Tag: Innovation

  • Weaving digital citizenship into edtech innovation

    Weaving digital citizenship into edtech innovation

    Key points:

    What happens when over 100 passionate educators converge in Chicago to celebrate two decades of educational innovation? A few weeks ago, I had the thrilling opportunity to immerse myself in the 20th anniversary of the Discovery Educator Network (the DEN), a week-long journey that reignited my passion for transforming classrooms.

    From sunrise to past sunset, my days at Loyola University were a whirlwind of learning, laughter, and relentless exploration. Living the dorm life, forging new connections, and rekindling old friendships, we collectively dove deep into the future of learning, creating experiences that went far beyond the typical professional development.

    As an inaugural DEN member, the professional learning community supported by Discovery Education, I was incredibly excited to return 20 years after its founding to guide a small group of educators through the bountiful innovations of the DEN Summer Institute (DENSI). Think scavenger hunts, enlightening workshops, and collaborative creations–every moment was packed with cutting-edge ideas and practical strategies for weaving technology seamlessly into our teaching, ensuring our students are truly future-ready.

    During my time at DENSI, I learned a lot of new tips and tricks that I will pass on to the educators I collaborate with. From AI’s potential to the various new ways to work together online, participants in this unique event learned a number of ways to weave digital citizenship into edtech innovation. I’ve narrowed them down to five core concepts; each a powerful step toward building future-ready classrooms and fostering truly responsible digital citizens.

    Use of artificial intelligence

    Technology integration: When modeling responsible AI use, key technology tools could include generative platforms like Gemini, NotebookLM, Magic School AI, and Brisk, acting as ‘thought partners’ for brainstorming, summarizing, and drafting. Integration also covers AI grammar/spell-checkers, data visualization tools, and feedback tools for refining writing, presenting information, and self-assessment, enhancing digital content interaction and production.

    Learning & application: Teaching students to ethically use AI is key. This involves modeling critical evaluation of AI content for bias and inaccuracies. For instance, providing students with an AI summary of a historical event to fact-check with credible sources. Students learn to apply AI as a thought partner, boosting creativity and collaboration, not replacing their own thinking. Fact-checking and integrating their unique voices are essential. An English class could use AI to brainstorm plot ideas, but students develop characters and write the narrative. Application includes using AI for writing refinement and data exploration, fostering understanding of AI’s academic capabilities and limitations.

    Connection to digital citizenship: This example predominantly connects to digital citizenship. Teaching responsible AI use promotes intellectual honesty and information literacy. Students can grasp ethical considerations like plagiarism and proper attribution. The “red, yellow, green” stoplight method provides a framework for AI use, teaching students when to use AI as a collaborator, editor, or thought partner–or not at all.This approach cultivates critical thinking and empowers students to navigate the digital landscape with integrity, preparing them as responsible digital citizens understanding AI’s implications.

    Digital communication

    Technology integration: Creating digital communication norms should focus on clarity with visuals like infographics, screenshots, and video clips. Canva is a key tool for a visual “Digital Communication Agreement” defining online interaction expectations. Include student voice by the integration and use of pictures and graphics to illustrate behaviors and potentially collaborative presentation / polling tools for student involvement in norm-setting.

    Learning & application: Establishing clear online interaction norms is the focus of digital communication. Applying clear principles teaches the importance of visuals and setting communication goals. Creating a visual “Digital Communication Agreement” with Canva is a practical application where students define respectful online language and netiquette. An elementary class might design a virtual classroom rules poster, showing chat emojis and explaining “think before you post.” Using screenshots and “SMART goals” for online discussions reinforces learning, teaching constructive feedback and respectful debate. In a middle school science discussion board, the teacher could model a respectful response like “I understand your point, but I’m wondering if…” This helps students apply effective digital communication principles.

    Connection to digital citizenship: This example fosters respectful communication, empathy, and understanding of online social norms. By creating and adhering to a “Digital Communication Agreement,” students develop responsibility for online interactions. Emphasizing respectful language and netiquette cultivates empathy and awareness of their words’ impact. This prepares them as considerate digital citizens, contributing positively to inclusive online communities.

    Content curation

    Technology integration: For understanding digital footprints, one primary tool is Google Drive when used as a digital folder to curate students’ content. The “Tech Toolbox” concept implies interaction with various digital platforms where online presence exists. Use of many tools to curate content allows students to leave traces on a range of technologies forming their collective digital footprint.

    Learning & application: This centers on educating students about their online presence’s permanence and nature. Teaching them to curate digital content in a structured way, like using a Google Drive folder, is key. A student could create a “Digital Portfolio” in Google Drive with online projects, proud social media posts, and reflections on their public identity. By collecting and reviewing online artifacts, students visualize their current “digital footprint.” The classroom “listening tour” encourages critical self-reflection, prompting students to think about why they share online and how to be intentional about their online identity. This might involve students reviewing anonymized social media profiles, discussing the impression given to future employers.

    Connection to digital citizenship: This example cultivates awareness of online permanence, privacy, responsible self-presentation, and reputation management. Understanding lasting digital traces empowers students to make informed decisions. The reflection process encourages the consideration of their footprint’s impact, fostering ownership and accountability for online behavior. This helps them become mindful, capable digital citizens.

    Promoting media literacy

    Technology integration: One way to promote media literacy is by using “Paperslides” for engaging content creation, leveraging cameras and simple video recording. This concept gained popularity at the beginning of the DEN through Dr. Lodge McCammon. Dr. Lodge’s popular 1-Take Paperslide Video strategy is to “hit record, present your material, then hit stop, and your product is done” style of video creation is something that anyone can start using tomorrow. Integration uses real-life examples (likely digital media) to share a variety of topics for any audience. Additionally, to apply “Pay Full Attention” in a digital context implies online viewing platforms and communication tools for modeling digital eye contact and verbal cues.

    Learning & application: Integrating critical media consumption with engaging content creation is the focus. Students learn to leverage “Paperslides” or another video creation method to explain topics or present research, moving beyond passive consumption. For a history project, students could create “Paperslides” explaining World War II causes, sourcing information and depicting events. Learning involves using real-life examples to discern credible online sources, understanding misinformation and bias. A lesson might show a satirical news article, guiding students to verify sources and claims through their storyboard portion. Applying “Pay Full Attention” teaches active, critical viewing, minimizing distractions. During a class viewing of an educational video, students could pause to discuss presenter credentials or unsupported claims, mimicking active listening. This fosters practical media literacy in creating and consuming digital content.

    Connection to digital citizenship: This example enhances media literacy, critical online information evaluation, and understanding persuasive techniques. Learning to create and critically consume content makes students informed, responsible digital participants. They identify and question sources, essential for navigating a digital information-saturated world. This empowers them as discerning digital citizens, contributing thoughtfully to online content.

    Collaborative problem-solving

    Technology integration: For practicing digital empathy and support, key tools are collaborative online documents like Google Docs and Google Slides. Integration extends to online discussion forums (Google Classroom, Flip) for empathetic dialogue, and project management tools (Trello, Asana) for transparent organization. 

    Learning & application: This focuses on developing effective collaborative skills and empathetic communication in digital spaces. Students learn to work together on shared documents, applying a “Co-Teacher or Model Lessons” approach where they “co-teach” each other new tools or concepts. In a group science experiment, students might use a shared Google Doc to plan methodology, with one “co-teaching” data table insertion from Google Sheets. They practice constructive feedback and model active listening in digital settings, using chat for clarification or emojis for feelings. The “red, yellow, green” policy provides a clear framework for online group work, teaching when to seek help, proceed cautiously, or move forward confidently. For a research project, “red” means needing a group huddle, “yellow” is proceeding with caution, and “green” is ready for review.

    Connection to digital citizenship: This example is central to digital citizenship, developing empathy, respectful collaboration, and responsible problem-solving in digital environments. Structured online group work teaches how to navigate disagreements and offers supportive feedback. Emphasis on active listening and empathetic responses helps internalize civility, preparing students as considerate digital citizens contributing positively to online communities.

    These examples offer a powerful roadmap for cultivating essential digital citizenship skills and preparing all learners to be future-ready. The collective impact of thoughtfully utilizing these or similar approaches , or even grab and go resources from programs such as Discovery Education’s Digital Citizenship Initiative, can provide the foundation for a strong academic and empathetic school year, empowering educators and students alike to navigate the digital world with confidence, integrity, and a deep understanding of their role as responsible digital citizens.

