Tag: Learning

  • Without AI “Quiet Cars,” Learning Is At Risk

    Without AI “Quiet Cars,” Learning Is At Risk

    In the late 1990s, a group of commuters would board the early-morning Amtrak train from Philadelphia to Washington, D.C. They’d sit in the first car behind the locomotive, enjoying communal, consensual silence. Eventually and with the conductor’s help, their car was officially designated as a noise-free zone. Soon after, Denise LaBencki-Fullmer, an Amtrak manager, recognized the value of a peaceful ride and institutionalized the program as the quiet car. At the request of passengers, it soon spread to a number of other commuter services.

    The educational technology sector has something to learn from the Amtrak commuters’ deliberate design of their environment. Learning requires the ability to concentrate. You need a space where you are allowed to process information, recall facts, analyze complex questions and think creatively about ideas, problems and solutions. Learning is not a smooth and easy process—in fact, it is desirable that it’s a bit difficult, because that is how we actually learn. Getting someone to do learning tasks for you, as tempting or comfortable as that might be, won’t work.

    A great deal of learning still happens online, even at colleges that value in-person teaching as much as Princeton University does. The learning management system is where our students find readings, review lecture slides and practice their skills and comprehension on homework assignments. It is also where many instructors administer assessments, both low-stakes quizzes and high-stakes exams.

    Last month, Google launched a feature called “Homework help” in Chrome—a shiny blue button right in the address bar. By engaging it, a student could prompt Google Gemini to summarize a reading or solve a quiz question in a matter of seconds. It thereby robbed the student of the learning activity that they were there to do. A few weeks later Google repositioned the feature so it is a bit less obvious (at least for now), but the question remains: What kind of AI tools should we make available to our students in learning management systems and assessment platforms?

    You might be thinking that this is a pointless question: AI is going to be everywhere—it already is. And sure, that is true. Also, if a student wants to use AI, it is easy enough to open another browser tab and ask an LLM for help. But installing the AI right in the environment in which the student is trying to learn is equivalent to sitting next to the most obnoxious cell yeller on your train ride: You can’t think your own thoughts, because the distraction is so big.

    Just as there are quiet cars on trains, there can be quiet areas of the internet. Learning management systems and assessment platforms should be one such area. That doesn’t mean that there can’t be good uses of AI in learning. Our students should know how to use AI responsibly, thoughtfully and critically, as should the faculty who teach them (I sometimes use AI in my own teaching, for instance). But we should also ask that the companies that provide us with learning technologies think critically and carefully about whether AI aids the difficult, careful work that learning requires or, in fact, removes the opportunity for it. AI is inevitable, but that doesn’t mean we can’t be intentional about how, why and where we implement it.

    I have spent the last few weeks talking with colleagues at other colleges and universities and with the partners that provide our educational technology. Everyone I have spoken with cares about education, and none of them think it’s a good idea that we implement AI in a way that so clearly pulls students out of the learning process. It is actually not unrealistic that people in the tech industry and education sector come together to make the same kind of pact that the train commuters made some 25 years ago and declare our online learning systems an AI quiet zone. We would be doing the right thing by our students if we did.

    Mona Fixdal provides strategic planning and pedagogical leadership for Princeton University’s suite of teaching and learning technologies as well its online learning program. She has a Ph.D. in political science from the University of Oslo and is the author of Just Peace: How Wars Should End and a number of chapters and articles on postwar justice and third-party mediation.

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  • Next gen learning spaces: UDL in action

    Next gen learning spaces: UDL in action

    Key points:

    By embracing Universal Design for Learning (UDL) principles in purchasing decisions, school leaders can create learning spaces that not only accommodate students with disabilities but enhance the educational experience for all learners while delivering exceptional returns on investment (ROI).

    Strangely enough, the concept of UDL all started with curb cuts. Disability activists in the 1960s were advocating for adding curb cuts at intersections so that users of wheelchairs could cross streets independently. Once curb cuts became commonplace, there was a surprising secondary effect: Curb cuts did not just benefit the lives of those in wheelchairs, they benefited parents with strollers, kids on bikes, older adults using canes, delivery workers with carts, and travelers using rolling suitcases. What had been designed for one specific group ended up accidentally benefiting many others.

    UDL is founded on this idea of the “curb-cut effect.” UDL focuses on designing classrooms and schools to provide multiple ways for students to learn. While the original focus was making the curriculum accessible to multiple types of learners, UDL also informs the physical design of classrooms and schools. Procurement professionals are focusing on furniture and technology purchases that provide flexible, accessible, and supportive environments so that all learners can benefit. Today entire conferences, such as EDspaces, focus on classroom and school design to improve learning outcomes.

