=== AI Literacy Initiatives: Part 2 -- New Capstone Course on AI+Health === Despite the rapid proliferation of AI technologies, the adoption of AI solutions in organizations remains challenging. In many work contexts, it is unclear what problems AI can/should help solve and how AI solutions fit into workflows. There is an increasing demand for AI transformation experts (or forward deployed engineers), who understand not only technical aspects of AI implementation, but also the process of integrating such technologies into workflows and institutional environments. CS497 is a project-based course that teaches students how to tackle AI transformation challenges in organizations. In Fall 2026, CS497 will be held with HLT480 (with Jennifer Yessis, PhD, CE) -- CS and Health students form teams and work with health organizations (e.g., hospitals, primary care clinics, rehabilitation centers, retirement homes) to co-create AI prototypes. Students will have the opportunity to interact with clinicians, administrative staff, patient advocates from a variety of healthcare settings. The course is open to CS students, and students outside of CS with technical backgrounds (e.g., Engineering). Preliminary Course Website: https://lnkd.in/efTzV3h3 Our healthcare partners include: Waterloo Regional Health Network (WRHN), KidsAbility Centre for Child Development, Schlegel Villages, KW4 Ontario Health Team, Ottawa Valley Ontario Health Team and Cambridge North Dumfries Ontario Health Team (CNDOHT). University of Waterloo's social impact incubator, Greenhouse (Tania Del Matto), will be facilitating the interactions between student teams and organizations. Some of the use cases will come from the "AI Literacy through Storytelling" workshops that we are running this summer, where we work with clinicians, administrative staff and patient advocates on developing AI use cases: https://lnkd.in/eamdAjA3 Students -- please help us spread the word by re-posting, and let us know if you are interested by completing this form: https://lnkd.in/evuhU-R8 Stephanie Whitney University of Waterloo Faculty of Health Faculty of Mathematics, University of Waterloo University of Waterloo David R. Cheriton School of Computer Science University of Waterloo Faculty of Engineering Waterloo Women in Computer Science
University of Waterloo Launches Capstone Course on AI in Healthcare
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Hey University of Waterloo students enrolled in Math, Math and Business and Computer Science, there's a unique course being offered this fall where you have the opportunity to engage with several healthcare organizations and work with students in Health Sciences and Public Health to explore and build AI and non-AI solutions that will foster more compassionate care to those facing health and wellness challenges. This course is suited for those students who want to apply their skills and knowledge towards building something of value in our community. Sign-up using the QR code below to learn more about this course. I'm personally looking forward to supporting this course and helping students bring their ideas into action.
Human-AI researcher, Executive Director of the Future of Work Institute, Google Research Chair in the Future of Work and Learning
=== AI Literacy Initiatives: Part 2 -- New Capstone Course on AI+Health === Despite the rapid proliferation of AI technologies, the adoption of AI solutions in organizations remains challenging. In many work contexts, it is unclear what problems AI can/should help solve and how AI solutions fit into workflows. There is an increasing demand for AI transformation experts (or forward deployed engineers), who understand not only technical aspects of AI implementation, but also the process of integrating such technologies into workflows and institutional environments. CS497 is a project-based course that teaches students how to tackle AI transformation challenges in organizations. In Fall 2026, CS497 will be held with HLT480 (with Jennifer Yessis, PhD, CE) -- CS and Health students form teams and work with health organizations (e.g., hospitals, primary care clinics, rehabilitation centers, retirement homes) to co-create AI prototypes. Students will have the opportunity to interact with clinicians, administrative staff, patient advocates from a variety of healthcare settings. The course is open to CS students, and students outside of CS with technical backgrounds (e.g., Engineering). Preliminary Course Website: https://lnkd.in/efTzV3h3 Our healthcare partners include: Waterloo Regional Health Network (WRHN), KidsAbility Centre for Child Development, Schlegel Villages, KW4 Ontario Health Team, Ottawa Valley Ontario Health Team and Cambridge North Dumfries Ontario Health Team (CNDOHT). University of Waterloo's social impact incubator, Greenhouse (Tania Del Matto), will be facilitating the interactions between student teams and organizations. Some of the use cases will come from the "AI Literacy through Storytelling" workshops that we are running this summer, where we work with clinicians, administrative staff and patient advocates on developing AI use cases: https://lnkd.in/eamdAjA3 Students -- please help us spread the word by re-posting, and let us know if you are interested by completing this form: https://lnkd.in/evuhU-R8 Stephanie Whitney University of Waterloo Faculty of Health Faculty of Mathematics, University of Waterloo University of Waterloo David R. Cheriton School of Computer Science University of Waterloo Faculty of Engineering Waterloo Women in Computer Science