    In addition, this event reminded me of the power of professional learning communities.  Every educator needs and deserves a supportive community that will share ideas, push their thinking, and support their professional development. One of my long-standing communities is the Discovery Educator Network (which is currently accepting applications for membership). 

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  • ARTIFICIAL INTELLIGENCE AND THE FUTURE OF HBCUS: A CALL FOR INVESTMENT, INNOVATION, AND INCLUSION

    ARTIFICIAL INTELLIGENCE AND THE FUTURE OF HBCUS: A CALL FOR INVESTMENT, INNOVATION, AND INCLUSION

    Dr. Emmanuel LalandeHistorically Black Colleges and Universities (HBCUs) have always stood on the frontlines of educational equity, carving pathways to excellence for generations of Black students against overwhelming odds. Today, as higher education faces a shift driven by technology, declining enrollment, and resource disparities, a new opportunity emerges: the power of Artificial Intelligence (AI) to reshape, reimagine, and reinforce the mission of HBCUs.

    From admissions automation and predictive analytics to personalized learning and AI-powered tutoring, artificial intelligence is no longer theoretical, it is operational. At large institutions, AI-driven chatbots and enrollment algorithms have already improved student engagement and reduced summer melt. Meanwhile, HBCUs, particularly smaller and underfunded ones, risk being left behind.

    The imperative for HBCUs to act now is not about chasing trends about survival, relevance, and reclaiming leadership in shaping the future of Black education.

    AI as a Force Aligned with the HBCU Mission

    Artificial intelligence, when developed and implemented with intention and ethics, can be one of the most powerful tools for educational justice. HBCUs already do more with less. They enroll 10% of Black students in higher education and produce nearly 20% of all Black graduates. These institutions are responsible for over 25% of Black graduates in STEM fields, and they produce a significant share of Black teachers, judges, engineers, and public servants.

    The power of AI can amplify this legacy.

    • Predictive analytics can flag at-risk students based on attendance, financial aid gaps, and academic performance, helping retention teams intervene before a student drops out.
    • AI chatbots can provide round-the-clock support to students navigating complex enrollment, financial aid, or housing questions.
    • AI tutors and adaptive platforms can meet students where they are, especially for those in developmental math, science, or writing courses.
    • Smart scheduling and resource optimization tools can help HBCUs streamline operations, offering courses more efficiently and improving completion rates.

    For small HBCUs with limited staff, outdated technology, and tuition-driven models, AI can serve as a strategic equalizer. But accessing these tools requires intentional partnerships, resources, and cultural buy-in.

    The Philanthropic Moment: A Unique Opportunity

    The recent announcement from the Bill & Melinda Gates Foundation that it plans to spend its entire $200 billion endowment by 2045 presents a monumental opportunity. The foundation has declared a sharpened focus on “unlocking opportunity” through education, including major investments in AI-powered innovations in K-12 and higher education, particularly in mathematics and student learning platforms.

    One such investment is in Magma Math, an AI-driven platform that helps teachers deliver personalized math instruction. The foundation is also actively funding research and development around how AI can close opportunity gaps in postsecondary education and increase economic mobility. Their call for “AI for Equity” aligns with the HBCU mission like no other.

    Now is the time for HBCUs to boldly approach philanthropic organizations like the Gates Foundation as strategic partners capable of leading equity-driven AI implementation. 

    Other foundations should follow suit. Lumina Foundation, Carnegie Corporation, Kresge Foundation, and Strada Education Network have all expressed interest in digital learning and postsecondary success. A targeted, collaborative initiative to equip HBCUs with AI infrastructure, training, and research capacity could be transformative.

    Tech Industry Engagement: From Tokenism to True Partnership

    • The tech industry has begun investing in HBCUs, but more is needed.
    • OpenAI recently partnered with North Carolina Central University (NCCU) to support AI literacy through its Institute for Artificial Intelligence and Emerging Research. The vision includes scaling support to other HBCUs.
    • Intel has committed $750,000 to Morgan State University to advance research in AI, data science, and cybersecurity.
    • Amazon launched the Educator Enablement Program, supporting faculty at HBCUs in learning and teaching AI-related curricula.
    • Apple and Google have supported HBCU initiatives around coding, machine learning, and entrepreneurship, though these efforts are often episodic or branding-focused. What’s needed now is sustained, institutional investment.
    • Huston-Tillotson University hosted an inaugural HBCU AI Conference and Training Summit back in April, bringing together AI researchers, students, educators, and industry leaders from across the country. This gathering focused on building inclusive pathways in artificial intelligence, offering interactive workshops, recruiter engagement, and a platform for collaboration among HBCUs, community colleges, and major tech firms.

    We call on Microsoft, Salesforce, Nvidia, Coursera, Anthropic, and other major EdTech firms to go beyond surface partnerships. HBCUs are fertile ground for workforce development, AI research, and inclusive tech talent pipelines. Tech companies should invest in labs, curriculum development, student fellowships, and cloud infrastructure, especially at HBCUs without R1 status or multi-million-dollar endowments.

    A Framework for Action Across HBCUs

    To operate AI within the HBCU context, a few strategic steps can guide implementation:

    1. AI Capacity Building Across Faculty and Staff

    Workshops, certification programs, and summer institutes can train faculty to integrate AI into pedagogy, advising, and operations. Staff training can ensure AI tools support, not replace, relational student support.

    2. Student Engagement Through Research and Internships

    HBCUs can establish AI learning hubs where students gain real-world experience developing or auditing algorithms, especially those designed for educational equity.

    3. AI Governance

    Every HBCU adopting AI must also build frameworks for data privacy, transparency, and bias prevention. As institutions historically rooted in justice, HBCUs can lead the national conversation on ethical AI.

    4. Regional and Consortial Collaboration

    HBCUs can pool resources to co-purchase AI tools, share grant writers, and build regional research centers. Joint proposals to federal agencies and tech firms will yield greater impact.

    5. AI in Strategic Planning and Accreditation

    Institutions should embed AI as a theme in Quality Enhancement Plans (QEPs), Title III initiatives, and enrollment management strategies. AI should not be a novelty, it should be a core driver of sustainability and innovation.

    Reclaiming the Future

    HBCUs were built to meet an unmet need in American education. They responded to exclusion with excellence. They turned marginalization into momentum. Today, they can do it again, this time with algorithms, neural networks, and digital dashboards.

    But this moment calls for bold leadership. We must go beyond curiosity and into strategy. We must demand resources, form coalitions, and prepare our institutions not just to use AI, but to shape it.

    Let them define what culturally competent, mission-driven artificial intelligence looks like in real life, not in theory. 

    And to the Gates Foundation, Intel, OpenAI, Amazon, and all who believe in the transformative power of education: invest in HBCUs. Not as charity, but as the smartest, most impactful decision you can make for the future of American innovation.

    Because when HBCUs lead, communities rise. And with AI in our hands, the next 
    level of excellence is well within reach.

    Dr. Emmanuel Lalande currently serves as Vice President for Enrollment and Student Success and Special Assistant to the President at Voorhees University.

     

     

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  • The AI balancing act: universities, innovation and the art of not losing the plot

    The AI balancing act: universities, innovation and the art of not losing the plot

    • By Professor Alejandro Armellini, Dean of Education and Digital Innovation at the University of Portsmouth.

    Universities want to be at the cutting edge of knowledge creation, but many are grappling with a paradox: how to harness the potential of AI while minimising its pitfalls. Done well, generative AI can help institutions run more efficiently, enhance teaching quality and support students in new and exciting ways. Done poorly, it can generate misinformation, introduce bias and make students (and staff) over-reliant on technology they do not fully understand. The challenge is not whether to use AI but how to make it work for human-driven, high-quality education.

    Across the sector, institutions are already putting AI to work in ways that go far beyond administrative efficiencies. At many universities, AI-driven analytics are helping identify students at risk of disengagement before they drop out. By analysing attendance, engagement and performance data, tutors can intervene earlier, offering personalised support before problems escalate. Others have deployed AI-powered feedback systems that provide students with instant formative feedback on their writing. The impact? Students who actually improve before their assignments are due, rather than after they’ve been graded.

    Concerns about the accuracy, transparency and provenance of AI tools have been well documented. Many of them operate as ‘black boxes’, making it difficult to verify outputs or attribute sources. These challenges run counter to academic norms of evidence, citation and rigour. AI tools continue to occupy a liminal space: they promise and deliver a lot, but are not yet fully trusted. AI can get things spectacularly wrong. AI-powered recruitment tools have been found to be biased against women and minority candidates, reinforcing rather than challenging existing inequalities. AI-driven assessment tools have been criticised for amplifying bias, grading students unfairly or making errors that, when left unchallenged, can have serious consequences for academic progression.