    There is now a solid research base indicating that the design of learning spaces is a critical factor in educational success: Learning space design changes can significantly influence student engagement, well-being, and academic achievement. While we focus on obvious benefits for specific types of learners, we often find unexpected ways that all students benefit. Adjustable desks designed for wheelchair users can improve focus and reduce fatigue in many students, especially those with ADHD. Providing captions on videos, first made available for deaf students, benefit ELL and other students struggling to learn to read.

    Applying UDL to school purchasing decisions

    UDL represents a paradigm shift from retrofitting solutions for individual students to proactively designing inclusive environments from the ground up. Strategic purchasing focuses on choosing furniture and tech tools that provide multiple means of engagement that can motivate and support all types of learners.

    Furniture that works for everyone

    Modern classroom furniture has evolved far beyond the traditional one-size-fits-all model. Flexible seating options such as stability balls, wobble cushions, and standing desks can transform classroom dynamics. While these options support students with ADHD or sensory processing needs, they also provide choice and movement opportunities that enhance engagement for neurotypical students. Research consistently shows that physical comfort directly correlates with cognitive performance and attention span.

    Modular furniture systems offer exceptional value by adapting to changing needs throughout the school year. Tables and desks that can be easily reconfigured support collaborative learning, individual work, and various teaching methodologies. Storage solutions with clear labeling systems and accessible heights benefit students with visual impairments and executive functioning challenges while helping all students maintain organization and independence.

    Technology that opens doors for all learners

    Assistive technology has evolved from specialized, expensive solutions to mainstream tools that benefit diverse learners. Screen readers like NVDA and JAWS remain essential for students with visual impairments, but their availability also supports students with dyslexia who benefit from auditory reinforcement of text. When procuring software licenses, prioritize platforms with built-in accessibility features rather than purchasing separate assistive tools.

    Voice-to-text technology exemplifies the UDL principle perfectly. While crucial for students with fine motor challenges or dysgraphia, these tools also benefit students who process information verbally, ELL learners practicing pronunciation, and any student working through complex ideas more efficiently through speech than typing.

    Adaptive keyboards and alternative input devices address various physical needs while offering all students options for comfortable, efficient interaction with technology. Consider keyboards with larger keys, customizable layouts, or touchscreen interfaces that can serve multiple purposes across your student population.

    Interactive displays and tablets with built-in accessibility features provide multiple means of engagement and expression. Touch interfaces support students with motor difficulties while offering kinesthetic learning opportunities for all students. When evaluating these technologies, prioritize devices with robust accessibility settings including font size adjustment, color contrast options, and alternative navigation methods.

    Maximizing your procurement impact

    Strategic procurement for UDL requires thinking beyond individual products to consider system-wide compatibility and scalability. Prioritize vendors who demonstrate commitment to accessibility standards and provide comprehensive training on using accessibility features. The most advanced assistive technology becomes worthless without proper implementation and support.

    Conduct needs assessments that go beyond compliance requirements to understand your learning community’s diverse needs. Engage with special education teams, occupational therapists, and technology specialists during the procurement process. Their insights can prevent costly mistakes and identify opportunities for solutions that serve multiple populations.

    Consider total cost of ownership when evaluating options. Adjustable-height desks may cost more initially but can eliminate the need for specialized furniture for individual students. Similarly, mainstream technology with robust accessibility features often costs less than specialized assistive devices while serving broader populations.

    Pilot programs prove invaluable for testing solutions before large-scale implementation. Start with small purchases to evaluate effectiveness, durability, and user satisfaction across diverse learners. Document outcomes to build compelling cases for broader adoption.

    The business case for UDL

    Procurement decisions guided by UDL principles deliver measurable returns on investment. Reduced need for individualized accommodations decreases administrative overhead while improving response times for student needs. Universal solutions eliminate the stigma associated with specialized equipment, promoting inclusive classroom cultures that benefit all learners.

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  • Not knowing is the start of learning

    Not knowing is the start of learning

    “I don’t know” is an underrated student response.

    If viewed in a positive light instead of as a lack of understanding or a fault, it can become a catalyst for enquiry, supporting students with their research and knowledge building skills.

    What does “I don’t know” mean?

    Picture yourself as a student who has been asked a direct question during a lecture.

    This was a position I found myself in on several occasions during my own undergraduate science degree course. Sometimes I would know the answer and be able to respond confidently – relieved. On other occasions, perhaps not coming up with the answer immediately, I would default to “I don’t know”.

    Many academics recall a particular lecturer who motivated them to succeed. For me, this lecturer emerged as a mentor during my own MSc in chemistry. He used to hold challenging tutorials, if  I asked a difficult question, there was nowhere to go. I simply had to stay with the moment and work through the question.

    I didn’t realise it at the time, but this helped me find a starting point for figuring out things I didn’t understand and embracing the discomfort that comes with not understanding something…yet!

