Tag: Teaching

  • Teaching students to use AI: from digital competence to a learning outcome

    Teaching students to use AI: from digital competence to a learning outcome

    by Concepción González García and Nina Pallarés Cerdà

    Debates about generative AI in higher education often start from the same assumption: students need a certain level of digital competence before they can use AI productively. Those who already know how to search, filter and evaluate online information are seen as the ones most likely to benefit from tools such as ChatGPT, while others risk being left further behind.

    Recent studies reinforce this view. Students with stronger digital skills in areas like problem‑solving and digital ethics tend to use generative AI more frequently (Caner‑Yıldırım, 2025). In parallel, work using frameworks such as DigComp has mostly focused on measuring gaps in students’ digital skills – often showing that perceived “digital natives” are less uniformly proficient than we might think (Lucas et al, 2022). What we know much less about is the reverse relationship: can carefully designed uses of AI actually develop students’ digital competences – and for whom?

    In a recent article, we addressed this question empirically by analysing the impact of a generative AI intervention on university students’ digital competences (García & Pallarés, 2026). Students’ skills were assessed using the European DigComp 2.2 framework (Vuorikari et al, 2022).

    Moving beyond static measures of digital competence

    Research on students’ digital competences in higher education has expanded rapidly over the past decade. Yet much of this work still treats digital competence as a stable attribute that students bring with them into university, rather than as a dynamic and educable capability that can be shaped through instructional design. The consequence is a field dominated by one-off assessments, surveys and diagnostic tools that map students’ existing skills but tell us little about how those skills develop.

    This predominant focus on measurement rather than development has produced a conceptual blind spot: we know far more about how digital competences predict students’ use of emerging technologies than about how educational uses of these technologies might enhance those competences in the first place.

    Recent studies reinforce this asymmetry. Students with higher levels of digital competence are more likely to engage with generative AI tools and to display positive attitudes towards their use (Moravec et al, 2024; Saklaki & Gardikiotis, 2024). In this ‘competence-first’ model, digital competence appears as a precondition for productive engagement with AI. Yet this framing obscures a crucial pedagogical question: might AI, when intentionally embedded in learning activities, actually support the growth of the very competences it is presumed to require?

    A second limitation compounds this problem: the absence of a standardised framework for analysing and comparing the effects of AI-based interventions on digital competence development. Although DigComp is widely used for diagnostic purposes, few studies employ it systematically to evaluate learning gains or to map changes across specific competence areas. As a result, evidence from different interventions remains fragmented, making it difficult to identify which aspects of digital competence are most responsive to AI-mediated learning.

    There is, nevertheless, emerging evidence that AI can do more than simply ‘consume’ digital competence. Studies by Dalgıç et al (2024) and Naamati-Schneider & Alt (2024) suggest that integrating tools such as ChatGPT into structured learning tasks can stimulate information search, analytical reasoning and critical evaluation—provided that students are guided to question and verify AI outputs rather than accept them uncritically. Yet these contributions remain exploratory. We still lack experimental or quasi-experimental evidence that links AI-based instructional designs to measurable improvements in specific DigComp areas, and we know little about whether such benefits accrue equally to all students or disproportionately to those who already possess stronger digital skills.

    This gap matters. If digital competences are conceived as malleable rather than fixed, then AI is not merely a technology that demands certain skills but a pedagogical tool through which those skills can be cultivated. This reframing shifts the centre of the debate: away from asking whether students are ready for AI, and towards asking whether our teaching practices are ready to use AI in ways that promote competence development and reduce inequalities in learning.

    Our study: teaching students to work with AI, not around it

    We designed a randomised controlled trial with 169 undergraduate students enrolled in a Microeconomics course. Students were allocated by class group to either a treatment or a control condition. All students followed the same curriculum and completed the same online quizzes through the institutional virtual campus.

    The crucial difference lay in how generative AI was integrated:

    • In the treatment condition, students received an initial workshop on using large language models strategically. They practised:
    • contextualising questions
    • breaking problems into steps
    • iteratively refining prompts
    • and checking their own solutions before turning to the AI.
    • Throughout the course, their online self-assessments included adaptive feedback: instead of simply marking answers as right or wrong, the system offered hints, step-by-step prompts and suggestions on how to use AI tools as a thinking partner.
    • In the control condition, students completed the same quizzes with standard right/wrong feedback, and no training or guidance on AI.

