap between high-achieving and struggling students will likely widen because of how students think about time. Students with short-term vision—those thinking about the next 24 hours rather than long-term consequences—are the most vulnerable to AI shortcuts, and they're also the ones who need human attention most.

Lorin Koch is an educator with 21 years experience teaching high school and 3 years as a college instructor of education. He holds an Ed.D. degree from the University of South Carolina. Lorin currently teaches online and in person from Washington state, where he works at Walla Walla University. He also writes and presents on Artificial Intelligence in education, focusing on integrating generative AI into the classroom.

Key Takeaways:

• Curiosity as a pedagogical aim changes everything about how students use AI. When learning for its own sake is the standard—not grades or compliance—AI becomes a catalyst for deeper exploration rather than a means of dodging work. A student asking AI about birds of prey out of genuine interest learns far more than one copying homework.
• Making AI use visible and gamified shifts students from hiding it to owning it. Mary's "quack quack" jar and peer accountability turn using AI into something worth discussing openly. Social transparency works where rules do not.
• Three non-negotiable standards replace prohibition: name the tool, share the prompt, explain the output in your own words. This mirrors citation practices students already know. It's not about policing—it's about maintaining the chain between question, resource, and understanding.
• Strict phones, generous computers reflects a deeper principle about attention and agency. Banning personal devices while enabling desktop computers creates a bounded space for learning. The boundary isn't about rejecting technology; it's about who controls the environment.
• Late-career teachers bring a rare asset: they remember how knowledge worked before AI. Mary's corporate background means she can model learning alongside students without needing to be the expert first. That permission ripples through the classroom.

Mary Shawn M. Newins is a Marketing and Computer Science educator at Southern Guilford High School in Greensboro, North Carolina. She has been a full-time faculty member since Spring 2023 and proudly serves as the school’s AI Champion, supporting innovative and responsible technology integration in the classroom. Mary holds a Bachelor of Science in Education from Bowling Green State University and is an Ambassador for the CodeMonkey High School curriculum, advocating for accessible and engaging computer science education for all students. Before transitioning into education, Mary spent 30 years in the business sector, working across business-to-business sales, retail, direct sales, and operations management. Outside the classroom, Mary is a wardrobe stylist at Chico’s Friendly Center, a denim upcycler, and a creative at heart who enjoys painting.

Key Takeaways:

• Treating AI as a search engine reframes citation, sourcing, and critical thinking as one unified practice. Students must learn to evaluate AI outputs with the same skepticism they'd apply to any source—examining bias, verifying claims, and contextualizing information. This makes digital literacy inseparable from historical literacy.
• The equity issue isn't access; it's reliability and responsibility at different price tiers. Paid AI plans produce output 20% more accurate than free versions. When affluent students get more reliable tools, the learning gap widens. Teaching responsible use becomes a justice issue.
• Academic dishonesty with AI reflects overwhelm, not moral failure. High-achieving students risk-taking for perfection; struggling students disengaging entirely. Neither group benefits from prohibition. Both need to understand why checking your work still matters.
• Transparency about your own AI use gives students permission to use it thoughtfully. When teachers hide their tool-use, students either view AI as forbidden or adopt it covertly. Showing your process—and its limits—normalizes critical engagement over sneaking.
• Districts need protected time, not more mandates, to equip teachers as active learners. Asking educators to master AI literacy while managing diploma rewrites, state standards shifts, and dual-credit pipelines is unsustainable. The bottleneck is time, not will.

Alyssa Muhvic is a Social Studies Teacher at Noblesville High School in Indiana, where she has been shaping young minds since 2021. She teaches United States History, Pre-AP World History, and Indiana Studies, and was the driving force behind launching the school's Ethnic Studies course — designing and implementing the curriculum from the ground up. Alyssa earned her degree in General History and Secondary Social Studies Education, with a minor in African American Studies, from Ball State University in 2021. 

Key Takeaways:

• Speech-to-text accessibility tools matter more to this population than ChatGPT ever will. For students with typing challenges and diverse communication styles, the ability to speak and have systems capture their thinking credibly is transformative in ways that generative AI is not.
• Pandemic developmental delays hit hardest where social interaction was irreplaceable. Students with cognitive delays experienced compounding losses during remote learning—missing not just content but windows of social and executive development that cannot be fully recovered later.
• Teachers are curators of development, not content deliverers. Brian frames his role as shepherding students toward independent learning and workforce readiness, making technology decisions based on what advances that mission, not on what's trendy.
• AI's dual promise and peril is most acute for students with fewer safeguards against manipulation. The same tools that could help students with dyslexia access reading can also draw them into harmful spaces they wouldn't otherwise encounter—requiring active pedagogical intervention.
• Educators need unified policy guidance, not individual teacher judgment calls on authenticity. Without district-wide clarity on what constitutes authentic work in an AI world, each teacher invents their own standard, creating inconsistency and confusion.