    More questions

    Why do we ask students questions? Questions can be posed to the entire room, known as open questioning. This type of question can work well at the beginning of a session or when we want to offer choice in terms of who wishes to answer a question. We can also ask objective, subjective or speculative questions.

    Or we can pose direct questions to specific, individual, students. Their use may seem like quite an intense approach but can offer benefits. Directed questions can create a “high pressure, high stakes” atmosphere, it is often one that is more memorable for the individual involved and allows the lecturer to assess whether that individual understands the topic at hand. It presents a mechanism for the student to check their understanding and to build resilience by answering under pressure.

    It can also act as a gateway to Socratic questioning, which can allow the student or wider attending group to explore the topic being studied in more depth and with greater thought.

    Working as a lecturer in both further education (with BTEC students) and higher education institutions, I have gained experience with how to support students through these moments and how to make the questioning process less daunting.

    It is easy to take “I don’t know” at face value, believing that a student really does not know the answer to a given question. However, “I don’t know” could be a default answer for something completely different.

    “I don’t know” could mean: “I need time to think about that”, “I didn’t hear what you asked”, “I don’t want to answer in front of…”, “I don’t like being put on the spot”, “I’m not interested”, “I’m not sure if the answer I’m thinking of is correct” or even… “I don’t know”.

    How we respond is something to think about.

    Conversational, not confrontational

    As universities (across the UK and globally) embrace active collaborative learning approaches, the traditional lecture has sometimes come to be viewed as didactic in a negative sense.

    Evidence presented following a 2019 report by Nottingham Trent University, Anglia Ruskin University and University of Bradford has shown that active collaborative learning methods such as team-based learning create engaging learning environments with positive links to progression and attainment. Nottingham Trent University has followed up through a university-wide TBL pilot study during the 2024-25 academic year.

    Interactive lectures can act as a “half-way house” between traditional lectures and active collaborative learning sessions. Effective questioning strategy can make them more engaging. When lectures are interactive, open, directed and Socratic questioning can be sprinkled in using a non-confrontational approach, such that the questions become part of the conversation and are no longer perceived as an unwelcome assessment of knowledge.

    The important thing is how the lecturer approaches this; an effective application being one where students can feel comfortable answering the questions posed. Importantly, asking the correct questions, will help students to leap from where they currently are, with a project for example, to what they could potentially explore next, or to what their results could possibly mean. “How do you think that process happens?”, “What do you think about that?” or “What would it mean if you got the opposite result?”, are a few examples of questions we could ask to encourage a student to dig deeper.

    Using questions to frame conversations can create this exploratory environment where an initial not knowing can lead to the confidence to learn more about the topic being studied, moving further into Vygotsky’s zone of proximal development.

    Enjoy the silence

    Whether in a large lecture theatre, an active collaborative learning room, a small workshop session or an online session, questions can be posed and time given for the answers to come.

    As lecturers posing questions to students, we need to remember to give students time to answer the question or to think about a possible answer. It is common to only allow a few seconds before jumping back in to prompt the student, to bounce the question to someone else or even for us to answer it yourself.

    Building in thinking time can make the difference. Feeling even stranger in a silent, online environment, it’s important to allow the silence and discomfort to fill the space and wait for an answer – any answer – even “I don’t know” to break through! Then, there is something to work with.

    Turning the heat up – or down

    As lecturing academics, we also have the responsibility to turn down the heat if we can see that a questioning experience is becoming too intense for a given student or group of students. Questioning should be challenging but not traumatic – know when to pull back.

    Having knowledge of your students is the best way of managing this as one can be aware of a student’s profile, background and temperament or how much they enjoy engaging with an interactive questioning approach. For some students, it may not be effective to pose directed questions, particularly in front of a large audience. Think “How will this student respond if I ask them a directed question?” “Will it help them develop their understanding and build resilience, or will it be too much for them?”

    For such students, weaving in discussion during group or individual activities in a conversational way may be the best approach to gauge their understanding. For larger cohorts, where we may not know the temperament or preference of all students, intuition and experience can be the key, allowing us to pose questions and then decide whether to persist or perhaps back off and move on – potentially returning to discuss the topic with that student later or in a different session.

    And it is important to return to the reason we pose questions. Questioning is more than transactional. If used effectively, it can help us to understand what our students are learning and thinking about, and that can generate real discussion. “Do my students understand this topic?”, “Can my students explain what is happening in this experiment?” or “Are they enjoying it?”.

    Taking a question path approach, students can also learn to use this process, applying enquiry-based learning as they explore their subjects of study independently

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  • Digital Learning Project Manager at Notre Dame

    Digital Learning Project Manager at Notre Dame

    I heard from my friend Sonia Howell, director of the Office of Digital Learning at the University of Notre Dame, that she is recruiting for a digital learning project manager. I asked Sonia if she wanted to share more about the role in this Featured Gig series.