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Finding the right balance between academic rigor and real-world application is key to preparing for what’s next. If you’re a University of Waterloo student that is passionate about healthcare, and looking to apply AI tools to have impact in this space, this is a unique opportunity to learn with the Google Chair in the Future of Work and Learning, Edith Law! Jennifer Yessis, PhD, CE is pretty awesome, too! This brand-new capstone course is designed specifically to help you navigate the changing landscape of work. Check it out and see how you can get involved! ⬇️ Velocity Conrad School of Entrepreneurship and Business University of Waterloo Faculty of Mathematics #FutureOfWork #UWaterloo #WorkIntegratedLearning #StudentSuccess #Innovation
Human-AI researcher, Executive Director of the Future of Work Institute, Google Research Chair in the Future of Work and Learning
=== AI Literacy Initiatives: Part 2 -- New Capstone Course on AI+Health === Despite the rapid proliferation of AI technologies, the adoption of AI solutions in organizations remains challenging. In many work contexts, it is unclear what problems AI can/should help solve and how AI solutions fit into workflows. There is an increasing demand for AI transformation experts (or forward deployed engineers), who understand not only technical aspects of AI implementation, but also the process of integrating such technologies into workflows and institutional environments. CS497 is a project-based course that teaches students how to tackle AI transformation challenges in organizations. In Fall 2026, CS497 will be held with HLT480 (with Jennifer Yessis, PhD, CE) -- CS and Health students form teams and work with health organizations (e.g., hospitals, primary care clinics, rehabilitation centers, retirement homes) to co-create AI prototypes. Students will have the opportunity to interact with clinicians, administrative staff, patient advocates from a variety of healthcare settings. The course is open to CS students, and students outside of CS with technical backgrounds (e.g., Engineering). Preliminary Course Website: https://lnkd.in/efTzV3h3 Our healthcare partners include: Waterloo Regional Health Network (WRHN), KidsAbility Centre for Child Development, Schlegel Villages, KW4 Ontario Health Team, Ottawa Valley Ontario Health Team and Cambridge North Dumfries Ontario Health Team (CNDOHT). University of Waterloo's social impact incubator, Greenhouse (Tania Del Matto), will be facilitating the interactions between student teams and organizations. Some of the use cases will come from the "AI Literacy through Storytelling" workshops that we are running this summer, where we work with clinicians, administrative staff and patient advocates on developing AI use cases: https://lnkd.in/eamdAjA3 Students -- please help us spread the word by re-posting, and let us know if you are interested by completing this form: https://lnkd.in/evuhU-R8 Stephanie Whitney University of Waterloo Faculty of Health Faculty of Mathematics, University of Waterloo University of Waterloo David R. Cheriton School of Computer Science University of Waterloo Faculty of Engineering Waterloo Women in Computer Science
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Artificial Intelligence is creating incredible opportunities for middle and high school classrooms, and many educators are eager to learn how to implement it in meaningful, responsible, and student-centered ways. One of the greatest opportunities in education right now is supporting teachers through practical AI training and collaboration that connects directly to real classroom experiences. When teachers are given the tools, guidance, and confidence to use AI effectively, classrooms can become more: ✔ Engaging ✔ Personalized ✔ Creative ✔ Efficient ✔ Future-focused AI has the potential to support: • Differentiation for diverse learners • Standards-aligned instruction • Student critical thinking and problem-solving • Meaningful collaboration and discussion • Teacher creativity and lesson design • Preparation for future careers and technology literacy As a middle school mathematics teacher and doctoral researcher studying AI in education, I am passionate about helping educators see AI not as something to fear, but as a tool that can strengthen teaching and learning when used thoughtfully. Teachers deserve support, training, and practical examples from educators actively working in classrooms and navigating these changes alongside students every day. I would love opportunities to collaborate with schools, districts, and educators to help provide practical AI strategies, classroom ideas, and professional learning experiences that empower both teachers and students. The future of education will not be built by technology alone. It will be built by educators who are willing to lead innovation with purpose, creativity, and human connection. #AIinEducation #EducationalLeadership #TeacherLeadership #EdTech #ProfessionalDevelopment #MiddleSchool #HighSchool #FutureOfEducation #TeachersMatter #InnovationInEducation #STEMEducation
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OSU Learns STEM Students Lean on AI Too Much, New Study Suggests Ways to Keep Them Thinking “The choices educators and AI designers make now will shape not only what students learn, but how they learn to think.” https://lnkd.in/eijMJUi5
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In the words of Rodney Dangerfield "What's a guy gotta do to get a drink around here?" If you haven't checked out the advancements and progress that Ryan Wright and his team at UVA AI is making you definitely should -- https://lnkd.in/gTkh-E3X Seeing some of the scheduled events for both students and faculty should give all of us ideas of how to get universities more involved.