    Importantly, the intervention did not encourage students to outsource solutions to AI. Rather, it framed AI as an interactive study partner to support self-explanation, comparison of strategies and self-regulation in problem solving.

    We administered pre- and post-course questionnaires aligned with DigComp 2.2, focusing on five competences: information and data literacy, communication and collaboration, safety, and two aspects of problem solving (functional use of digital tools and metacognitive self-regulation). Using a difference-in-differences model with individual fixed effects, we estimated how the probability of reporting the highest level of each competence changed over time for the treatment group relative to the control group.

    What changed when AI was taught and used in this way?

    At the overall sample level, we found statistically significant improvements in three areas:

    • Information and data literacy – students in the AI-training condition were around 15 percentage points more likely to report the highest level of competence in identifying information needs and carrying out effective digital searches.
    • Problem solving – functional dimension – the probability of reporting the top level in using digital tools (including AI) to solve tasks increased by about 24 percentage points.
    • Problem solving – metacognitive dimension – a similar 24-point gain emerged for recognising what aspects of one’s digital competences need to be updated or improved.

    In other words, the AI-integrated teaching design was associated not only with better use of digital tools, but also with stronger awareness of digital strengths and weaknesses – a key ingredient of autonomous learning. Communication and safety competences also showed positive but smaller and more uncertain effects. Here, the pattern becomes clearer when we look at who benefited most.

    A compensatory effect: AI as a potential leveller, not just an amplifier

    When we distinguished students by their initial level of digital competence, a pattern emerged. For those starting below the median, the intervention produced large and significant gains in all five competences, with improvements between 18 and 38 percentage points depending on the area. For students starting above the median, effects were smaller and, in some cases, non-significant.

    This suggests a compensatory effect: students who began the course with weaker digital competences benefited the most from the AI-based teaching design. Rather than widening the digital gap, guided use of AI acted as a levelling mechanism, bringing lower-competence students closer to their more digitally confident peers.

    Conceptually, this challenges an implicit assumption in much of the literature – namely, that generative AI will primarily enhance the learning of already advantaged students, because they are the ones with the skills and confidence to exploit it. Our findings show that, when AI is embedded within intentional pedagogy, explicit training and structured feedback, the opposite can happen: those who started with fewer resources can gain the most.

    From ‘allow or ban’ to ‘how do we teach with AI?’

    For higher education policy and practice, the implications are twofold.

    First, we need to stop thinking of digital competence purely as a prerequisite for using AI. Under the right design conditions, AI can be a pedagogical resource to build those competences, especially in information literacy, problem solving and metacognitive self-regulation. That means integrating AI into curricula not as an add-on, but as part of how we teach students to plan, monitor and evaluate their learning.

    Second, our results suggest that universities concerned with equity and digital inclusion should focus less on whether students have access to AI tools (many already do) and more on who receives support to learn how to use them well. Providing structured opportunities to practise prompting, to critique AI outputs and to reflect on one’s own digital skills may be particularly valuable for students who enter university with lower levels of digital confidence.

    This does not resolve all the ethical and practical concerns around generative AI – far from it. But it shifts the conversation. Instead of treating AI as an external threat to academic integrity that must be tightly controlled, we can start to ask:

    • How can we design tasks where the added value lies in asking good questions, justifying decisions and evaluating evidence, rather than in producing a single ‘correct’ answer?
    • How can we support students to see AI not as a shortcut to avoid thinking, but as a tool to think better and know themselves better as learners?
    • Under what conditions does AI genuinely help to close digital competence gaps, and when might it risk opening new ones?

    Answering these questions will require further longitudinal and multi-institutional research, including replication studies and objective performance measures alongside self-reports. Yet the evidence we present offers a cautiously optimistic message: teaching students how to use AI can be part of a strategy to strengthen digital competences and reduce inequalities in higher education, rather than merely another driver of stratification.