    With new applications emerging almost daily, it’s becoming harder to assess their quality, reliability and appropriateness for academic use. Some institutions rush headlong into AI adoption without considering long-term implications, while others hesitate, paralysed by the sheer number of options, risks and potential costs. Indeed, a major barrier to AI adoption at all levels in higher education is fear: fear of the unknown, fear of losing control, fear of job displacement, fear of fostering metacognitive laziness. AI challenges long-held beliefs about authorship, expertise and what constitutes meaningful engagement with learning. Its use can blur the boundaries between legitimate assistance and academic misconduct. Students express concerns about being evaluated by algorithms rather than humans. These fears are not unfounded, but they must be met with institutional transparency, clear communication, ethical guidelines and a commitment to keeping AI as an enabler, not a replacement, for human judgment and interaction. Universities are learning too.

    No discussion on AI in universities would be complete without addressing the notion of ‘future-proofing’. The very idea that we can somehow freeze a moving target is, at best, naive and, at worst, an exercise in expensive futility. Universities drafting AI policies today will likely find them obsolete before the ink has dried. Many have explicitly reversed earlier AI policies. That said, having an AI policy is not without merit: it signals an institutional commitment to ethical AI use, academic integrity and responsible governance. The trick is to focus on agile, principle-based approaches that can adapt as AI continues to develop. Over-regulation risks stifling innovation, while under-regulation may lead to confusion or misuse. A good AI policy should be less about prediction and more about preparation: equipping staff and students with the skills and capabilities to navigate an AI-rich world, while creating a culture that embraces change. Large-scale curriculum and pedagogic redesign is inevitable.

    Where does all this leave us? Universities must approach AI with a mix of enthusiasm and caution, ensuring that innovation does not come at the expense of academic integrity or quality. Investing in AI fluency (not just ‘literacy’) for staff and students is essential, as is institutional clarity on responsible AI use. Universities should focus on how AI can support (not replace) the fundamental principles of good teaching and learning. They must remain committed to the simple but powerful principle of teaching well, consistently well: every student, every session, every time.

    AI is a tool – powerful, perhaps partly flawed, but full of potential. It is the pocket calculator of the 1970s. How universities wield it will determine whether it leads to genuine transformation or a series of expensive (and reputationally risky) missteps. The challenge, then, is to stay in control, keep the focus on successful learning experiences in their multiple manifestations, and never let AI run the show alone. After all, no algorithm has yet mastered the art of handling a seminar full of students who haven’t done the reading.

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  • Research and innovation in the industrial strategy

    Research and innovation in the industrial strategy

    The UK needs a plan for growth and innovation – an industrial strategy is a way of picking winners in terms of sector investments and prioritisation.

    Today’s iteration (the fourth in recent times, with Theresa May’s government providing the previous one) chooses eight high-potential sectors to prioritise funding and skills interventions, with the overall intention of encouraging private investment over the long term.

    Picking winners for the long term

    The choices are the important bit – as the strategy itself notes

    Past UK industrial strategies have not lasted because they have either refused to make choices or have failed to back their choices up by reallocating resources and driving genuine behaviour change in both government and industry.

    And there are clear commonalities between previous choices and the new ones. Successive governments have prioritised “clean growth”, data and technology, and health – based both on the potential for growth and the impact that investment could have.

    What is different this time is the time scales on which the government is thinking – much of the spending discussed today is locked in to the next five years of departmental spending via the spending review, and of course we have those infamous 10-year research and development plans in some areas: the Aerospace Technology Institute (linked to Cranfield), the National Quantum Computing Centre (at the Harwell STFC campus), the Laboratory of Molecular Biology (MRC supported at Cambridge), and the new DRIVE35 automotive programme are the first to be announced.

    The headlines

    My colleague Michael Salmon has analysed the skills end of the strategy elsewhere, here we are looking at investments in research and innovation – both in the industrial strategy itself and the five (of eight) IS-8 sector plans published alongside it (Advanced manufacturing, Creative industries, Clean energy, Digital and technologies, Professional and business services – with defence, financial services, and life sciences pending)

    Government funded innovation programmes will prioritise the IS-8, within a wider goal to focus all of research and development funding on long term economic growth. This explicitly does not freeze out curiosity delivered research – but it is clear that there will be a focus on the other end of the innovation pipeline.

    At a macro level UKRI will be pivoting financial support towards the IS-8 sectors – getting new objectives around innovation, commercialisation, and scale-up. If you are thinking that this sounds very Innovate UK you would be right, the Catapult Network will also get tweaks to refocus.

    The £500m Local Innovation Partnerships Fund is intended to generate a further £1bn of additional investment and £700m of value to local economies, and there are wider plans to get academia and industry working together: a massive expansion in supercomputer resources (the AI research resource, inevitably) and a new Missions Accelerator programme supported by £500m of funding. And there’s the Sovereign AI Unit within government (that’s another £500m of industry investments) in “frontier AI”. On direct university allocation we get the welcome news that the Higher Education Innovation Fund (HEIF) is here to stay.

    There’s an impressively hefty chunk of plans for getting the most out of public sector data – specifically the way in which government (“administrative”) data can be used by research and industry. Nerds like me will have access to a wider range of data under a wider range of licenses – the government will also get better at valuing data in order to maximise returns for the bits it does sell, and there will be ARDN-like approaches available to more businesses to access public data in a safe and controlled way (if parliamentary time allows, legislation will be brought forward) – plus money (£12m) for data sharing infrastructure and the (£100m) national data library.

    By sector

    The sector plans themselves have a slant towards technology adoption (yes even the creative sector – “createch” is absolutely a thing). But there’s plenty of examples throughout of specific funding to support university-based research, innovation, and bringing discoveries to market – alongside (as you’ll see from Michael’s piece) plenty on skills.

    Clearly the focus varies between sectors. For example, there will be a specific UKRI professional and business services innovation programme; while digital and technologies work is more widely focused on the entirety of the UK’s research architecture: there we get promises of “significant” investment via multiple UKRI and ARIA programmes alongside a £240m focus on advanced communication technologies (ACT). The more research-focused sectors also get the ten-year infrastructure-style investments like the £1bn on AI research resources.

    Somewhat surprisingly clean energy is not one of the big research funding winners – there’s just £20m over 7 years for the sustainable industrial futures programme (compare the £1bn energy programme in the last spending review). With sustainability also being a mission it also gets a share of the missions accelerator programme (£500m), but for such a research-intensive field that doesn’t feel like a lot.

    The creative industries, on the other hand, get £100m via UKRI over the spending review period – there’s a specific creative industries research and development plan coming later this year, alongside (£500m) creative clusters, and further work on measuring the output of the sector.  And “createch” (the increasingly technical underpinnings of the creative industries) is a priority too.

    It’s also worth mentioning advanced manufacturing as a sector where business and industry are major funders. Here the government is committing “up to £4.3bn” for the sector, with £2.8bn of this going to research and development. Key priorities include work on SME technology adoption, and advanced automotive technologies – the focus is very much on commercialisation, and there is recognition that private finance needs to be a big part of this.

    Choice cuts?

    The IS-8 are broadly drawn – it is difficult to think of an academic research sector that doesn’t get a slice. But there will be a shaking out of sub-specialisms, and the fact that one of the big spenders (health and medicine) is currently lacking detail doesn’t help understand how the profile of research within that area will shift during the spending review period.

    Industrial policy has always been a means of picking winners – focusing necessarily limited investment on the places it will drive benefits. The nearly flat settlement for UKRI in the spending review was encouraging, but it is starting to feel like new announcements like these need to be seen both as net benefits (for the lucky sectors) and funding cuts (for the others).

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  • The identity crisis of teaching and learning innovation

    The identity crisis of teaching and learning innovation

    Universities love to talk about innovation. Pedagogical innovation is framed as a necessity in an era of rapid change, yet those expected to enact it – academics – are caught in an identity crisis.

    In our research on post-pandemic pedagogical innovation, we found that the decision to engage with or resist innovation is not just about workload, resources, or institutional strategy. It’s about identity – who academics see themselves as, how they are valued within their institutions, and what risks they perceive in stepping beyond the status quo.

    Academics are asked to be both risk-taking pedagogical entrepreneurs and compliant employees within increasingly bureaucratic, metric-driven institutions. This paradox creates what we call the moral wiggle room of innovation – a space where educators justify disengagement, not necessarily because they oppose change, but because their institutional environment does not meaningfully reward it.