    Q: What is the university’s mandate behind this role? How does it help align with and advance the university’s strategic priorities?

    A: Excellence in undergraduate education is essential to how Notre Dame envisions itself fulfilling its institutional mission. The digital learning project manager will contribute directly to the educational experience of our undergraduate students, working with faculty, learning designers, a media team and other project management professionals to create cutting-edge digital offerings meant to enhance Notre Dame’s signature residential learning environment.

    In addition, the person in this role will manage initiatives that bring elements of Notre Dame’s academic life to learners beyond our campus. These range from online courses open to the general public to online pathway programs for current high school students exploring college opportunities and incoming first-year Notre Dame students prepping for the rigors of a university curriculum.

    Q: Where does the role sit within the university structure? How will the person in this role engage with other units and leaders across campus?

    A: The digital learning project manager is a member of the Office of Digital Learning, which is part of a larger unit, reporting to the Office of the Provost, called Notre Dame Learning. Housing the ODL and the Kaneb Center for Teaching Excellence, Notre Dame Learning brings together their teaching and learning expertise along with that of the Office of Information Technology’s Teaching and Learning Technologies group to serve as the hub of learning excellence and innovation at Notre Dame.

    Working in the ODL will give the person in this position the chance to collaborate directly with instructors, the university’s academic departments and colleges, and colleagues across the Notre Dame Learning organization. They will work closely with the ND Learning leadership team to advance the organization’s strategic priorities.

    Q: What would success look like in one year? Three years? Beyond?

    A: From day one, building relationships will be paramount in this position. The Notre Dame family embodies a strong sense of community, and successful project managers on our campus are those who embrace the human component of their work, recognizing that shepherding a project from initiation to completion requires personal connection as much as it does the ability to keep a group on task. The importance of being able to understand faculty priorities and concerns, interface with administrators both internal and external to Notre Dame, and partner with colleagues across the ODL and Notre Dame Learning more generally cannot be overstated. As these relationships deepen over time, the digital learning project manager will become a go-to member of the Notre Dame Learning team and assume a larger role in driving its initiatives.

    Q: What kinds of future roles would someone who took this position be prepared for?

    A: Given all the different skill sets someone in this position will draw on and/or develop—e.g., project management, client/stakeholder relations, written and verbal communication, familiarity with media production and learning design processes, knowledge of higher education and organizational dynamics more broadly—it is a role that can serve as a springboard into opportunities with expanded leadership components. This might be within a unit like the Office of Digital Learning, in other areas of higher ed such as student services or information technology, or in fields outside academia altogether. Named as America’s Best Large Employer by Forbes earlier this year, Notre Dame is a great place both to work and build toward future career success.

    Please get in touch if you are conducting a job search at the intersection of learning, technology and organizational change. If your gig is a good fit, featuring your gig on Featured Gigs is free.

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  • From Detection to Development: How Universities Are Ethically Embedding AI for Learning 

    From Detection to Development: How Universities Are Ethically Embedding AI for Learning 

    This HEPI blog was authored by Isabelle Bristow, Managing Director UK and Europe at Studiosity, a HEPI Partner.  

    The Universities UK Annual Conference always serves as a vital barometer for the higher education sector, and this year, few topics were as prominent as the role of Generative Artificial Intelligence (GenAI). A packed session, Ethical AI in Higher Education for improving learning outcomes: A policy and leadership discussion, provided a refreshing and pragmatic perspective, moving the conversation beyond academic integrity fears and towards genuine educational innovation. 

    Based on early findings from new independent research commissioned by Studiosity, the session’s panellists offered crucial insights and a clear path forward. 

    A new focus: from policing to pedagogy 

    For months, the discussion around Gen-AI has been dominated by concerns over academic misconduct and the development of detection tools. However, as HEPI Director Nick Hillman OBE highlighted, this new report takes a different tack. Its unique focus is on how AI can support active learning, rather than just how students are using it. 

    The findings, presented by independent researcher Rebecca Mace, show a direct correlation between the ethical use of AI for learning and improved student attainment and retention. Crucially, these positive effects were particularly noticeable among students often described as ‘non-traditional’. This reframes the conversation, positioning AI not as a threat to learning but as a powerful tool to enhance it, especially for those who need it most. 

    The analogy that works 

    The ferocious pace of AI’s introduction to the sector has undoubtedly caught many off guard. Professor Marc Griffiths, Pro-Vice Chancellor for Regional Partnerships, Engagement & Innovation at UWE Bristol, acknowledged this head-on, advocating for a dual approach of governance and ‘​​​​sand-boxing’ (the security practice of isolating and testing to make sure an application, system or platform is safe)  of new technologies. Instead of simply denying access, he argued, we must test new tools and develop clear guardrails for their use. 