    Concepción González García is Assistant Professor of Economics at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain, and holds a PhD in Economics from the University of Alicante. Her research interests include macroeconomics, particularly fiscal policy, and education.

    Nina Pallarés is Assistant Professor of Economics and Academic Coordinator of the Master’s in Management of Sports Entities at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain. Her research focuses on applied econometrics, with particular emphasis on health, labour, education, and family economics.

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

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  • 3 Questions on Academic Innovation and Centers for Teaching and Learning for BU’s Wendy Colby

    3 Questions on Academic Innovation and Centers for Teaching and Learning for BU’s Wendy Colby

    In the evolving landscape of higher education, centers and institutes dedicated to teaching and learning can no longer be viewed as optional support units; they have become critical engines driving academic innovation at scale. Success in this mission depends on rethinking legacy structures, fostering a culture of collaboration across schools and colleges, identifying opportunities that deliver broad impact, and remaining grounded in the core principles that make teaching truly effective: clarity, engagement, reflection, inclusivity, adaptability, support and rigor.

    Wendy Colby, Vice President and Associate Provost at Boston University, leads BU Virtual and BU’s new Institute for Excellence in Teaching and Learning. The Institute was established to chart a new vision and approach, thoughtfully reconstituting and expanding the role of several former units such as a Center for Teaching and Learning and Digital Learning Innovation. The goal—to create a university-wide nexus for advancing teaching and learning excellence and academic innovation.

    In part one of our conversation, Wendy and I talk about the role of academic innovation centers for teaching and learning (CTLs) within the university ecosystem.

    Courtesy Wendy Colby

    Q1: What does it take to conceive and build a new Institute that truly advances academic innovation and delivers on the promise to create more impact across a faculty community—especially one as large and diverse as you have at Boston University?

    A: It’s interesting. I approached the leadership of the Institute in the same way I approach any new leadership assignment. It started with a lot of listening—being out in the community, talking with faculty and students, and learning what was working well and where challenges persisted. I sat in on classes and was invited to guest lecture in a few. I saw some truly exceptional examples of teaching at its best, and I also examined how faculty and students were—or were not—using technology and tools to support the learning experience.

    Needless to say, I learned a great deal. I saw where innovation was thriving, and where faculty were often tackling similar challenges independently, without a shared platform for collaboration or access to scalable resources. I also saw the importance of leadership and advocacy at the top—working closely with deans and academic leaders to align on shared goals and priorities. It became clear that we needed to create the conditions for faculty to feel that this was an Institute by them and for them.

    From those early observations, we began to define what the Institute needed to be. We established a Strategic Advisory Council and an Institute Faculty Liaison group, comprised of faculty champions from across our schools and colleges. We also formed close alliances with key partners—most notably the Office of Undergraduate Affairs and the Dean of Students—to clarify the Institute’s role in student success and the broader student experience. These partnerships, built early in the Institute’s formation, have been essential to creating the conditions for success.

    Ultimately, what emerged most clearly is that our success depends on shared ownership. Impact at this scale cannot be achieved by any single unit alone. In a university comprised of many distinct academic and operational units, each with its own priorities and ways of working, collective alignment is not automatic—but it is powerful. When we operate as a connected university, able to harness, contribute to, and amplify the extraordinary work already happening across our community, the potential for impact is far greater than the sum of its parts.

    Q2: What are some of the initial initiatives you have created at the Institute, and how are these fostering meaningful faculty engagement and better student experiences?

    A: I’ll start by saying that I’ve now spoken with many leaders at other institutions who are either leading similar centers or institutes or working to transform their own in more meaningful ways. A common theme I hear is that these units often have incredibly talented teams—frequently comprised of former faculty—who work hard to develop thoughtful programming, offer workshops and create resources grounded in evidence-based pedagogy. Yet, too often, their impact is limited. They struggle to establish a clear institutional identity; they compete for faculty attention; and they tend to reach only a small subset of the community. Historically, that was a challenge we faced at BU as well.