Brian Merusi is a mission-driven educational leader and community developer who combines over four decades of diverse global experience with a passion for practical solutions. Deeply rooted in Special Education and Learning Support across the U.S., Malaysia, the UAE, and Poland, his career also encompasses executive roles as a biotech CEO and development leadership in the D.R. Congo and Uzbekistan. A specialist in technology integration, Brian currently leverages this unique cross-sector expertise to create accessible learning environments where technology opens doors for every student.

Key Takeaways:

• EdTech should solve existing problems, not create new ones. Paul approaches technology as a tool only when he's already facing a pedagogical challenge. This shifts the question from "what can this tool do?" to "what does my classroom need?"
• YouTube explainers preceded ChatGPT in reshaping how students research and learn. Long before AI, students were outsourcing understanding to video tutorials rather than wrestling with dense texts, revealing a deeper shift in how students approach knowledge.
• Critical thinking instruction requires direct practice with real arguments, not shortcuts around difficulty. There's no substitute for students actually constructing and defending their own positions through dialogue and written work, even when AI can do it faster.
• Scaling critical thinking instruction demands new infrastructure, not just new pedagogy. Paul and his team are testing whether platforms like Think Arguments can help instructors manage the feedback and iteration needed to teach reasoning at scale across institutions.
• AI may not replace the professor's role so much as expand it into explicit curation and judgment. In a world where explanations are abundant, the teacher's value shifts toward deciding which frameworks matter and helping students evaluate competing arguments.

Paul Blaschko is an assistant teaching professor at the University of Notre Dame. He teaches God and the Good Life, a course dedicated to asking the big questions about meaning, morality, and faith. He also serves as the Director of the Sheedy Family Program in Economy, Enterprise, and Society, a program devoted to exploring how the humanities can help us find meaning in work. With Meghan Sullivan, he has co-authored The Good Life Method (Penguin Press, 2022), a book about how philosophy can help us live better lives. He is currently working on a book on the philosophy of work (under contract with Princeton University Press), and is the co-founder of a Notre Dame based tech start-up that aims to solve problems with dialogue on the internet.

Key Takeaways:

• Before children can use chatbots, they need a solid concept of real versus not real. Most kindergartners interact with AI through voice and animated characters, adding layers of anthropomorphization that make it nearly impossible for them to distinguish a computer from a person. Megan argues that chatbot-based AI is not developmentally appropriate at this age, and any exposure should be adult-controlled and side-by-side, consistent with American Academy of Pediatrics guidance on co-viewing media.
• The attention economy is becoming a relational economy—and children are the target. The same design logic that removed page numbers from Google search results is now being applied to conversational AI. If a child builds five years of chat history with a platform before adulthood, that relationship becomes a powerful lock-in mechanism. Megan also raises the concern that chat histories are now being used to drive advertising, meaning the tools students use for learning are simultaneously selling to them.
• AI literacy in elementary school means information literacy, not prompt engineering. Rather than teaching young students how to use AI tools directly, Megan focuses on helping them understand who generates information, who validates it, and where AI is already present in their daily lives. During morning announcements, she points out the background remover tool and tells students, "This is AI right here." The goal is building foundational skills for evaluating any new technology, not training on a specific product.
• Every generation of creative technology triggers the same panic—and the pattern holds. Megan draws on her background as a violinist and recording arts student. When Apple's GarageBand launched during her final semester, her synthesizer professor declared it the downfall of music. Instead, it democratized creativity. More people creating doesn't mean everything produced is good, but the tool itself is not the threat. AI follows the same arc.
• Curiosity doesn't need to be taught—it needs to be protected. Young children arrive with natural wonder intact. Megan distinguishes between formal classroom learning and the informal learning space of the library, where autonomy and exploration still drive engagement. The job of early education is not to instill curiosity but to give children frameworks for approaching new things with wonder while still thinking critically, so that instinct survives into adulthood.

Megan E. Barnes is a librarian with over 14 years experience, as well as a Ph.D. student in Learning Technologies at the University of North Texas. Her research focuses on ethical considerations in educational technology adoption and curriculum design. She is currently a research assistant developing curriculum for edge AI and is an ed-tech leader and library director at an independent school. She believes that librarians are information professionals uniquely suited to exploring the intersection of information, technology, and pedagogy.