    In a welcome departure from ​​​​​​​​the widely used but ultimately flawed calculator analogy (​​read more here Generative AI is not a ‘calculator for words’. 5 reasons why this idea is misleading), Professor Griffiths offered a more fitting one: the overhead projector. Like PowerPoint today, the projector was a new technology that was a conduit for content, but it never replaced the core act of teaching and learning itself. AI, he posited, is simply another conduit. It is what we put into it, and what we get out of it, that matters. 

    Evidenced insights and reframing the conversation 

    The panel also grappled with the core questions leaders must ask themselves. Stephanie Harris, Director of Policy at Universities UK posed two fundamental challenges: 

    • How can I safeguard my key product that I am offering to students? 
    • How can I prepare my students for the workforce if I don’t yet know how AI will be used in the future? 

    She stressed the importance of protecting the integrity of the educational experience to prevent an ‘erosion of trust’ between students and institutions. In response to the second question, both Steph and Marc emphasised the answer lies not in specific tech skills, but in timeless critical thinking skills that will prepare students not just for the next three years, but for the next 15. The conversation also touched upon the need for universities to consider students under 16 as the future pipeline, ensuring our policies and frameworks are future-proof. Steph mentioned further prompts for leaders to think about as listed in a UUK-authored, OfS blog Embracing innovation in high education: our approach to artificial intelligence – which was given a commonsense shorthand by Steph as ‘have fun, don’t be stupid!’.  

    The session drove home the importance of evidence-based insights. Dr David Pike, Head of Digital Learning at the University of Bedfordshire, shared key findings from his own research comparing student outcomes for Studiosity users versus those of non-Studiosity users, stating that the results were ‘very clear’ that students did improve at scale. He provided powerful data showing significant measurable academic progress, along with a large positive correlational impact on retention and progression. Dr. Pike concluded that, given this demonstrated positive impact, we should be calling the technology ‘Assisted Intelligence,’ because when used correctly, that is exactly what it is. 

    A guiding framework of values 

    To navigate this new landscape, Professor Griffiths laid out seven core values that must underpin institutional policy on AI: 

    1. Academic integrity: Supporting learning, not replacing it. 
    1. Equity of access: Addressing the real challenge of paywalls. 
    1. Transparency: Clearly communicating how students will be supported. 
    1. Ethical Responsibility 
    1. Empowerment and Capability Building 
    1. Resilience 
    1. Adaptability 

    These values offer a robust framework for leaders looking to create policies that are both consistent and fair, ensuring that AI use aligns with a university’s mission. 

    The policy challenge of digital inequality 

    The issue of equity of access was explored in greater detail by Nick Hillman, who connected the digital divide to the broader student funding landscape. He pointed out that no government had commissioned a proper review on the actual cost of being a student since 1958. With modern student life costing upwards of £20,000 annually if a student wants to involve themselves fully in student life. He made a powerful case for increased maintenance support to match an increased tuition fee, which would also help prevent further disparity between those who can afford premium tech tools and those who cannot. This highlights that addressing digital inequality is not just a technical challenge; it is a fundamental policy one too. 

    In closing 

    The session’s core message was clear: while the rise of AI has been rapid, the sector’s response does not have to be only reactive. By embracing a proactive, values-led approach that prioritises ethical development, equity and human-centric learning, universities can turn what was once seen as a threat into a powerful catalyst for positive change. 

    Studiosity is AI-for-Learning, not corrections – to scale student success, empower educators, and improve retention with a proven , while ensuring integrity and reducing institutional risk. 

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  • There Are No “Shy Students”, Only Poor Learning Environments – Faculty Focus

    There Are No “Shy Students”, Only Poor Learning Environments – Faculty Focus

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  • Why critical data literacy belongs in every K–12 classroom

    Why critical data literacy belongs in every K–12 classroom

    Key points:

    An unexpected group of presenters–11th graders from Whitney M. Young Magnet High School in Chicago–made a splash at this year’s ACM Conference on Fairness, Accountability, and Transparency (FAccT). These students captivated seasoned researchers and professionals with their insights on how school environments shape students’ views of AI. “I wanted our project to serve as a window into the eyes of high school students,” said Autumn Moon, one of the student researchers.

    What enabled these students to contribute meaningfully to a conference dominated by PhDs and industry veterans was their critical data literacy–the ability to understand, question, and evaluate the ethics of complex systems like AI using data. They developed these skills through their school’s Data is Power program.