    The paradox of pedagogical innovation

    During the pandemic, universities celebrated those who embraced new digital tools, hybrid learning, and flexible teaching formats. “Necessity breeds innovation” became the dominant narrative. Yet, as the crisis has subsided, many of these same institutions have reverted to rigid processes, managerial oversight, and bureaucratic hurdles, making innovation feel like an uphill battle.

    On paper, universities support innovation. Education strategies abound with commitments to “transformative learning experiences” and “sector-leading digital education.” However, in practice, academics face competing pressures – expectations to drive innovation while being weighed down by institutional inertia.

    The challenge is not just about introducing innovation but sustaining it in ways that foster long-term change. While institutions may advocate for pedagogical innovation, the reality for many educators is a system that does not provide the necessary time, support, or recognition to make such innovation a viable, sustained effort.

    The result? Many feel disillusioned. As one academic in our research put it:

    I definitely think there’s a drive to be more innovative, but it feels like a marketized approach. It’s not tangible – I can’t say, ‘Oh, they’re really supporting me to be more innovative.’ There’s no clear pathway, no structured process.
    Academic at a post-92 university

    For some, engaging in pedagogical innovation is a source of professional fulfilment. For others, it is a career gamble. Whether academics choose to innovate or resist depends largely on how their identity aligns with institutional structures, career incentives, and personal values.

    Three identity tensions shaping pedagogical innovation

    Regulated versus self-directed identity Institutions shape identity through expectations: teaching excellence frameworks, fellowship accreditations, and workload models dictate what “counts” in an academic career. Yet, many educators see their professional identity as self-driven – rooted in disciplinary expertise and a commitment to students. When institutional definitions of innovation clash with personal motivations, resistance emerges.

    As one participant put it:

    When you’re (personally) at the forefront of classroom innovation…you’re constantly looking outwards for ideas. Within the institution, there isn’t really anyone I can go to and say, ‘What are you doing differently?’ It’s more about stumbling upon people rather than having a proactive approach to being innovative. I think there’s a drive for PI, but it feels like a marketised approach.
    Academic at a post-92 university

    For some, innovation is an extension of their identity as educators; for others, it is a compliance exercise – an expectation imposed from above rather than a meaningful pursuit.

    This tension is explored in Wonkhe’s discussion of institutional silos, which highlights how universities often create structures that inadvertently restrict collaboration and cross-disciplinary innovation, making it harder for educators to engage with meaningful change.

    Risk versus reward in academic careers Engaging in pedagogical innovation takes time and effort. For those on teaching and scholarship contracts, it is often an expectation. For research and scholarship colleagues, it is rarely a career priority.

    Despite strategic commitments to pedagogical innovation, career incentives in many institutions still favour traditional research outputs over pedagogical experimentation. The opportunity cost is real – why invest in something that holds little weight in promotions or workload models?

    As one academic reflected:

    I prioritise what has immediate impact. Another teaching award isn’t a priority. Another publication directly benefits my CV.

    Senior leader at a Russell Group university

    Until pedagogical I is properly recognised in career progression, it will remain a secondary priority for many. As explored on Wonkhe here, the question is not just whether innovation happens but whether institutions create environments that allow it to spread. Without clear incentives, pedagogical innovation remains the domain of the few rather than an embedded part of academic practice.

    Autonomy versus bureaucracy Academics value autonomy. It is one of the biggest predictors of job satisfaction in higher education. Yet pedagogical innovation is often entangled in institutional bureaucracy (perceived or real) through slow approval processes, administrative hurdles, and performance monitoring.

    The pandemic showed that universities can be agile. But many educators now feel that flexibility has been replaced by managerialism, stifling creativity.

    I’ve had people in my office almost crying at the amount of paperwork just to get an innovation through. People get the message: don’t bother.

    Senior leader at a Russell Group university

    To counteract this, as one educator put it:

    It’s better to ask forgiveness afterwards than ask permission beforehand.

    Senior leader at a Russell Group university

    This kind of strategic rule-bending highlights the frustration many educators feel – a desire to innovate constrained by institutional red tape.

    Mark Andrews, in a Wonkhe article here, argues that institutions need to focus on making education work rather than simply implementing digital tools for their own sake. The same logic applies to pedagogical innovation – if the focus is solely on regulation, innovation will always struggle to take root.

    Beyond the rhetoric: what needs to change

    If universities want sustained innovation, they must address these identity tensions. Pedagogical innovation needs to be rewarded in promotions, supported through streamlined processes, and recognised as legitimate academic work – not an optional extra.

    This issue of curriculum transformation was explored on Wonkhe here, raising the critical question of how universities can move beyond rhetoric and make change a reality.

    The post-pandemic university is at a crossroads. Will pedagogical innovation be institutionalised in meaningful ways, or will it remain a talking point rather than a transformation? Academics are already navigating an identity crisis – caught between structural constraints, career incentives, and their own motivations. Universities must decide whether to ease that tension or allow it to widen.

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  • The spending review is a critical moment for UK science and innovation

    The spending review is a critical moment for UK science and innovation

    A series of key government announcements over the coming weeks will set the direction of travel for research and innovation for years to come. Next week’s spending review will set the financial parameters for the remainder of this Parliament – and we shouldn’t expect this outcome to maintain the status quo, given this is the first zero-based review under a Labour government for 17 years.

    Accompanying this will be the industrial strategy white paper, which is likely to have a focus on driving innovation and increasing the diffusion and adoption of technologies across the economy – in which the UK’s universities will need to be key delivery partners. We can also expect more detail on the proposals in the immigration white paper, with implications for international student and staff flows to the UK.

    The outcome for higher education and research remains hard to call, but the government has sent early signals that it recognises the value of investment in R&D as crucial to transforming the UK’s economy. In a volatile fiscal environment, DSIT’s R&D budget saw a real-terms increase of 8.5 per cent for 2025–26 with protection for “core research” activity within this.

    Looking ahead to the spending review, the Institute for Fiscal Studies has pointed out that the fiscal envelope set by the Chancellor for capital spending – which is how R&D is classified – at the spring statement is significantly frontloaded. There is scope for increases in the early years of the spending review period and then real-terms declines from 2027–28. With such significant constraints on the public finances, it’s more essential than ever that the UK’s R&D funding system maximises efficiency and impact, making the best possible use of available resources.

    International comparisons

    Last month, the Russell Group published a report commissioned from PwC and funded by Wellcome which considered the experiences of countries with very different R&D funding systems, to understand what the UK might learn from our competitors.

    Alongside the UK, the report examined four countries: Canada, Germany, the Netherlands and South Korea, scoring them across five assessment criteria associated with a strong R&D system: strategic alignment to government priorities; autonomy, stability and sustainability; efficiency; and leveraging external investment. It also scored the countries on two measures of output: research excellence and innovation excellence.

    The analysis can help to inform government decisions about how to strike a balance between these criteria. For example, on the face of it there’s a trade-off between prioritising institutional autonomy and ensuring strategic alignment to government priorities. But PwC found that providing universities with more freedom in how they allocate their research funding – for example, through flexible funding streams like Quality-Related (QR) funding – means they can also take strategic long-term decisions, which create advantage for the UK in key research fields for the future.

    Over the years, QR funding and its equivalents in the devolved nations have enabled universities to make investments which have led to innovations and discoveries such as graphene, genomics, opto-electronics, cosmology research, and new tests and treatments for everything from bowel disease to diabetes, dementia and cancer.

    Conversely, aligning too closely to changing political priorities can stifle impact and leave the system vulnerable. PwC found that, at its extreme, a disproportionate reliance on mission-led or priority-driven project grant funding inhibits the ability of institutions to invest outside of government’s immediate priority areas, resulting in less long-term strategic investment.

    With a stretching economic growth mission to deliver, policymakers will be reaching for interventions which encourage private investment into the economy. The PwC report found long-term, stable government incentives are crucial in leveraging industry investment in R&D, alongside supporting a culture of industry-university collaboration. This has worked well in Germany and South Korea with a mix of incentives including tax credits, grants and loans to strengthen innovation capabilities.

    Getting the balance right

    The UK currently lags behind global competitors on the proportion of R&D funded by the business sector, at just over 58 per cent compared to the OECD average of 65 per cent. However, when considering R&D financed by business but performed by higher education institutions, the UK performs fifth highest in the OECD – well above the average.