    So, in addition to establishing a new vision and direction for the Institute—and situating it in a more central, visible location on campus—we organized our initial work around a small number of shared, strategic initiatives designed for scale, coherence and broad relevance.

    One of the Institute’s core initiatives is a campus-wide focus on AI in teaching and learning, developed in close collaboration with the AI Development Accelerator (AIDA) —a new center created to position Boston University at the forefront of leadership in AI. By combining the AIDA’s strategic and technical expertise with the Institute’s focus on pedagogy, faculty engagement and evidence-based practice, this collaboration enables a coordinated, university-wide approach to education, experimentation and responsible integration of AI into teaching and learning. Early efforts include the launch of a new AI at BU online certificate available to all undergraduate students, as well as discipline-specific studio sessions, guidance and resources designed to help faculty integrate AI thoughtfully into curriculum, assessment and classroom practice.

    Another flagship effort is the Classroom LX (Learning Experience) Transformation Initiative, which was created to address the overall student experience across courses and programs. Through early listening sessions with faculty and students, we identified a recurring challenge: While individual instructors were doing excellent work, students often encountered inconsistent experiences from course to course—particularly in the use of digital platforms, learning resources, communication norms and expectations. These inconsistencies created unnecessary friction for students and additional work for faculty.

    The Classroom LX initiative focuses on establishing shared principles, practices and design patterns that enhance the learning experience without constraining academic freedom. By providing faculty with common frameworks, toolkits and support for course design—especially in high-enrollment and foundational courses—we aim to reduce friction, improve clarity and engagement, and create more inclusive and supportive learning environments. Importantly, this work is codeveloped with faculty, grounded in evidence-based pedagogy, and designed to scale across disciplines while remaining flexible enough to meet individual needs.

    Together, these initiatives are just a few examples of the shift we are making from isolated programming to intentional, institution-wide efforts that support faculty, improve student experiences and create lasting impact at scale.

    Q3: Looking ahead, what role do you see Institutes like this playing in the future of higher education, and what lessons might other universities take from this work?

    A: I believe teaching and learning institutes must evolve from “centers of excellence” into strategic engines that help drive institutional priorities. Our role extends beyond faculty support and engagement alone—it is about connecting pedagogy, scholarly research, technology, the student experience and career readiness. We are also addressing themes like digital wellness and the growing connection between student wellness and academic success. Institutes like ours sit at the intersection of academic mission and institutional change. 

    At a time when higher education faces significant disruption, our institutes have an opportunity to accelerate long-needed conversations about curricular innovation, assessment in the age of AI, learning design, inclusion and wellbeing, and what it truly means to educate students in a rapidly changing world. Our teams are uniquely positioned to guide and partner on this work thoughtfully and responsibly.

    At the same time, there is no single blueprint for success. Every university has its own culture, structures, and constraints, and meaningful change must be grounded in that reality. For me, the work always begins with learning and listening—being present in classrooms, engaging faculty and students, and observing teaching and learning in practice. You are rarely handed a clear roadmap. Instead, the work requires synthesizing what you hear, identifying recurring patterns and shared challenges, and distinguishing between isolated issues and opportunities for broader impact. That sense-making—turning complexity into clarity—is a critical leadership skill in and of itself.

    Equally important is building coalitions early. Sustainable impact depends on strong partnerships with academic leaders, deans, faculty and students, and on creating shared ownership around priorities and outcomes. When institutes are able to convene communities, align stakeholders and design initiatives that respect academic autonomy while supporting scale, they can become powerful catalysts for change. Ultimately, the lesson is that teaching and learning innovation succeeds not through mandates or isolated programs, but through trust, collaboration and a sustained commitment to improving the educational experience for all learners.

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  • Neuroinclusive teaching in higher education

    Neuroinclusive teaching in higher education

    Over the weekend, HEPI published a blog on where universities go from here.

    This blog was kindly authored by Lewis Eves, Teaching Associate at the University of Nottingham.

    Neuroinclusivity is increasingly important in Higher Education. A recent survey shows that 22% of UK students have a neurodivergent diagnosis (e.g. ADHD or Autism), with up to 28% identifying as neurodivergent in some way. This constitutes an overrepresentation of neurodivergence among students compared to the general population, of which only 15-20% are diagnosed as neurodivergent.