Key Takeaways:

• Curriculum doesn't absorb AI -- it has to be rebuilt for it. Zendell found that standard learning outcomes written with Bloom's Taxonomy are too broad for an AI system to use as mentoring scaffolds. Her team breaks each outcome into granular component steps, essentially teaching the AI how to guide a student the way an experienced instructor would.
• AI is the first classroom technology to split faculty, students, and administration into opposing camps. Some faculty add zero-tolerance rubric rows while others experiment eagerly. Students range from uneasy to already reliant. Zendell describes a three-way perception gap she hasn't seen with any previous technology, including the transition to online learning.
• Healthcare employers aren't waiting for higher ed to figure this out. Zendell regularly scans job postings for healthcare leadership roles and finds AI literacy and AI tool proficiency appearing with increasing frequency, particularly in informatics, clinical data analytics, and healthcare finance. Her students are asking for these skills and feeling the urgency themselves.
• A student tester changed the entire design process. Zendell recruited an informatics student with an interest in healthcare AI to take each module as a learner before it goes live. That feedback loop -- where the student flags where prompts mislead or where the AI drifts into unproductive territory -- became central to how the team iterates on course design.
• The real danger isn't AI itself -- it's losing the habit of questioning it. Zendell's deepest concern is dependency: that convenience erodes the capacity to critically evaluate AI output. In healthcare especially, where students might default to ChatGPT instead of dedicated clinical interfaces, the gap between accessible and appropriate matters.

Anna Zendell is the program director for the MS in Healthcare Administration program. For over a decade, she has directed degree programs in healthcare administration, health sciences, and public administration. She teaches regularly at the graduate and undergraduate levels. A major emphasis is on ensuring equitable and accessible higher education for students of all abilities by leveraging the power of online learning and the unique attributes that adult learners bring to their learning.

Prior to her academic administration and teaching work, Anna oversaw operations and evaluations for grant-funded research projects focusing on issues such as walkable communities, community health education, and dementia interventions. She developed enduring interdisciplinary partnerships with organizations, local governments, and community members. She provided professional development and continuing education for healthcare professionals. Key focus areas in Anna’s work include fostering meaningful inclusion in workplaces and communities and addressing health disparities, particularly around chronic illness and health promotion.

Anna earned her doctorate and master’s degrees in social work at the University at Albany with a focus on management and community systems.

Key Takeaways:

• Citation can't bridge the gap between AI-generated ideas and their sources. Traditional citation connects ideas to a discrete, traceable origin. AI severs that connection by synthesizing across sources in ways that can't be pinpointed. Weaver notes this is structurally similar to what Western scholarship has long done to traditional and lived knowledge -- and now researchers are experiencing that same disconnection applied to their own work.
• A global AI disclosure standard is actively being built. Weaver is co-leading a large-scale effort with the European Network of Research Integrity Offices, the International Science Council, and the Committee on Publication Ethics to develop a consistent disclosure framework through the World Conferences on Research Integrity. The goal is to stop researchers from having to tailor disclosures to each journal's idiosyncratic requirements.
• AI use in research often falls outside methodology entirely. A researcher translating articles from an unfamiliar language using AI is a real and beneficial use case, but it doesn't fit neatly into a methods section. These peripheral uses still shape how researchers interact with and think about their material, which is exactly why disclosure needs to be broader than methodological reporting.
• Separating the disclosure from the assignment makes students more likely to do it. At the undergraduate level, voluntary disclosure is hard to get. Weaver recommends having students submit a disclosure rubric alongside their assignment in a separate dropbox. This treats disclosure as a professional skill worth practicing on its own, and it gives instructors a reference point if questions arise about how an assignment was produced.
• Authorship will likely settle at the disciplinary level, not the universal one. Weaver is candid that she doesn't have an answer to the authorship question. In qualitative research, she sees coding as irreplaceable human work. In STEM fields, AI-assisted analysis may be more readily accepted. She expects discourse communities will develop their own standards -- but that shouldn't delay building consistent disclosure practices across all of them.

Kari D. Weaver (she/her) holds a B.A. from Indiana University, a M.L.I.S. from the University of Rhode Island, and an Ed.D. in Curriculum and Instruction from the University of South Carolina where her dissertation examined the impact of professional development interventions on academic librarian teaching self-efficacy. She is the Program Manager, Artificial Intelligence and Machine Learning with the Ontario Council of University Libraries on secondment from her permanent role as the Learning, Teaching, and Instructional Design Librarian at the University of Waterloo. Additionally, Dr. Weaver is a continuing sessional faculty member in the Department of Leadership, Higher, and Adult Education at the Ontario Institute for Studies in Education (OISE) at the University of Toronto. Her wide-ranging research background includes study of accessibility for online learning, information literacy, academic integrity, misinformation. She is widely recognized as an expert in AI citation, attribution, and disclosure practices for her development of the Artificial Intelligence Disclosure (AID) Framework and is currently the co-lead of the 2026 World Conferences on Research Integrity Focus Track: Toward a Global Reporting Standard for AI Disclosure in Research.