    This demonstrates the current system is successfully leveraging private sector collaboration and investment into higher education R&D. We should now be pursuing opportunities to bolster this even further. Schemes such as the Higher Education Innovation Fund (HEIF) deliver a proven return on investment: every £1 invested in HEIF yields £14.8 in economic return at the sector-level. PwC’s report noted that HEIF has helped develop “core knowledge exchange capabilities” within UK HEIs which are crucial to building successful partnerships with industry and spinning out new companies and technologies.

    In a time of global uncertainty, economic instability and rapid technological change, investments in R&D still play a key role in tackling our most complex challenges. In its forthcoming spending review – the Russell Group submission is available here – as well as in the industrial strategy white paper and in developing reforms to the visa system, the government will need to balance a number of competing but interrelated objectives. Coordination across government departments will be crucial to ensure all the incentives are pointing in the right direction and to enable sectors such as higher education to maximise the contribution they can make to delivering the government’s missions.

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  • Accelerating Innovation From Lab to Market (opinion)

    Accelerating Innovation From Lab to Market (opinion)

    American universities are dynamic engines of deep technological innovation (deep tech), responding to a growing demand for STEM research innovations that can reach the market quickly and at scale. In order to remain competitive in a fast-moving global scientific landscape and strengthen national research dominance, universities need to accelerate their innovation outputs by shortening the time it takes for research products from graduate students and postdoctoral researchers in STEM fields to reach the market, while providing these early-career researchers with the necessary mentorship and resources needed to translate their academic research projects into high-impact startup companies. By targeting these highly qualified scientists at the juncture of innovative university research and entrepreneurial ambition, we can more effectively advance academic research discoveries from early-career STEM talent into commercially viable new companies (NewCos) at scale.

    To fully capitalize on this immense potential, America must transcend the current national innovation paradigms. We argue that our nation’s global leadership in science and technology could be maintained through strategically scaled and nationally coordinated approaches to innovation, including cross-cutting and cross-sectoral approaches. Additionally, to retain American scientific and technological leadership on the global stage, we must confront the inherent risks of deep tech ventures head-on and decisively maximize our national “shots on goal,” which can lead to developing a truly robust and self-sustaining innovation ecosystem.

    A Scalable Model for National STEM Innovation

    The foundation of a new American innovation model lies in the urgent creation of new and effective cross-sectoral partnerships involving universities, industry, government and philanthropic players. Existing models supporting American innovation rely heavily on public seed funding, which, while valuable, often falls short in meeting the needs for the capital-intensive process of commercializing deep tech ventures from university lab research. Historically, the federal government has borne much of the early risk for deep tech company formation such as through the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, administered by agencies including the Department of Defense, the National Institutes of Health and the National Science Foundation.

    These programs have served as important launchpads for many academic entrepreneurs, including early-career scientists. However, early-phase SBIR/STTR grants typically range around $150,000 for durations of six months to one year. While this funding provides critical seed capital, it represents only a fraction of the substantial investment required for R&D, prototyping and market validation for deep tech ventures. Compounding this challenge, the acceptance rate for SBIR grants has declined sharply, from approximately 30 percent in 2001 to just 10 percent in 2024 in some sectors, further straining the pipeline necessary for deep tech innovation.

    Current federally focused financial support systems are falling short. Start-up success rates remain low, and private venture capital is unlikely to close the funding gap, especially for university-based early-career scientists. As competition for SBIR funding intensifies and global venture capital investment drops by 30 percent, America’s scientific and technological competitiveness is at risk without stronger shared-risk models and expanded backing for academic innovation.

    In today’s highly commercialized and globally competitive research landscape, the quality and quantity of start-ups emerging from academic labs are critical parameters for developing the next generation of entrepreneurs. A strong pipeline of NewCos enables more innovations to be tested in real-world markets, increasing the chances that transformative companies will succeed and attract external investment from industry. To meet this challenge, America needs a bold vision focused on maximizing national shots on goal through strategic scaling, proactive risk management and innovative risk-sharing models. This framework must not only rely on investment from the federal government but also from a strategically blended funding model that includes state and local governments, industry, philanthropy, venture capital, mission-driven investors, and other nontraditional funding sources.

    A nationally coordinated cross-sector pooled NewCo fund, supported by federal agencies, universities, industry, philanthropy, private equity and venture capital, partnering together, is essential for rapidly advancing national innovation at scale.

    This idea is not unique to us; it has been proposed in Europe and Australia and has been part of the science policy conversation for some time. However, the current historical moment in American science offers a unique opportunity to move from conversation to action.

    Impacts of Research Funding Cuts

    This year, significant reductions in federal funding for R&D at multiple federal agencies have posed substantial challenges to universities striving to remain global-leading STEM innovation hubs. Reductions in staff at the NSF have implications for SBIR programs, which rely on robust institutional support and agency capacity to guide early-stage innovation effectively. In addition, proposed reductions in indirect cost reimbursements for grantees at multiple agencies including NIH, DOD, NSF and the Department of Energy may also pose a challenge to research institutions and resulting start-ups in covering essential overhead expenses, impacting the transition of federally-funded research from labs to market-ready applications.

    An Updated Framework

    The national shots on goal framework is a potential remedy to the currently changing landscape imposed by federal science funding cuts. By emphasizing public-private-philanthropic partnerships, scaled seed investments and improved use of existing infrastructure within universities, this framework can help mitigate the impact of research funding cuts at federal agencies on early-career researchers.

    This framework can be especially impactful for graduate students and postdoctoral researchers in STEM fields whose scientific projects, entrepreneurial endeavors and research careers require robust and sustained federal support from multiple funding sources over a longer period of time. It also allows universities to maintain and expand deep tech innovation without relying solely on federal agency funding.

    For example, targeted one-year investments of $200,000 per NewCo can provide an essential and low-risk commercialization runway, similar in scale to the NIH R21 program. This fund would be sustained through contributions from a broad coalition of federal agencies, philanthropies, state governments, regional industries, universities and venture and private equity partners. By distributing risk across the ecosystem and focusing on returns from a growing pipeline of NewCos, this coordinated effort could partially counteract the losses sustained by the research enterprise as a result of federal agency funding cuts and accelerate university-driven scientific innovation nationwide.

    To support the long-term sustainability of these start-up companies, a portion of national NewCo funds could be reinvested in traditional and emerging markets, including crypto. This would help grow the NewCo funds over time and de-risk a pipeline of start-ups led by early-career scientists pursuing high-risk research.

    A Pilot Program

    To validate the national shots on goal vision, we propose a targeted pilot program initially focused on graduate students and postdoctoral researchers in STEM fields pursuing NewCo formation at select U.S. land-grant universities. Land-grant universities, which are vital hubs for STEM research innovation, workforce development and regional workforce growth, are uniquely positioned to lead this effort. Below, we suggest a few elements of effective pilot programs, bringing together ideas for outreach, partnerships, funding and relevant STEM expertise.