    Launched last year, Data is Power is a collaboration among K-12 educators, AI ethics researchers, and the Young Data Scientists League. The program includes four pilot modules that are aligned to K-12 standards and cover underexplored but essential topics in AI ethics, including labor and environmental impacts. The goal is to teach AI ethics by focusing on community-relevant topics chosen by our educators with input from students, all while fostering critical data literacy. For example, Autumn’s class in Chicago used AI ethics as a lens to help students distinguish between evidence-based research and AI propaganda. Students in Phoenix explored how conversational AI affects different neighborhoods in their city.

    Why does the Data is Power program focus on critical data literacy? In my former role leading a diverse AI team at Amazon, I saw that technical skills alone weren’t enough. We needed people who could navigate cultural nuance, question assumptions, and collaborate across disciplines. Some of the most technically proficient candidates struggled to apply their knowledge to real-world problems. In contrast, team members trained in critical data literacy–those who understood both the math and the societal context of the models–were better equipped to build responsible, practical tools. They also knew when not to build something.

    As AI becomes more embedded in our lives, and many students feel anxious about AI supplanting their job prospects, critical data literacy is a skill that is not just future-proof–it is future-necessary. Students (and all of us) need the ability to grapple with and think critically about AI and data in their lives and careers, no matter what they choose to pursue. As Milton Johnson, a physics and engineering teacher at Bioscience High School in Phoenix, told me: “AI is going to be one of those things where, as a society, we have a responsibility to make sure everyone has access in multiple ways.”

    Critical data literacy is as much about the humanities as it is about STEM. “AI is not just for computer scientists,” said Karren Boatner, who taught Autumn in her English literature class at Whitney M. Young Magnet High School. For Karren, who hadn’t considered herself a “math person” previously, one of the most surprising parts of the program was how much she and her students enjoyed a game-based module that used middle school math to explain how AI “learns.” Connecting math and literature to culturally relevant, real-world issues helps students see both subjects in a new light.

    As AI continues to reshape our world, schools must rethink how to teach about it. Critical data literacy helps students see the relevance of what they’re learning, empowering them to ask better questions and make more informed decisions. It also helps educators connect classroom content to students’ lived experiences.

    If education leaders want to prepare students for the future–not just as workers, but as informed citizens–they must invest in critical data literacy now. As Angela Nguyen, one of our undergraduate scholars from Stanford, said in her Data is Power talk: “Data is power–especially youth and data. All of us, whether qualitative or quantitative, can be great collectors of meaningful data that helps educate our own communities.”

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  • How Credit for Prior Learning Strengthens Workforce Ties

    How Credit for Prior Learning Strengthens Workforce Ties

    In today’s rapidly evolving workforce landscape, higher education institutions face mounting pressure to demonstrate value, relevance and return on investment. Amid this challenge lies an underutilized strategy with remarkable potential: credit for prior learning.

    We’ve long recognized CPL’s benefits for students. Learners who receive CPL credits are more likely to complete their degrees (49 percent vs. 27 percent for those without) and, on average, they earn 17.6 additional credits, finish nine to 14 months sooner and save between $1,500 and $10,200 in tuition costs (CAEL). But what’s often overlooked is CPL’s power to transform relationships between educational institutions and employers—creating a win-win-win for students, institutions and industry.

    Beyond a Student Benefit

    The traditional narrative around CPL emphasizes student advantages: increased enrollment, improved completion rates and reduced time to graduation. These metrics matter tremendously, but they tell only part of the story.

    CPL can serve as a bridge between academia and industry, creating powerful new partnerships. When colleges and universities embrace robust CPL programs, they send a clear message to employers: We value the training and development you provide. Recognizing corporate training as creditworthy learning demonstrates respect for workplace knowledge and underscores higher education’s commitment to real-world relevance.

    Employer and Workforce Gains

    For employers, CPL validates that their internal training programs have academic merit. This recognition strengthens recruitment and retention efforts, as workers see clear pathways to advance their education without duplicating learning they’ve already mastered. Companies that invest in employee development also gain educational partners who understand industry needs and value the attributes that drive employee success.

    The benefits extend further: Organizations with tuition remission or reimbursement programs can reduce costs while enhancing employee motivation and persistence.

    Deeper Collaboration Between Higher Ed and Industry

    As institutions evaluate workplace training for credit equivalency, they gain invaluable insights into industry practices and skill needs. This exchange allows colleges to refine curricula to better meet market demand, ensuring graduates possess the competencies employers seek—not just those defined within academic silos.

    The hard but necessary conversations—between faculty and corporate training leaders—help ensure CPL evaluations are rigorous and relevant. Key questions include: Why include certain topics but not others? How do we know participants can demonstrate knowledge? Does the training align with broader disciplinary or leadership needs, or is it niche? These discussions strengthen both educational and workplace outcomes.

    Reimagining CPL

    The future of higher education lies in breaking down artificial barriers between academic and workplace learning. By embracing CPL as a cornerstone strategy—not only for student success but also for employer partnerships—institutions can position themselves at the nexus of education and employment.