Key Takeaways:

• AI tools can accelerate diagnostic reasoning—but training still requires struggle. Platforms like Open Evidence can reliably synthesize evidence and suggest diagnoses, but reflexively reaching for them risks stunting the critical thinking that clinical practice demands. The goal should be building heuristics strong enough to stay present with patients, not offloading cognition.
• Transparency about surveillance matters. From Canvas quiz monitoring in college to clinical logging systems, students often don't know what's being tracked. Jack's experience as a TA revealed the extent of visibility administrators have—and raised questions about whether strategic ambiguity helps maintain standards or just breeds anxiety.
• Institutions are starting to take AI governance seriously. Harvard Medical School's AI Sandbox gives trainees access to multiple LLMs in a secure environment that protects curriculum materials and personal data (though it's not HIPAA compliant). This kind of infrastructure signals that leadership is thinking carefully about responsible use.
• Career concerns about AI replacement are real. For students considering imaging-heavy specialties like radiology or radiation oncology, the specter of AI "scope creep" is a recurring topic in conversations with attendings and senior trainees. It's not paranoia—it's a practical factor in career planning.
• Discovery often happens peer-to-peer. Jack first learned about Open Evidence by glancing at a classmate's screen during a simulation exercise. The most impactful tools aren't always introduced through formal curricula—they spread through observation and word of mouth.

John “Jack” Kincaid is a trainee in the Harvard/MIT MD-PhD Program at Harvard Medical School interested in the intersection of diet and disease. Jack received B.A. (Nutritional Biochemistry and Metabolism) and M.S. (Nutrition) degrees from Case Western Reserve University in 2021, where he helped investigate the impact of obesity and obesogenic diet on cancer development in the laboratory of Nathan Berger at Case Comprehensive Cancer Center. Concomitantly, Jack worked with a variety of food access and health literacy groups including CWRU Food Recovery Network and Cooking Matters STL. After leaving CWRU, Jack relocated to the UK to train as a postgraduate in the group of Sir Stephen O’Rahilly at the University of Cambridge Institute of Metabolic Science, studying the neuroendocrine regulation of human appetitive behavior and body weight. As a physician scientist, Jack hopes to leverage basic science and clinical medicine to help address the growing burden of diet-associated illnesses as well as develop safe, effective treatments for metabolic disease.

Key Takeaways:

• Scaffolding matters more than speed. AI tools often give direct answers because that's what they're engineered for. But real learning requires guiding students through the thinking process—something teachers do that AI cannot replicate. Educators should look for tools that provide step-by-step guidance rather than instant solutions.
• Teacher skepticism is healthy—and often fades with use. Most teachers approach AI with skepticism, which is appropriate. But just like PowerPoint and video once were new classroom tools, AI becomes less intimidating through hands-on experience. The recommendation: start with personal, low-stakes use before thinking about classroom implementation.
• Gen Alpha's AI fluency makes teacher presence more important, not less. Students are already fluent AI users. This doesn't diminish the teacher's role—it elevates it. Teachers need to help students navigate bias, develop critical thinking, and understand when AI is appropriate and when it isn't.
• We lack clear guidelines—so educators must set their own. In the absence of federal or state AI policies, individual educators need to establish clear ethical boundaries around data security, safety, and appropriate use. The technology is moving faster than regulation can keep up.
• Creative technologies extend beyond chatbots. From 3D printing and laser cutting that let students build physical objects to AR/VR simulations for medical training, there's a whole landscape of educational technology that emphasizes hands-on learning and creative exploration—not just AI conversation.

Yanni Chen is an Ed.M. candidate at the Harvard Graduate School of Education, where she studies Learning Design, Innovation, and Technology. She earned her B.S. from Boston University, majoring in Public Relations and minoring in Applied Human Development. Her work sits at the intersection of education, product management, AI, XR, and edtech. She focuses on student experience and the design of educational products that foster engagement, growth, and meaningful learning outcomes. Drawing from both her academic training and her work in edtech, Yanni brings the perspective of both a student and a product manager to conversations about teaching, learning, and educational innovation.