    Meanwhile neurodivergent disclosure rates among academic staff are significantly lower. In 2023, only 1.8% of academic staff disclosed a neurodivergent condition.

    It’s unsurprising that neurodivergent people would be underrepresented in academia. Academia is structured around neurotypical behaviours, activities and thought processes that are intuitive for those whose brain functions conform to the collective standard.

    As such, academia privileges ways of working that are difficult for neurodivergent people to navigate. The need to excel in, and constantly switch between, research, teaching and administrative tasks poses challenges for neurodivergent staff. This discourages neurodivergent people from pursuing careers in academia. Meanwhile, those who do pursue academia express fear and anxiety that disclosing their neurodivergence might negatively impact their careers.

    The growing gap between the number of neurodivergent academics and students poses a challenge for higher education. How is a traditionally neurotypical environment, lacking in lived experience of neurodivergence, going to adapt to the learning needs of an increasingly neurodivergent community?

    Neuroinclusive teaching

    As a neurodivergent academic, I often reflect on the challenges I faced as a student. I use this lived experience to inform my teaching practice, employing various techniques and measures to support the learning of neurodivergent students.

    Inductive Teaching

    Teaching in Higher Education is mostly deductive. A top-down approach that focusses on teaching staff telling students what is important to know, providing examples and testing students’ understanding.

    This is something that I struggled with as a student, and is something that my neurodivergent students have shared that they struggle with too. I suspect the issue is that the way a neurotypical teacher links ideas and concepts to real-world examples is not as intuitive for neurodivergent students. Neurodivergence means that brains link and connect information differently.

    To address this, I employ inductive approaches in my teaching. This involves focussing first on examples, preferably examples from students’ lived experience, and using these examples to discuss and learn key ideas and concepts. This enables students to connect examples and concepts in a way that is intuitive for them. It is also a more collaborative learning process, promoting discussion and sharing of ideas that I find benefits both neurodivergent and neurotypical students.

    Structure

    Secondary and further education are highly structured learning environments, with students’ learning time being timetabled and supervised. Higher education, however, is much less structured. Some subjects have very few timetabled sessions, with a significant emphasis on independent study.

    Many students struggle with this transition into a less structured learning environment. However, this sudden drop in structure is something that neurodivergent students particularly struggle with. It was something that I struggled with as a student, and now that I teach, it is one of the more common topics that my neurodivergent students wish to discuss with me.

    To help address this issue, I support the structuring of students’ independent study. One method I use is hosting regular study workshops in which students can complete their assignments. I facilitate these using techniques like body doubling. This is a productivity technique commonly used by those with ADHD, which relies on the natural rhythms of productivity in shared workspaces to encourage focus. All students are welcome, and the feedback has been overwhelmingly positive, especially from neurodivergent students.

    Neuroinclusive policy

    In my experience of teaching in higher education, there are opportunities to develop teaching practice to better support the increasing number of neurodivergent students. In my experience of teaching in higher education, there are opportunities to develop teaching practice to better support the increasing number of neurodivergent students. However, this will need to be done sector-wide, which will require supportive and effective policymaking.

    These policies should promote teaching and learning practices that make learning environments more accessible, equitable and inclusive. These require co-creation with the neurodivergent community, who are underrepresented in academia. Accordingly, for policy to promote neuroinclusive teaching and learning for students, it must also promote academic neuroinclusion.

    Achieving this will require a decoupling of academic performance monitoring and career progression from neurotypical behaviours. This will help address barriers to disclosure and empower neurodivergent academics to more effectively inform teaching and learning practices based on their lived experience.

    Research and guidance from UCL lists numerous suggestions that could be incorporated into broader policy. This includes:

    • Promoting greater flexibility and accessibility in research, focussing on the depth of contributions rather than the breadth of activity neurotypical scholars may engage in.
    • Challenging the culture of ‘publish-or-perish’ that privileges quick publication, recognising the value of slower, high-quality research that alleviates pressure on both neurodivergent and neurotypical researchers.