    • Dedicated, national risk-mitigating funding pool: To minimize capital risk, provide one-year seed grants of $200,000, along with subsidized or free access to core facilities. By the end of the year, each venture must secure external funding from the commercial sector, such as venture capital, or it will be discontinued, given that follow-on support cannot come from additional federal grants or the seed fund itself.
    • Targeted, risk-aware STEM outreach and recruitment: Implement a national outreach campaign explicitly targeting STEM graduate students and postdoctoral researchers at land-grant universities, highlighting risk-managed opportunities and participation pathways. Industry and philanthropic partners should be included in outreach and recruitment steps, and promote projects that meet high-priority industrial and/or philanthropic R&D strategic interests.
    • Specialized, STEM-oriented risk management–focused support network: Develop a tailored mentorship network leveraging STEM expertise within land-grant universities. The network should include alumni with entrepreneurial talent and economic development partners. It should also include training for academic scientists on risk modeling and corporate strategy, and actively incorporate industry experts and philanthropists.
    • Earmarked funding for STEM-based graduate and postdoctoral programs: In addition to the above, new funding streams should be specifically allocated to graduate students and postdoctoral researchers in STEM fields. This framework would grant them an intensive year of subsidized financial support and access to the university’s core facilities, along with support from business experts and technology transfer professionals to help them launch a company ready for external venture funding within one year. Critically, during this process, the university where academic research was conducted should take no equity or intellectual property stake in a newly formed company based on this research.
    • Rigorous, risk-adjusted evaluation and iteration framework: Establish a robust national evaluation framework to track venture progress, measure performance and iteratively refine the framework based on data-driven insights and feedback loops to optimize risk mitigation.
    • Leverage existing programs to maximize efficiency and avoid duplication: Entrepreneurial talent and research excellence are nationally distributed, but opportunity is not. Select federal programs and initiatives can help level the playing field and dramatically expand STEM opportunities nationwide. For example, the NSF I-Corps National Innovation Network provides a valuable collaborative framework for expanding lab-to-market opportunities nationwide through the power of industry engagement.
    • Prioritize rapid deep tech commercialization through de-risking models that attract early-stage venture and private equity: Transformative multisector funding models can unlock NewCo formation nationwide by combining public investment with private and philanthropic capital. The Deshpande Center at MIT demonstrates this approach, offering one-year seed grants of $100,000, with renewal opportunities based on progress. These early investments can help deep tech entrepreneurs tackle complex challenges, manage early risk and attract commercial funding. ARPA-E’s tech-to-market model similarly integrates commercialization support early on. Additionally, the mechanism of shared user facilities at DOE national labs reduces R&D costs by providing subsidized access to advanced infrastructure for academic researchers in universities, thereby supporting the formation of NewCos through strong public-private partnerships.
    • Bridge the academic-industry gap: Given the central role of universities in national innovation, building commercially viable deep tech ventures requires bridging the science-business gap through integrated, campus-based STEM ecosystems. This requires strengthening internal university connections by connecting science departments with business schools, embedding training in risk modeling and corporate strategy and fostering cross-disciplinary collaboration. These efforts will support the creation of successful start-ups and equip the next generation of scientists with skills in disruptive and inclusive innovation.

    Conclusion

    As American scientific innovation continues to advance, this moment presents an opportunity to rethink how we can best support and scale deep tech ventures resulting in start-up companies emerging from university research labs. In the face of federal funding cuts and ongoing barriers to rapid commercialization at scale within universities, these institutions must adopt bold thinking, forge innovative partnerships and exhibit a greater willingness to experiment with new models of innovation.

    By harnessing the strengths of land-grant universities, deploying innovative funding strategies and driving cross-disciplinary collaboration, we can build a more resilient and globally competitive national research and innovation ecosystem.

    Adriana Bankston is an AAAS/ASGCT Congressional Policy Fellow, currently working to support sustained federal research funding in the U.S. House of Representatives. She holds a Ph.D. in biochemistry, cell and developmental biology from Emory University and is a member of the Graduate Career Consortium—an organization providing an international voice for graduate-level career and professional development leaders.

    Michael W. Nestor is board director of the Government-University-Industry-Philanthropy Research Roundtable at the National Academies of Sciences, Engineering and Medicine. He directed the Human Neural Stem Cell Research Lab at the Hussman Institute for Autism, where his work led to the founding of start-ups Synapstem and Autica Bio, and contributed to early-stage biotech commercialization at Johnson & Johnson Innovation–JLABS. He holds a Ph.D. in neuroscience from the University of Maryland School of Medicine and completed postdoctoral training at the NIH and the New York Stem Cell Foundation.

    The views expressed by the authors of this article do not represent the views of their organizations and are written in a personal capacity.

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  • ASU Online: Where Success Is Accessible and Innovation Is Standard

    ASU Online: Where Success Is Accessible and Innovation Is Standard

    What is your passion? What sparks your curiosity and brings you joy? Whatever it is, Arizona State University (ASU) will help you find it, study it, master it, and turn it into a rewarding career, regardless of your previous educational journey.

    Casey Evans

    Chief Operating Officer, EdPlus at ASU

    For more than 15 years, ASU has offered high-quality programs online taught by the same world-renowned faculty that teach on-campus students, using the same rigorous curriculum. ASU offers more than 300 degree programs online, with over 100,000 graduates now working across nearly every industry, helping to strengthen the university’s reputation for educational excellence and career readiness. ASU graduates are highly recruitable, with ASU ranking No. 2 in the United States among public universities for the employability of its graduates, ahead of UCLA, the University of Michigan, and Purdue University.

    ASU Online combines the exceptional resources and academic excellence of the nation’s most innovative university with a rigorous, world-class online learning experience. Students are supported every step of the way, ensuring they gain the skills and knowledge they need to thrive in their career, no matter where they are in the world.

    “ASU’s rigorous coursework and knowledgeable instructors have been instrumental in preparing me for my career, equipping me with the skills to excel in my field,” said Evelyn M., ’24 BS in speech and hearing science.


    To learn more, visit asuonline.asu.edu


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  • Is the Partnership for American Innovation Gone? (opinion)

    Is the Partnership for American Innovation Gone? (opinion)

    In January I wrote a piece asking whether America’s research universities would make it to their 100th birthday, marking their birth with the creation of the National Science Foundation in 1950—its 75th birthday was May 10. The article built on concerns that our research universities are in a precarious state, with their resources stretched thin supporting their dual missions of education and research. At the end I added a new concern: with the beginning of the Trump administration would these institutions survive the year?

    In only the first 100-odd days, the precipitous cancellation of grants and freezing of research support and now the proposed slashing of the budgets of the NSF and the National Institutes of Health and dramatic increase in the tax on university endowments have made my worst fears real. Are we really trying to end the partnership that has led to the greatest period of innovation in history?

    With the creation of the NSF, the government and universities established a research partnership to feed the American economy and national defense and to train the R&D labor force. The partnership was supported by funding from both sides, coupled with an unrelenting commitment to research excellence and impact. By any measure it has been wildly successful, generating new knowledge, inventions and cures and educating generations to lead our economy and society.

    In 2022 alone, the 174 Carnegie R-1—very research intensive—universities filed more than 20,000 patent applications and were granted nearly 6,000. But perhaps to understand why sustaining this partnership is vital to our future we only need to recall that the mRNA vaccines that spelled the end of the COVID-19 pandemic were built on research supported over decades by the NIH.

    The scale of the partnership is apparent in the data: In 2022, university research spending totaled $97.8 billion, with $54.1 billion coming from the federal government. What has not been widely acknowledged is that universities contributed $24.5 billion of this total in the form of self-supported research and cost sharing, especially supporting the misunderstood indirect costs of research. Many of these expenses are not so “indirect,” as they support specialized spaces, facilities and instruments—you cannot do research in a parking lot.

    Universities invested 45 cents for each federal research dollar received— this is the financing of the partnership. It seems like a bargain for the government to contribute only 0.2 percent of GDP (or less than 1 percent of the federal budget) to fuel innovation and the labor force of the world’s largest economy. Federal support of university research has grown only 44 percent since 2010. This compares to China’s threefold growth in investment in its universities.

    The Chinese investment highlights the increasing competition for research talent, and we risk falling behind. Other countries are emulating us, building research universities and trying to attract the stream of talent that has come to the U.S. to learn, work and live. Our chilling climate for immigrants is making it much easier to lure this talent abroad.

    American universities have done what they can to stay in front, with their own support of research growing twice as fast as federal funding, up from 30 cents to a federal dollar in 2010. It will be difficult for universities to continue to grow this investment. Following the pandemic, inflation has taken its toll. Now the funding cuts already imposed, and the enormous ones in the administration’s proposed budget, will shift billions in research costs to universities—costs they cannot afford. The proposed 15 percent cap on indirect costs alone—spread across all federal support—could cost the R-1 universities more than $10 billion, doubling their support relative to the federal government.

    The result will be catastrophic, with universities retreating from research, essentially destroying in a few months the innovation ecosystem built over three-quarters of a century. The long-term impact will be devastating for all Americans, as measured in undiscovered inventions and cures, the global competition for ideas and people, and the country’s future economic prosperity.

    Our innovation ecosystem will be hamstrung by the loss of a generation or more of research talent, who are either not trained or who go elsewhere. Already our talent pipeline is being constricted by cutting in half the number of NSF fellowships awarded to the most promising scientists and engineers. Reports also are mounting of scientists moving to countries where they are warmly welcomed with substantial government support. Is this our national strategy to strengthen America’s knowledge-based economy?

    We are on the verge of an innovation winter that will last decades when we can ill afford it as we respond to demands to improve health care, compete for global dominance in AI and other critical technologies, and create a secure and peaceful world. Universities do face important challenges, such as expanding access, educating more Americans to be informed and thoughtful citizens, and giving them the skills to thrive in an AI-driven world. Universities can meet these challenges if they are supported.

    We must avoid the innovation winter by continuing the partnership so our research universities remain the beacons for innovation and education that they have been for three-quarters of a century. This is the only way to keep America at the forefront, not at the back of the pack.