UVA's AI Research Initiative (link in comments)just received the largest grant in The Jefferson Trust history, and I'm proud to be part of the team behind it. The goal is to put AI skills, tools, and infrastructure in the hands of faculty across every school at UVA, not only those already deep in computing. We want UVA to lead in AI-enabled research, and that requires the kind of cross-school collaboration that usually lives in strategy documents but rarely in practice. One piece I'm especially excited about: pilot AI Agentic Labs, where faculty can test new capabilities and students help build the practical research tools that come out of that work. Thank you to Brent Percival and the Jefferson Trust board for backing something this ambitious. The Trust remains one of the most consequential sources of innovation funding at UVA. I'm looking forward to sharing our progress as we get this off the ground. https://lnkd.in/e_MSrEDU
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While few of us have figured out the best way to implement AI in our classrooms, there are some interesting, novel approaches. Come join us for what should be a fascinating conversation about practical approaches for engaging with AI.
Join us for an upcoming webinar on AI in Higher Education featuring Dr. Brian Magerko from Georgia Tech. 📅 May 12 🕛 12:00 – 1:00 PM Talk Title: Building an Ethical Future for AI in Research and Education Join Dr. Brian Magerko, Regents’ Professor at Georgia Tech, for a session on how higher education can lead the way in AI literacy for a just and human-centered future. Drawing on research from the Expressive Machinery Lab, he will explore essential AI competencies and approaches to designing AI literacy in both classrooms and informal learning spaces. The session will highlight practical strategies for integrating AI literacy into curricula while emphasizing accessibility, equity, academic integrity, and critical thinking around the societal impacts of AI. Learn more here: https://lnkd.in/dkb4JjHi
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Most R1 universities now have AI governance committees, published guidelines, and faculty AI training programs. What most do not have is anything that operates at the level where AI integrates into individual faculty research practice. The pattern I see across universities: The AI advisory committee publishes principles. The principles are sound. The AI Academy or workforce training program addresses general AI literacy at scale. The student-facing courses address AI literacy for undergraduates. The individual faculty fellowship program funds researchers to advance their own AI work. What is missing is the operational layer. Specifically: how do five faculty members in a research cluster develop shared workflows for AI-assisted literature synthesis? How do they document AI use consistently for journal submissions? How do they supervise graduate students using AI in their research? How do they prepare grant proposals that meet emerging NSF and NIH AI disclosure requirements? These questions are not addressed by general AI literacy. They are not addressed by individual fellowships. They require small cohort engagement where faculty work together on shared research practice problems. The Penn State Engineering Science and Mechanics pilot ran exactly this: three parallel cohorts of five faculty each, fifteen faculty trained in two weeks, with personalized case studies built from each participant's own published research. The pattern that emerged: the personalization is what made it work. Generic AI training for faculty produces generic outcomes. Personalized AI workflow development produces faculty who actually integrate AI into research practice. The institutional infrastructure is mostly built. The personalized operational layer is what closes the gap. For faculty and chairs reading this: what is the most underserved AI need in your department right now?