    Changes like these will take time. However, if the higher education sector is serious about creating a neuroinclusive environment and effectively supporting the growing demographic of neurodivergent students, we need to take these steps.

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  • Bridging the Divide: Teaching Across Online and In-Person Classrooms – Faculty Focus

    Bridging the Divide: Teaching Across Online and In-Person Classrooms – Faculty Focus

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  • Bridging the Divide: Teaching Across Online and In-Person Classrooms – Faculty Focus

    Bridging the Divide: Teaching Across Online and In-Person Classrooms – Faculty Focus

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  • Sparking civic engagement as we approach America’s 250th

    Sparking civic engagement as we approach America’s 250th

    Key points:

    Imagine students who understand how government works and who see themselves as vital contributors to their communities. That’s what happens when students are given opportunities to play a role in their school, district, and community. In my work as a teacher librarian, I have learned that even the youngest voices can be powerful, and that students embrace civic responsibility and education when history is taught in a way that’s relevant and meaningful. 

    Now is the moment to build momentum and move our curriculum forward. It’s time to break past classroom walls and unite schools and communities. As our nation’s 250th anniversary approaches, education leaders have a powerful opportunity to teach through action and experience like never before. 

    Kids want to matter. When we help them see themselves as part of the world instead of watching it pass by, they learn how to act with purpose. By practicing civic engagement, students gain the skills to contribute solutions–and often offer unique viewpoints that drive real change. In 2023, I took my students [CR1] to the National Mall. They were in awe of how history was represented in stone, how symbolism was not always obvious, and they connected with rangers from the National Park Service as well as visitors in D.C. that day. 

    When students returned from the Mall, they came back with a question that stuck: “Where are the women?” In 2024, we set out to answer two questions together: “Whose monuments are missing?” and “What is HER name?” 

    Ranger Jen at the National Mall, with whom I worked with before, introduced me to Dr. Linda Booth Sweeney, author of Monument Maker, which inspired my approach. Her book asks, “History shapes us–how will we shape history?” Motivated by this challenge, students researched key women in U.S. history and designed monuments to honor their contributions. 

    We partnered with the Women’s Suffrage National Monument, and some students even displayed their work at the Belmont-Paul Women’s Equality National Monument. Through this project, questions were asked, lessons were learned, and students discovered the power of purpose and voice. By the end of our community-wide celebration, National Mall Night, they were already asking, “What’s next?” 

    The experience created moments charged with importance and emotion–moments students wanted to revisit and replicate as they continue shaping history themselves. 

    Reflecting on this journey, I realized I often looked through a narrow lens, focusing only on what was immediately within my school. But the broader community, both local and online, is full of resources that can strengthen relationships, provide materials, and offer strategies, mentors, and experiences that extend far beyond any initial lesson plan. 

    Seeking partnerships is not a new idea, but it can be easily overlooked or underestimated. I’ve learned that a “no” often really means “not yet” or “not now,” and that persistence can open doors. Ford’s Theatre introduced me to Ranger Jen, who in turn introduced me to Dr. Sweeney and the Trust for the National Mall. When I needed additional resources, the Trust for the National Mall responded, connecting me with the new National Mall Gateway: a new digital platform inspired by America’s 250th that gives all students, educators and visitors access to explore and connect with history and civics through the National Mall. 

    When I first shared the Gateway with students, it took their breath away. They could reconnect with the National Mall–a place they were passionate about–with greater detail and depth. I now use the platform to teach about monuments and memorials, to prepare for field trips, and to debrief afterward. The platform brings value for in-person visits to the National Mall, and for virtual field trips in the classroom, where they can almost reach out and touch the marble and stone of the memorials through 360-degree video tours. 

    Another way to spark students’ interest in civics and history is to weave civic learning into every subject. The first step is simple but powerful: Give teachers across disciplines the means to integrate civic concepts into their lessons. This might mean collaborating with arts educators and school librarians to design mini-lessons, curate primary sources, or create research challenges that connect past and present. It can also take shape through larger, project-based initiatives that link classroom learning to real-world issues. Science classes might explore the policies behind environmental conservation, while math lessons could analyze community demographics or civic data. In language arts, students might study speeches, letters, or poetry to see how language drives change. When every subject and resource become hubs for civic exploration, students begin to see citizenship as something they live, not just study. 