    This choice is what is at stake for all of us.

    Robert A. Brown is president emeritus of Boston University.

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  • Banking on Human Capital: How RBC Sees the Future of Talent, Innovation, and the Role of Post-Secondary Institutions

    Banking on Human Capital: How RBC Sees the Future of Talent, Innovation, and the Role of Post-Secondary Institutions

    Canada’s heading into some pretty choppy waters in 2025. For a century or so, we’ve had a one track economic strategy, closer integration with the United States. Now, the Trump administration with its faith in tariffs as an instrument of both power and corruption, has essentially nuked that strategy, at least as far as the trading goods is concerned. There’s a lot of change coming to Canada, and it’ll be costly. In much the same way that diplomatic evolution and defense needs are forcing European countries to look at higher education in a different light, Canadian universities are looking around at their new situation very nervously too.

    In Canada right now, a few people are making the case for change as strongly as John Stackhouse. John’s the ex editor-in-chief of the Global Mail. He’s now a Senior Vice President at the Royal Bank of Canada, leading that organization’s economics and thought leadership group. He’s the lead author of a recent report called “A Smarter Path, the Case for Post-Secondary Reform.” This report makes a number of, shall we say, uncomfortable observations about the relationship between Canadian higher education and the Canadian knowledge economy, in particular, between high spending and high graduate numbers on the one hand, and low productivity and significant levels of graduate underemployment on the other.

    Though the report does not directly address the issue of Trump or tariffs — it was released 48 hours before Liberation Day — it has added to the sense in Canada that the higher education sector is headed for and indeed needs a shakeup. And just to come clean for a moment, we here at Higher Education Strategy Associates are in a partnership with John and RBC and the Business Higher Education Roundtable, putting together a series of events culminating in a policy summit on post-secondary education in late September of this year.

    In the interview today, I talked to John about what the Canadian system’s biggest challenges are, how universities and businesses can more effectively partner with one another, and why Canadian political parties are increasingly shy about betting on the knowledge economy. But enough for me. Let’s turn it over to John.


    The World of Higher Education Podcast
    Episode 3.30 | Banking on Human Capital: How RBC Sees the Future of Talent, Innovation, and the Role of Post-Secondary Institutions

    Transcript

    Alex Usher (AU): Okay, John, why does a bank care so much about post-secondary education?

    John Stackhouse (JS): That’s a fair question, Alex—and thank you for including us in the podcast. If I can put it in terms of capital, maybe that’s what people would expect from a bank. Our economy, and the society that depends on it, relies on different kinds of capital. We have natural capital, technology capital, and of course, financial capital—which you’d expect from a bank. But just as critical is human capital. That’s core to the economy.

    There’s nothing new in saying that, except to emphasize that from RBC’s perspective, when we look at Canada’s prospects through the 2030s and the prosperity we hope to achieve, we need to think seriously about how we harness all these forms of capital: natural, financial, technological—and critically—human capital.

    We need to develop a more prosperous economy and society, but also the kind of vibrant communities that companies want to be part of, and that we as individuals want to contribute to. As a bank, that matters to us. Our purpose is to help clients thrive and communities prosper—and both of those depend on human capital. We hear that from our clients, our community partners, and our employees. So those are just some of the reasons why RBC is leaning into the post-secondary conversation.

    AU: In the paper you co-wrote, you describe Canada’s post-secondary education system as being slow, costly, and often out of sync with the economy. I think those are fairly common criticisms of higher education around the world. Do you think there’s something specific to Canada in that critique? Or is this more of a general observation about modern higher ed?

    JS: There’s probably some parchment from a thousand years ago where an education critic wrote, “You’re too slow, too costly, and out of touch with the economy.” -Signed, the monks of higher education. But yes, it’s fair to say that Canada isn’t alone in facing these challenges. That said, there are a few things that may be more pronounced here. One is that we’ve been a bit of a victim of our own success. We have a lot of post-secondary education in this country, but we haven’t differentiated enough within the system.

    Continental Europe, for example, continues to differentiate in ways we haven’t. So we end up producing graduates with degrees and diplomas that are too similar—and not always aligned with specific needs.

    We also haven’t allowed the business model to evolve at the pace of the economy or society, or even the expectations of students and educators. Many of them know the world is moving faster than our institutions are.

    And then on the research side—which I’m sure we’ll get to—we really lag behind. As an advanced economy, a G7 country, we’re not where we should be in post-secondary research. Part of the issue lies with the private sector—we haven’t integrated research and business to the degree that an advanced economy will need to in the 2030s.

    AU: RBC has been a really strong voice on the education–work connection. What are employers still not getting from the current system? And what responsibility do you think they have in helping to improve it?

    JS: There’s definitely a shared responsibility—and thanks for mentioning RBC’s commitment to work-integrated learning. One of the reasons we’re so invested in this is because our CEO, Dave McKay, is a product of the co-op system at Waterloo. He has a deep belief that work-integrated learning not only improves the student experience, but also strengthens the education system itself.

    When students return to the classroom after applying their knowledge in the real world, it deepens their learning. And it also improves the organizations they work with. At RBC, we hire a couple thousand co-op students every year—not just programmers from Waterloo, but fantastic interns from TMU and a wide range of colleges and universities across the country.

    We benefit from that. It improves how we work. Yes, it creates a talent pipeline—but we’ve also seen something more transformative. Over the past decade, we’ve started giving our co-op students real challenges to solve. We form teams, provide some management support, and tell them: here are some of our biggest problems—see you in August. Then they present their ideas to senior leadership in what’s essentially a competitive showcase. We’ve had around a hundred patents come out of that system.

    Students bring critical thinking, fresh perspectives, and a collaborative mindset that they develop in post-secondary. They often arrive with stronger teamwork skills than we could teach them from scratch, and they’re able to apply those skills to real problems.

    So what do employers need to do? They need to treat this as a serious investment in their own businesses. It’s a way to drive change, but it requires resources. You have to hire people who are good at managing these programs. Students don’t just walk in and figure it out on their own—it’s not Lord of the Flies. It takes organizational effort.

    AU: Let’s talk about what educational institutions are doing. I got the impression from the report that you think they still need to do more to align educational outputs with labor market needs. That said, there’s been a lot of progress over the last decade: growth in work-integrated learning, the rise of microcredentials, experiments with competency-based learning. But it sounds like you don’t think that’s enough. What more needs to happen?

    JS: Sadly—or depending on your perspective, maybe excitingly—none of us are doing enough. That’s partly because of technology, but also because of broader global forces. The world around us is changing faster than most of us are able to keep up with—including large organizations, small businesses, and educational institutions.

    The pace of change is accelerating, and it will only continue to do so. Institutions need to become much more change-minded in how they operate. That’s hard in education, for all the reasons your listeners will understand.

    One major challenge is the business model. It’s becoming a crisis. Post-secondary institutions aren’t getting the funding they need. Everyone knows that—but they’re losing the argument in the public square when it comes to making the case for new funding. And given the pressures society is under, I don’t see that changing in a meaningful way anytime soon.

    So institutions need more freedom to change—to evolve their business models, including how they generate revenue. And that means becoming more connected to, and responsive to, the broader economy around them. That’s where many of the new opportunities lie.

    AU: John, we’ve been talking mostly about human capital, which you’ve said is a key concern for RBC. But what about research and the co-production of knowledge? What are the respective roles of post-secondary institutions and businesses? Why don’t we see the kind of close connection between enterprises and universities that exists in parts of Europe or the U.S.? What’s the missing link?

    JS: That’s a tough nut to crack—and one that people far smarter than me have studied and debated for decades. But part of the challenge lies in the private sector itself. In many ways, we’ve become too much of a “branch plant” and “hinterland” economy—living off the wealth of the land, our access to the U.S. market, and the dividends of an innovation economy.

    I wouldn’t say that’s coming to an end—because that would be overly dramatic—but we’re clearly experiencing a sharp shift. In an odd way, the Trump challenge to Canada is a bit of a gift. It’s forcing us to acknowledge that we can’t be so dependent on the U.S. market. That’s become a broadly shared Canadian view. We need to build stronger connections with other parts of the world—and that’s going to require more serious investment in R&D from our businesses.

    If we want to transform branch plants into independent, globally competitive facilities, especially ones that can succeed in European and Asian markets—despite the distance—we need to invest in research and development in a way we haven’t for a generation.