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Integrating Artificial Intelligence (AI) and STEM in early childhood education requires effective teacher professional development, yet practical and pedagogical barriers often hinder this. This study addresses this gap by examining the effects of a 13-week online flipped learning model, focused on STEM-AI integration, on 42 preschool teachers. Using an embedded mixed-methods design, the study collected quantitative data (pre/post-test scales) and qualitative data (semistructured interviews) to measure changes in AI awareness, innovative and computational thinking skills, and teacher opinions. Quantitative results showed that the intervention had a significant positive effect on all three measured skills. Qualitative findings corroborated this, revealing that the training met teachers’ expectations, expanded their understanding of AI to include complex concepts like machine learning, and critically, highlighted their significant concerns regarding data privacy and the ethical use of student data. This study provides novel evidence for the effectiveness of an online flipped learning model in developing preschool teachers’ advanced pedagogical and technical competencies. Findings underscore the model’s practical utility but also signal an urgent need for policies and training that directly address educators’ ethical and security concerns.
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The Johns Hopkins University - Carey Business School professors Iike Toby Gordon are embracing AI. What a fun way to leverage its power for learning!
***Embracing AI @Johns Hopkins *** While many educators lament the impact of AI on teaching and assessing student learning, I have had a very different experience this year. A year ago, we focused on preventing students from “cheating” with AI, but what quickly became apparent was the problem included us, the faculty. We needed to rapidly learn, embrace, and integrate AI into our teaching rather than spend our energy policing its use. Assignments that can easily be generated by AI are waste of everyone’s time except the AI agent. So instead of restricting AI, I incorporate it intentionally. If students now have access to vastly expanded research and analytical capabilities, including AI, then we should raise the level of complexity, synthesis, creativity, and judgment we expect from them. Traditional PowerPoint presentations have largely disappeared from my classes. Instead, students create board games depicting the U.S. healthcare system, virtual museums explaining international health systems, and documentary-style films inspired by The Wire that explore social determinants of health. I also run Shark Tank sessions with outside experts and incorporate design-thinking methods such as journey mapping, persona development, customer discovery, and post-mortem reflections. Recently, in my International Health Systems course, students created fictional healthcare journeys of a patient navigating care across healthcare systems. The resulting papers were extraordinary: insightful, authentic, analytically sophisticated, and genuinely enjoyable to read. Students synthesized complex policy content while engaging deeply with the patient experience and they said it was really fun. My “AHA" moment this year has been realizing that experimentation with AI is now a necessary part of modern pedagogy. Rather than prevent students from using AI, we should require students to combine AI-supported exploration with creativity, synthesis, judgment, and authentic human insight. AI raises the bar for all of us. With it, we can expect more creativity, more ambitious assignments, richer exploration, and deeper synthesis. Do not fear AI, embrace it! #AI #creativity #Carey #JHU #pedagogy
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DEPTH VS QUANTITY: My inbox is currently flooded with requests from tech startups asking for my clinical expertise as a Nurse Practitioner to help train their healthcare AI models. I recently read the White House Executive Order on "Advancing AI Education." https://lnkd.in/gE3_jdng ficial-intelligence-education-for-american-youth/1/6 The order mandates a massive push to train K-12 students with corporate "AI certificates" to prepare them for the future workforce. Looking at the current AI paradox, I can’t help but feel we are trying to solve a 21st-century technological shift using a 20th-century vocational mindset. Here is the elephant in the room: By pushing students into rapid, task-based AI certifications, we are building an industrial-style pipeline to train humans for the exact entry-level knowledge jobs that machine learning is on the verge of automating. This would be like training students to use a card catalog system before the launch of google. As an NP being asked to train my own "digital replacement," I see the exact same trap in healthcare. The promise of AI in medicine is often pitched as pure capacity and efficiency: “If AI does the charting and triage, providers can see twice as many patients!” But this completely misses the point of healing. The future of healthcare, and education, should not be about using technology to maximize throughput and quantity. It must be about using technology to protect and enhance depth and connection. If AI can handle the heavy lifting of data processing, differential diagnoses, and administrative bloat, the answer isn’t to force providers into unsafe, assembly-line workloads. The answer is to give that time back to the humans in the room. AI cannot look a terrified patient in the eye and build trust. It cannot read the subtle body language that reveals a hidden social barrier to treatment. It cannot alleviate anxiety, build long-term rapport, or navigate the complex human emotions tied to a difficult diagnosis. Those are the un-automatable skills. Whether we are talking about the future of K-12 education or the future of clinical practice, our fight shouldn’t be against the technology itself. Our fight must be to ensure the efficiency gained by AI is spent preserving our humanity, not erasing it. How is your industry navigating the pressure to choose between AI-driven quantity versus human-driven quality?