    This approach doesn’t diminish academic rigor; it expands our understanding of where and how meaningful learning occurs. Done well, CPL creates pathways that honor all learning, regardless of where it happens. And for learners, the message is clear: Your hard work counts.

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  • Learning to debate is an important facet of education, but too often public school students are left out 

    Learning to debate is an important facet of education, but too often public school students are left out 

    Ever since I first stepped onto the debate stage, I have been passionate about speech and debate. For the last three of my high school years, I have competed and placed nationally at major tournaments in Dallas, Los Angeles, Chicago, Atlanta and Las Vegas, among many others. Debate demands an incredible amount of research, preparation and practice, but those aren’t the biggest challenges for me.  

    I attend a public high school in California that lacks a formal debate program or coach, which has forced me to choose between quitting an activity I love and competing independently without any school support.  

    I chose the latter. And that means I prepare alone in the dark, navigate complex registration processes and, most importantly, pay hefty fees. 

    As many of us know, debate is an effective way to strengthen students’ comprehension, critical thinking and presentation skills. Debate allows students to explore ideas in a myriad of topics, from biotechnology to nuclear proliferation​​​​, and find their unique passions and interests. 

    Yet for many students, a lack of school support is a major entry barrier. It has turned debate into another private-school-dominated space, where private-school students receive access to higher quality research and on-the-spot coaching on argument structure and prose, like a football coach adjusting strategy on the sidelines. Additionally, most prestigious tournaments in the U.S. prohibit non-school-affiliated debaters like me from competing altogether.  

    Related: A lot goes on in classrooms from kindergarten to high school. Keep up with our free weekly newsletter on K-12 education.  

    These circumstances de facto prevent lower-income debaters from becoming successful in the activity. And that is why I believe that all schools should incorporate speech and debate classes into their core curriculums. Existing history and English teachers could act as debate coaches, as they do in many private schools. School districts could even combine programs across high schools to save resources while expanding access (Mountain View High School and Los Altos High School in California have pursued this strategy).  

    Over the past two decades, the debate community has engaged in efforts to democratize access to speech and debate through the creation of new formats (for example, public forum), local debate associations and urban debate leagues, among others.  

    However, many of these initiatives haven’t been successful. These newer formats, initially intended to lessen the research burden on debaters, have shifted toward emphasizing strict evidence standards and complex debate jargon. This shift has made debate less, not more, accessible, and led to more students from private schools — who were quickly able to ​​​​out-prepare those from public​​ schools — entering and dominating the competition.  

    Local debate associations and​​​​ competitive leagues for neighboring schools have provided more students with opportunities to participate. Still, debate via these organizations is limited, as they don’t provide direct coaching to member schools or rigorous opportunities for students, and prohibit certain students and programs from competing.  

    Similarly, urban debate leagues (for example, the Los Angeles Metropolitan ​​​​Debate League) have been incredibly successful in expanding debate access to lower-income and minority students; however, these programs are concentrated in major metropolitan cities, face opposition from some school districts and rely on donor funding, which can be uncertain.  

    In my debate rounds, I have analyzed pressing social problems such as global warming and economic inequality through a policymaking lens; in some rounds I defended increased wealth taxes, and in others I argued against bans on fossil fuels. Without debate, I wouldn’t be so conscious of the issues in my community. Now, as I enter college, I’m looking forward to continuing debate and leveraging my skills to fight for change.  

    Related: High school students find common ground on the debate stage 

    Speaking of college, in the competition for admission to the most selective colleges, extracurricular involvement can be a deciding factor, and debate is an excellent way to stand out, at least for those students with proper support.  

    However, when students from rural and low-income communities lack access to the same opportunities as students from more metropolitan and higher-income communities, we risk exacerbating the educational achievement gap to our collective detriment.  

    In the meantime, debate tournaments should reduce entry barriers for nontraditional debaters and for students from public schools without coaches and extra support.  

    Without these initiatives, too many rural and low-income students will be excluded from an amazing activity, one that is especially important in today’s polarizing and divisive climate.  

    Aayush Gandhi is a student at Dublin High School. He is an avid writer and nationally ranked Lincoln-Douglas debater.  

    Contact the opinion editor at [email protected].  

    This story about debate programs was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.  

    The Hechinger Report provides in-depth, fact-based, unbiased reporting on education that is free to all readers. But that doesn’t mean it’s free to produce. Our work keeps educators and the public informed about pressing issues at schools and on campuses throughout the country. We tell the whole story, even when the details are inconvenient. Help us keep doing that.

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  • Are we outsourcing our thinking to AI?

    Are we outsourcing our thinking to AI?