Key Takeaways:

• Policies should serve ethics, not replace them. Following rules isn't the same as doing the right thing. When a student has their phone off in their pocket but gets suspended because it's not in their backpack, the punishment no longer serves the policy's original intent of reducing distraction.
• Surveillance culture damages the learning environment. Constant monitoring and zero-tolerance enforcement create an atmosphere where students feel unsafe and disengaged. When students associate school with punishment rather than growth, absenteeism and mental health crises follow naturally.
• Deep literacy is becoming a privilege again. Many students no longer read books from start to finish, instead consuming only passages for standardized tests. This loss of story-based learning strips away both the joy of reading and critical thinking skills.
• AI detection is an unwinnable arms race. The cycle of AI detectors, humanizers, and humanizer-detectors demonstrates a fundamental misunderstanding of how to address academic integrity—tools cannot replace the trust and relationships needed for genuine learning.
• Human connection is irreplaceable in education. Whether it's a professor scrapping class to process a difficult moment with students, or a teacher stepping aside to comfort a struggling child, the most impactful educational experiences come from authentic human relationships—something no technology can replicate.

Jessica Maddry is an educator, strategist, and cofounder of BrightMinds AI, where she works with schools and districts to integrate AI ethically, intentionally, and with educators at the center. Her work focuses on helping systems move beyond hype toward human-centered, purpose-driven design– supporting policy, implementation, and systems change so technology strengthens learning, equity, and student well-being rather than undermines them.

Key Takeaways:

• Voting isn't enough—perspective is. The people impacted by decisions need to be in the rooms where those decisions get made.
• Outsiders can win. Harvard Forward gathered 4,500 signatures on parchment paper, won board seats, and a decade of resistance to divestment collapsed within a year.
• Institutions resist until they can't. Harvard ignored them, then attacked them. It didn't work.
• The model scales. The same playbook worked at Yale and Penn State. One elected climate scientist shifted Penn State's investment policy.
• Soccer has the same problem. 4 million youth players, zero recent youth players in governance. 

Born and raised in México, Nathán Goldberg Crenier is a new(ish) American who is passionate about using the power of democracy and sports to make the world a better place. He has been recognized in the Forbes 30 Under 30 list for his work in progressive politics and nonprofit management, in the New York Times for his work as an electoral organizer and climate advocate, and in the Sports Business Journal New Voices Under 30 list for his work as a soccer executive. He is also a proud recipient of the 2025 Paul & Daisy Soros Fellowship for New Americans as he pursues his JD at Harvard Law School, having graduated with a joint degree in philosophy and statistics from Harvard College, where he played for and captained the D1 varsity men’s soccer team.

Key Takeaways:

• The helicopter problem is real. Using AI to get answers without effort is like taking a helicopter to the top of Mount Everest. You get there, but you missed the point. The grind, the failure, the figuring-it-out—that's where the learning lives.
• Cognitive offloading is already happening to teachers, too. Varun no longer does mental math. He GPS's the airport he's been to hundreds of times. AI is next. The concern isn't hypothetical—it's already underway for him personally.
• Post-COVID is the bigger shift, not post-ChatGPT. Students who came through COVID developed habits of not showing up, not following through, and not asking questions. That behavioral shift is more visible than any change attributable to AI alone.
• The stress is gone—and that's the tell. Before ChatGPT, students peppered him with term paper questions all semester. Now? Silence. They're not less anxious because they're more prepared. They're less anxious because they've already decided how they'll produce the paper.
• There's inherent hypocrisy in the dynamic—and it's worth naming. Using AI to create assignments while discouraging students from using it to complete them isn't perfectly clean. Varun acknowledges it. The distinction is in where the journey matters: for the teacher creating the prompt, or for the student doing the thinking.
• The human value is in the face-to-face. In asynchronous online courses, the line between professor and bot is thin. Where Varun sees his irreplaceable value is in the in-person relationship—lived experience, empathy, career conversations, and the daily modeling of what professional effort actually looks like.

Varun Gupta, aka, The “Knotty” Economist is a dynamic and engaging economics professor with 19 years of experience making complex concepts both accessible and exciting. He has spent his entire career at Wharton County Jr. College (i. e. the “other” Wharton).  Known for his fun and energetic presentation style, and ever present elaborate necktie,  he has delivered insightful talks at conferences, college professional development events, and civic groups—both live and virtual. A passionate educator, Varun specializes in applying fundamental economic principles to real-world decision-making and classroom engagement. Whether tackling macro, micro, or the economics of everyday life, he brings a unique mix of expertise and humor that keeps audiences learning and laughing. When he’s not using economic concepts to explain the world, he spends time catering to his 4 year old golden doodle Cinnamon.

Key Takeaways:

• Follow the 80-20 rule with AI. AI gets you 80% of the way—the other 20% is you adding your own elements. This applies to teachers giving feedback and students creating work.
• Transparency builds trust. When students understand why you're using AI for feedback, they embrace it. Brian's study found 90% of students were in favor once they understood the reasoning.
• Technology can't replace human connection. Students need to learn how to talk to each other, problem-solve collaboratively, and develop empathy—skills that don't come from screens.
• Stamina is the real crisis. Post-COVID students struggle to push through hard things. The growth mindset isn't there. Writing a paragraph makes their hands hurt.
• Teach prompting, not just usage. Focus on prompt engineering—how to get what you want from AI. Experiment with students: change the words, add details, see what happens.
• Standards-based grading may help. With clear standards, teachers can focus instruction, use AI to target specific skills, and have more time for the human elements once mastery is achieved.