    New governments—federal and provincial—need to act with urgency. They should bring business leaders together and ask, “What do we need to build?” And not just through one-off tax incentives. We need to foster a culture of collaboration and dynamism between universities, colleges, polytechnics, and businesses to shape what I’d call a post-Trump Canadian economy.

    That’s not going to happen by copying Germany’s Fraunhofer model or Japan’s approach—those are deeply rooted in specific cultural contexts. We need to develop something uniquely Canadian.

    And we can’t afford to spend years on a Royal Commission or slow-moving studies. This needs to happen quickly. A new federal government could seize this moment to bring together the provinces and private sector with a sense of urgency—and maybe even a crisis mindset.

    AU: I’ll come back to the Trump issue in a moment, but going back to the report—you lay out a number of challenges in the sector: outdated budget models, over-credentialed but under-skilled graduates, and so on. What do you think is the most pressing reform Canadian post-secondary needs right now? What’s the weakest link in the system?

    JS: That’s a great question—and a hard one to answer. But I’d go back to the funding model. Post-secondary institutions need more flexibility to innovate with how they’re funded. They need to move beyond the constraints of provincial funding and develop new approaches to tuition and fees—ones that are more closely tied to performance, outputs, and outcomes.

    There also needs to be more competition within the sector. Most people I know in post-secondary are pretty enthusiastic about that idea—though, understandably, they’d like the model to be structured so they have a good shot at succeeding.

    I think provinces need to be nudged—and maybe not even that much—to open the door to more innovation, more competition, and a bit more daring on the institutional side.

    AU: I think the words you used in the report were “reasonable deregulation.” Tell me more about increased competition—are there things we could do to incentivize more new players in the system who might be more disruptive?

    JS: There’s nothing quite like new players. I’ve studied enough sectors over the years to see that when it comes to innovation, nothing works quite as well as a vibrant, well-funded new entrant. Encouraging that kind of disruption would move us forward significantly—and it would give creative people across the sector permission to come up with ideas they’re not even thinking about yet. That’s the power of competition.

    So one key step is reducing the regulatory barriers that prevent those new players from entering the space.

    I also think employers can play a bigger role by sending clearer market signals. That could be as simple as hiring differently. We tend to recruit from the same institutions over and over—often for good reasons—but “like hires like.” If we want to encourage new entrants, we have to show that their graduates will have good job prospects. That kind of signal travels fast—even down to the high school level, where students are making decisions about their future.

    AU: Outside the scope of the report, you’ve been very outspoken in recent months about the gravity of the threat Canada faces from the U.S. under Trump. You spoke at the Business + Higher Education Roundtable event, and I know people who heard your remarks were quite sobered by them.

    There are clearly big changes coming to the country as a whole. What are the implications for universities? What changes do you think are now baked into the systems of government subsidy and regulation because of the shifting geopolitical situation?

    JS: It’s unfortunate that colleges and universities aren’t more central to the Trump-related conversation. We’re hearing a lot about pipelines, export infrastructure, and ports—which are all important. We’re also hearing a lot about trade-exposed sectors: autos, steel, aluminum, even pharmaceuticals. Guess what? All of those sectors depend on post-secondary institutions.

    So how are we thinking about the steel plant of the future that might be exporting more to Europe or Asia? It’s going to need incentives to retool. The same goes for auto plants that may need to shift into different kinds of manufacturing—including, potentially, defense production as we scale up defense spending. What kind of talent will be needed for that? How are schools in those regions adapting? And to your point about research—how can we better integrate the research side of those institutions into this transformation?

    They’ll need to develop new models—and we need to incentivize that shift. The good news is, I think there will be more money on the table. But it will be different kinds of research and institutional funding than what we’ve seen in the past. And that could be a good thing.

    So how do colleges and universities rise to that challenge? There could be tens of billions of dollars available to support economic transition. They’ll need to step up and play a leading role—and if they do, they’ll be rewarded for it.

    Interestingly, there’s already growing enthusiasm to attract academic talent from the U.S.—what some are calling “Trump intellectual refugees.”

    I’ve seen similar cycles before. After 9/11, during the Bush years, there was a similar kind of excitement. Star academics moved here as a sort of cultural vote for Canada. But that kind of movement doesn’t tend to be sustainable—or even all that interesting—from a long-term perspective.

    So how do we make it sustainable and interesting? One idea, from someone else, is to create a kind of Canada Research Chairs 2.0 for the late 2020s.

    Not a play to say “Come escape Trump,” but rather to say: if you’re an entrepreneurial, ambitious academic working in areas that matter to Canada, there’s no better place in the world to be right now than here.

    AU: One of the points you touched on earlier is that political parties seem to be responding to aggressive tariffs on exports by doubling down on producing goods. I find that kind of strange—surely one of the answers is to pivot more toward services. We’re not especially strong in that area, and in theory, that’s where universities should have an advantage. Why do you think we’re pushing so hard on goods while letting the services side drift?

    JS: That’s a great observation. We’ve become more of a services—or maybe better put, an intangibles—economy. A knowledge economy. That was a popular thing to say a decade ago, though it’s become a bit derided since.

    But we need both. You can have intangibles on their own, but the best ones tend to emerge from tangible activities.

    We need to play to our strengths, and that includes our resource economy. One of the things we noted in our study is that post-secondary doesn’t align with the resource economy as well as it should. That doesn’t mean just producing miners and rig operators—though those roles will still matter for years to come. There’s a whole spectrum of science and discovery we’ve long excelled at, and we need to scale that up if we want to lead in critical minerals, for example.

    It’s not just about having critical mineral mines or processing plants. We’ve shut down many of our best mining schools in this country, while China has established far more than we have—far more than you’d expect based on population size alone.

    So yes, we need to invest in the intangible—knowledge—side of that tangible sector. It’s not just manufacturing, as you said. It’s also processing and resource extraction, which are highly sophisticated fields. Those have earned Canada substantial academic recognition over the decades.

    We need to ensure that the intangible capacity we’re building in our universities and colleges remains closely tied to the real economy—especially to manufacturing and resource development.

    AU: Best case scenario—ten years from now—what does the Canadian post-secondary system look like? How is it different from today?

    JS: It would have much more variation. In fact, we might see something entirely new emerge—something that’s not quite a college, university, or polytechnic, but a distinct Canadian model.

    Just as Canada pioneered community colleges in the 1950s and ’60s, we have a chance to create a new tier. And this wouldn’t be at the expense of the existing systems—but something more suited to evolving needs.

    We’d have institutions that reflect and respond to the economy across all regions, including the far North. We don’t need to be physically present everywhere—we can do a lot of this remotely—but we do need our institutions to better reflect the realities of the country and the economy. And they need to be more connected to the world.

    You and I have talked a lot about the situation with international students. The real tragedy of what’s happened over the last decade would be if we abandoned the whole model. We had something that was largely good—it got mucked up—but that doesn’t mean we throw it out.

    We need to fix what went wrong. And we need to remain a destination for the best and most ambitious students from around the world. Ideally, we want them to stay—but even if they go back home, they can help connect us to the world.

    Because if we’re being honest with ourselves, what we’re really saying as Canadians—though maybe not quite this explicitly—is that we want to be a more global country. And our post-secondary system is one of the best tools we have to make that happen. But it will take a deliberate effort to reach out to the world—and there’s no sector better positioned to do that than post-secondary.

    AU: John, thanks so much for being with us today.

    JS: Thanks, Alex. I’ve really enjoyed it.

    Alex Usher: And it just remains for me to thank our excellent producers, Tiffany MacLennan, Sam Pufek, and you, our viewers, listeners, and readers for following us. If you have any questions or concerns about today’s episode or suggestions for future ones, please don’t hesitate to get in touch with us at [email protected]. Run, don’t walk to our YouTube page and hit subscribe. That way you’ll never miss an episode of the World of Higher Education Podcast.

    Join us next week when our guest will be Rómulo Pinheiro. He’s a professor at the University of Agder in Norway, and we’ll be talking about university’s role in the economic development strategies of rural and remote regions. Bye for now.

    *This podcast transcript was generated using an AI transcription service with limited editing. Please forgive any errors made through this service. Please note, the views and opinions expressed in each episode are those of the individual contributors, and do not necessarily reflect those of the podcast host and team, or our sponsors.

    This episode is sponsored by KnowMeQ. ArchieCPL is the first AI-enabled tool that massively streamlines credit for prior learning evaluation. Toronto based KnowMeQ makes ethical AI tools that boost and bottom line, achieving new efficiencies in higher ed and workforce upskilling. 

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