    Key points:

    I’ll admit that I use AI. I’ve asked it to help me figure out challenging Excel formulas that otherwise would have taken me 45 minutes and a few tutorials to troubleshoot. I’ve used it to help me analyze or organize massive amounts of information. I’ve even asked it to help me devise a running training program aligning with my goals and fitting within my schedule. AI is a fantastic tool–and that’s the point. It’s a tool, not a replacement for thinking.

    As AI tools become more capable, more intuitive, and more integrated into our daily lives, I’ve found myself wondering: Are we growing too dependent on AI to do our thinking for us?

    This question isn’t just philosophical. It has real consequences, especially for students and young learners. A recent study published in the journal Societies reports that people who used AI tools consistently showed a decline in critical thinking performance. In fact, “whether someone used AI tools was a bigger predictor of a person’s thinking skills than any other factor, including educational attainment.” That’s a staggering finding because it suggests that using AI might not just be a shortcut. It could be a cognitive detour.

    The atrophy of the mind

    The term “digital dementia” has been used to describe the deterioration of cognitive abilities as a result of over-reliance on digital devices. It’s a phrase originally associated with excessive screen time and memory decline, but it’s found new relevance in the era of generative AI. When we depend on a machine to generate our thoughts, answer our questions, or write our essays, what happens to the neural pathways that govern our own critical thinking? And will the upcoming era of agentic AI expedite this decline?

    Cognitive function, like physical fitness, follows the rule of “use it or lose it.” Just as muscles weaken without regular use, the brain’s ability to evaluate, synthesize, and critique information can atrophy when not exercised. This is especially concerning in the context of education, where young learners are still building those critical neural pathways.

    In short: Students need to learn how to think before they delegate that thinking to a machine.

    Can you still think critically with AI?

    Yes, but only if you’re intentional about it.

    AI doesn’t relieve you of the responsibility to think–in many cases, it demands even more critical thinking. AI produces hallucinations, falsifies claims, and can be misleading. If you blindly accept AI’s output, you’re not saving time, you’re surrendering clarity.

    Using AI effectively requires discernment. You need to know what you’re asking, evaluate what you’re given, and verify the accuracy of the result. In other words, you need to think before, during, and after using AI.

    The “source, please” problem

    One of the simplest ways to teach critical thinking is also the most annoying–just ask my teenage daughter. When she presents a fact or claim that she saw online, I respond with some version of: “What’s your source?” It drives her crazy, but it forces her to dig deeper, check assumptions, and distinguish between fact and fiction. It’s an essential habit of mind.

    But here’s the thing: AI doesn’t always give you the source. And when it does, sometimes it’s wrong, or the source isn’t reputable. Sometimes it requires a deeper dive (and a few more prompts) to find answers, especially to complicated topics. AI often provides quick, confident answers that fall apart under scrutiny.

    So why do we keep relying on it? Why are AI responses allowed to settle arguments, or serve as “truth” for students when the answers may be anything but?

    The lure of speed and simplicity

    It’s easier. It’s faster. And let’s face it: It feels like thinking. But there’s a difference between getting an answer and understanding it. AI gives us answers. It doesn’t teach us how to ask better questions or how to judge when an answer is incomplete or misleading.

    This process of cognitive offloading (where we shift mental effort to a device) can be incredibly efficient. But if we offload too much, too early, we risk weakening the mental muscles needed for sustained critical thinking.

    Implications for educators

    So, what does this mean for the classroom?

    First, educators must be discerning about how they use AI tools. These technologies aren’t going away, and banning them outright is neither realistic nor wise. But they must be introduced with guardrails. Students need explicit instruction on how to think alongside AI, not instead of it.

    Second, teachers should emphasize the importance of original thought, iterative questioning, and evidence-based reasoning. Instead of asking students to simply generate answers, ask them to critique AI-generated ones. Challenge them to fact-check, source, revise, and reflect. In doing so, we keep their cognitive skills active and growing.

    And finally, for young learners, we may need to draw a harder line. Students who haven’t yet formed the foundational skills of analysis, synthesis, and evaluation shouldn’t be skipping those steps. Just like you wouldn’t hand a calculator to a child who hasn’t yet learned to add, we shouldn’t hand over generative AI tools to students who haven’t learned how to write, question, or reason.

    A tool, not a crutch

    AI is here to stay. It’s powerful, transformative, and, when used well, can enhance our work and learning. But we must remember that it’s a tool, not a replacement for human thought. The moment we let it think for us is the moment we start to lose the capacity to think for ourselves.

    If we want the next generation to be capable, curious, and critically-minded, we must protect and nurture those skills. And that means using AI thoughtfully, sparingly, and always with a healthy dose of skepticism. AI is certainly proving it has staying power, so it’s in all our best interests to learn to adapt. However, let’s adapt with intentionality, and without sacrificing our critical thinking skills or succumbing to any form of digital dementia.

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