Key Takeaways:

• Show students language is already in their lives. From "in lieu of" to Chipotle menus—they're already speaking foreign languages without realizing it. Recognition breeds respect.
• AI can't replace human connection. You can't build trust through a machine. Professional relationships require authentic communication, not a technological relay.
• Create honesty, not surveillance. Use AI openly alongside students and ask only for transparency. When trust flows both ways, students voluntarily admit mistakes—and learn from them.
• Teach students to verify AI output. AI isn't infallible. Once you put something in your paper, you own it—right or wrong.
• Explore AI bias together. "Nobody looks like me" in AI images sparked deeper conversations about bias and better prompting than any lecture could.
• Adapt or be replaced. Teachers won't lose jobs to AI—but they may lose them to teachers who use AI well.

Key Takeaways:

• Misuse is inevitable—guidance is the goal. Middle schoolers can misuse anything from rulers to AI. Instead of trying to eliminate misuse, focus on teaching students how to make tools work for them and guiding them when they stumble.
• Relationships trump detection tools. Teachers who know their students can spot AI-generated work by recognizing when writing doesn't match a student's voice or level—no software required. Treat violations as learning moments, not punishments.
• Give responsibility to gain freedom. When you trust students with responsibility and show them consequences aren't personal, they give you space to actually teach. The more ownership they have, the less you need to police.
• Parents need to parent. The research on smartphones and adolescent brains is irrefutable. Kids don't need iPhones—they need dumb phones, landlines, and parents willing to set boundaries even when their children push back.
• Know the time and place. Technology and AI are fantastic tools that can differentiate instruction, translate languages, and unlock learning. But sometimes you just need human brain power. The skill is knowing when to use tech and when to walk away.

Shon Holland is a science teacher in Dublin, Ohio, with over 22 years of experience in the classroom. Throughout his career, he has been known for blending strong content knowledge with a deep passion for technology and innovation. Shon consistently designs lessons that evolve with the rapidly changing world of educational technology, helping students engage with science through modern tools, data-driven thinking, and real-world applications. His work reflects a commitment to continuous improvement, creative problem-solving, and preparing students to think critically in a technology-rich future.

Key Takeaways:

• Know your students better than any detector. Teachers who build relationships with their students can identify AI-generated work by recognizing changes in sentence length, structure, and voice—no detection tools required.
• Make AI conversations transparent, not secretive. Rather than creating a surveillance culture, openly discuss how AI works, when it's appropriate, and how you can tell when it's been used—students respond better to honesty than to policing.
• Technology should amplify human expression, not replace it. Start with handwritten journals and personal ideas first, then bring in technology as a tool to enhance what students have already created on their own.
• Teaching self-control is lifelong. Help students recognize their own impulse patterns with technology—the habit of drifting to games during a thinking pause—because they'll need to manage this their whole lives.
• Focus on the goal, then find the tool. Instead of teaching specific AI technologies that come and go, teach students to identify what they want to achieve first, then select appropriate tools—this approach works for both students and teachers in professional development.

Kim Cowperthwaite, M.Ed., is an English language arts teacher at Freeport Middle School in Freeport, Maine where she has also worked as a teaching coach and leads a student writing club. She holds a Master’s degree in Literacy Education, a B.A. in Communication, and is a Teacher Consultant for the Maine Writing Project. She is a graduate of the 2017 Peace Literacy Summer Workshop.

Key Takeaways:

• Know your students better than any detector. Teachers who truly understand their students' abilities and writing styles can identify AI-generated work without relying on detection tools—you become the filter.
• Technology can bridge access gaps. For students without resources for tutoring or courses, AI tools can serve as supplementary learning support that was previously unavailable.
• Trust can work as enforcement. Having students acknowledge an honor statement and knowing their baseline abilities can be as effective as surveillance—students often rise to the expectation of integrity.
• Adapt assessments to what you're testing. Use technology-enabled tests when appropriate, but return to pen-and-paper or presentations when the skill being assessed requires it.
• Stay creative ahead of AI. As AI improves, teachers must develop AI-resistant assignments and varied assessment methods rather than abandoning technology entirely.

Dini Arini is a Fulbright Scholar and Ph.D. candidate in Language, Literacy, and Technology at Washington State University. Her research focuses on English as a Second Language (ESL), Computer-Assisted Language Learning (CALL), and the ethical integration of artificial intelligence in education. Before beginning her doctoral studies, she spent nearly a decade as an Associate Professor of English Language Education in Indonesia. Her work explores how AI and educational technology can support multilingual learners, enhance task engagement, and strengthen teacher preparation, particularly in resource-constrained contexts.

Key Takeaways:

• Students often want the boundaries. Research shows many students know phones are bad for them and appreciate when schools take them away—they just can't opt out alone due to social pressure.
• Use the "would I tell my students?" heuristic. Teachers can ethically use AI for lesson prep and practice exercises, but should avoid using it for grading or tasks where students would feel cheated if they knew.
• Relationships require friction. Technology is designed to eliminate friction, but meaningful human connection is inherently awkward and difficult—that's what makes it valuable.
• Consistent enforcement matters more than strict rules. Students accept boundaries when they're applied fairly and uniformly; arbitrary enforcement breeds resentment.
• The next wave isn't intellectual—it's emotional. AI that perfectly imitates consciousness will soon challenge how we help students distinguish between real relationships and convincing simulations.

Nate served as a Fellow in the Harvard Department of Philosophy for over five years, during which time he helped to found ThinkerAnalytix as Lead Instructor and later as COO, among other roles. Nate authored or co-authored many of the core ThinkerAnalytix curriculum and course offerings, including courses for HarvardX, HGSE, and LSAC. Nate currently teaches AP Statistics, AP Calculus AB, and 9th grade Humanities at Boston Trinity Academy in Hyde Park.

Key Takeaways:

• Students still need foundational skills. Just as calculators didn't eliminate the need to understand math, AI doesn't eliminate the need to write well—you can't evaluate output you couldn't create yourself.
• Don't assume students are cheating. Research shows most students use AI ethically; if they're over-relying on it, ask whether assignments are meaningful or just busy work.
• Read the terms and conditions. Before asking students to use any tool, educators must understand what data it collects and where that data goes.
• Use a simple privacy heuristic. If you wouldn't post it on social media, don't put it into a generative AI tool.
• Technology should open doors, not add burdens. Universal Design for Learning means educators do the work to minimize barriers—not hand students another tool and call it support.

Tamsyn Smith is a Senior Learning Designer Team Lead at the University of Southampton, where she has worked for over 13 years supporting staff and students with educational technology and digital capabilities. She works closely with academics staff as well as leading a team delivering training on emerging technologies, including generative AI, and she has particular expertise in inclusive education practices. Tamsyn holds SCMALT membership and is a CMALT assessor, and her work has been recognised through Vice Chancellor's Awards and an AdvanceHE Collaborative Award for Teaching Excellence.

Tamsyn is currently pursuing a PhD in E-Research and Technology Enhanced Learning at Lancaster University, where her research explores how educators can use generative AI to support inclusion through Universal Design for Learning (UDL) implementation. Her work draws on Cultural Historical Activity Theory (CHAT) to examine the complex relationships between technology, pedagogy, and inclusive practice in higher education contexts.

Key Takeaways:

• Meet communities where they already are. Rocket uses WhatsApp because families are already there—no new apps, no tech burden.
• Technology should supplement, not replace, human interaction. APU is capped at 15-20 minutes daily to preserve parent-child engagement.
• Context matters more than content. Effective ed tech adapts cultural references, not just language, for each region.
• Test slowly, learn deeply. Field testing revealed that background noise breaks speech-to-text—rushing would have shipped a broken product.
• Parents are the most transformative tool. AI can model joyful pedagogy, but it can't replace human connection.

Ratna Gill serves as Lead, Special Projects at Rocket Learning. Previously the Head of Government Partnerships at Mumbai-based child safety nonprofit Aangan, she worked with state governments across the country to train school administrators and police officers to create safer communities for children in hotspots for child trafficking. Ratna graduated from Harvard Kennedy School in 2022, where she studied the impacts of parental labor migration on education and development outcomes for adolescents. She has a B.A. in Economics from Harvard College.

Key Takeaways:

• Start small and ground AI in learning goals. Like any instructional design challenge, begin with what you want students to demonstrate—then find where AI fits naturally.
• Use AI to deepen reflection, not replace it. A "reflection bot" that asks follow-up questions can help students dig deeper than a one-time submission ever could.
• Pick two or three tools and stick with them. The app explosion taught us this lesson—chasing every new AI tool leads to burnout, not better teaching.
• AI literacy is discipline-specific. Every field will be impacted differently; the goal isn't generic AI skills but understanding what AI means for your particular context.
• We've been here before. Higher ed has absorbed 80+ technologies since the 1970s. The playbooks exist—we just need to adapt them for this moment.

Dr. Lance Eaton is the Senior Associate Director of AI in Teaching and Learning at Northeastern University. His work engages with the possibility of digital tools for expanding teaching and learning communities while considering the profound issues and questions that educational technologies open up for students, faculty, and higher ed as a whole. He has engaged with scores of higher education institutions about navigating the complexities and possibilities that generative AI represents for us at this moment. His musings, reflections, and ramblings on AI and Education can be found on his blog: 

Key Takeaways:

• There's no shared AI experience. Different platforms and access levels mean students and teachers use fundamentally different tools—making unified policies nearly impossible.
• AI detection is a losing battle. Focus instead on making student thinking visible through conversations and walled-garden tools like Flint.
• "Do no harm" cuts both ways. Schools must prevent misuse while also ensuring students aren't left behind on AI literacy.
• Understand learning science before deploying AI. The key question: are students cognitively offloading the task, or genuinely learning?
• The future is a design problem, not a prediction problem. Decide what you want from AI and build toward it—don't just react to updates.

Justin Cerenzia is the Buckley Executive Director of the Center for Teaching & Learning at The Episcopal Academy, where he leads work at the intersection of cognitive science, teacher inquiry, and AI-informed practice. His work centers on translating research into practical, human-centered tools that improve teaching and learning at scale.

Key Takeaways:

• Schools shouldn't rush to adopt every new technology. Taking time for thoughtful due diligence and involving all stakeholders (teachers, division directors, student support services) leads to better outcomes than being the first to implement.
• Technology decisions should trace back to institutional core values. If a tool can't be connected to values like inquiry or community, it's a hard no.
• Implement a three-tier approach: no access for youngest students, guided access for middle grades, and unfettered access for upper school.  
• Educators must remain conversant in emerging technologies even if they choose not to adopt them. You can't effectively guide students away from tools you don't understand.
• Today's students are building digital communities without the face-to-face foundation previous generations had. Schools must explicitly teach digital norms and social skills that used to develop naturally through in-person interaction.

Joseph Carver is the Associate Head of School at The Meadows School, a Prek-12 independent school in Summerlin, Nevada and the Head of School Elect for the Academy of the Sacred Heart in Bloomfield Hills, Michigan. Previously, Joseph served as the Chief Innovation Officer at The Meadows and the Director of Technology at Carrollton School of the Sacred Heart.

A sought-after speaker and panelist on technology, he has presented at a series of national conferences, including  ATLIS, NDCA, FCIS, and FETC, on topics ranging from social media in schools to ongoing education for non-instructional staff. Joseph has worked alongside the Center for Transformative Teaching and Learning. His focus is on a data-driven, mind and brain education approach to decision-making in all aspects of school life.  Additionally, he is a certified Situational Leadership facilitator and a graduate of both the ATLIS Leadership Institute and the Center for Humane Technology’s Foundations of Humane Technology program. Joseph is also the founder and host of “At the Meadow”, a popular podcast focused on Innovation in independent schools.

Joe’s experience as instructional faculty, coach, and administrator at Carrollton School of the Sacred Heart has profoundly impacted his “mission-informed” approach to technology integration in schools. Joe currently oversees Technology and Innovation, Advancement, Communications, Athletics, and Admissions at The Meadows School. Joe was unanimously selected as a recipient of the 2025 ATLIS PIllar Award, recognizing his many contributions and long-standing service to and leadership within the independent school educational technology community.

Key Takeaways:

• Critical thinking requires explicit instruction—it's not automatically developed through traditional coursework
• Ethical EdTech means putting pedagogical goals first, not engagement metrics or "stickiness"
• Reasoning is inherently difficult and requires sustained practice; shortcuts undermine real learning
• Direct accountability between EdTech developers and educators leads to better products and outcomes
• Mission-driven organizations can prioritize both growth and integrity when the mission guides decision-making

Aidan Kestigian, Ph.D. Chief Operating Officer for ThinkerAnalytix, an education non-profit organization, and a visiting Associate of the Department of Philosophy at Harvard University. Aidan received her Ph.D. in Logic, Computation, and Methodology from Carnegie Mellon University in 2018 and taught logic and ethics to college students for a decade before and during her time at TA. She is the author of Democratic Decisions in a Critical Thinking Crisis (2025).

The podcast will feature conversations with educators, civic leaders, technologists, academics, and students, focusing on two main threads:

1. Ethics of Education Technology – Examining AI, surveillance, privacy, and digital safety in K-12 schools (companion to Priten's upcoming book Ethical Ed Tech)
2. Civics Education – Exploring how to prepare students for meaningful democratic participation

At its core, the show asks: How do we preserve what matters while navigating what's coming? Through thoughtful dialogue and deliberate reflection, Margin of Thought aims to help shape institutions that better serve our children.

Relevant Links:

• listen.priten.org
• ethicaledtech.org