    Students thrive when their learning has purpose and connection. They remember lessons tied to meaningful experiences and shared celebrations. For instance, one of our trips to the National Mall happened when our fourth graders were preparing for a Veterans Day program with patriotic music. Ranger Jen helped us take it a step further, building on previous partnerships and connections–she arranged for the students to sing at the World War II Memorial. As they performed “America,” Honor Flights unexpectedly arrived. The students were thrilled to sing in the nation’s capital, of course. But the true impact came from their connection with the veterans who had lived the history they were honoring. 

    As our nation approaches its 250th anniversary, we have an extraordinary opportunity to help students see themselves as part of the story of America’s past, present, and future.

    Encourage educator leaders to consider how experiential civics can bring this milestone to life. Invite students to engage in authentic ways, whether through service-learning projects, policy discussions, or community partnerships that turn civic learning into action. Create spaces in your classes for collaboration, reflection, and application, so that students are shaping history, not just studying it. Give students more than a celebration. Give them a sense of purpose and belonging in the ongoing story of our nation. 

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  • Teaching Students Agency in the Age of AI

    Teaching Students Agency in the Age of AI

    Students have little opportunity to practice agency when an LMS tracks their assignments, they’re not encouraged to explore different majors and colleges shrink general education requirements, according to writer and educator John Warner.

    In the latest episode of The Key, Inside Higher Ed’s news and analysis podcast, Warner tells IHE’s editor in chief, Sara Custer, that colleges should refocus on teaching students how to learn and grow.

    “Agency writ large is the thing we need to survive as people … but it’s also a fundamental part of learning, particularly writing.”

    Warner argues that with the arrival of AI, helping students develop agency is even more of an imperative for higher education institutions.

    “AI is a homework machine … Our response cannot be ‘you’re just going to make this thing using AI now,’” Warner said. “More importantly than this is not learning anything, it is a failure to confront [the question]: What do we, as humans, do now with this technology?”

    Warner also shares what he’s learned from consulting and speaking about teaching and AI at campuses across the country. Ultimately, he says, faculty can work with AI in a way that still aligns with their institutional values.

    Listen to the full episode.

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  • Measuring student global competency learning using direct peer connections

    Measuring student global competency learning using direct peer connections

    Key points:

    Our students are coming of age in a world that demands global competency. From economic interdependence to the accelerating effects of climate change and mass migration, students need to develop the knowledge and skills to engage and succeed in this diverse and interconnected world. Consequently, the need for global competency education is more important than ever.

    “Being born into a global world does not make people global citizens,” Andreas Schleicher of the Organization for Economic Cooperation and Development (OECD) has said. “We must deliberately and systematically educate our children in global competence.” 

    Here at Global Cities, we regularly talk with educators eager to bring global competency into their classrooms in ways that engage and excite students to learn. Educators recognize the need, but ask a vital question: How do we teach something we can’t measure?

    It’s clear that in today’s competitive and data-driven education environment, we need to expand and evaluate what students need to know to be globally competent adults. Global competency education requires evaluation tools to determine what and whether students are learning.

    The good news is that two recent independent research studies found that educators can use a new tool, the Global Cities’ Codebook for Global Student Learning Outcomes, to identify what global competency learning looks like and to assess whether students are learning by examining student writing. The research successfully used the evaluation tool for global competency programs with different models and curricula and across different student populations.

    Global Cities developed the Codebook to help researchers, program designers, and educators identify, teach, and measure global competency in their own classrooms. Created in partnership with Harvard Graduate School of Education’s The Open Canopy, the Codebook captures 55 observable indicators across four core global learning outcomes: Appreciation for Diversity, Cultural Understanding, Global Knowledge, and Global Engagement. The Codebook was developed using data from our own Global Scholars virtual exchange program, which since 2014 has connected more than 139,000 students in 126 cities worldwide to teach global competency.

    In Global Scholars, we’ve seen firsthand the excitement of directly connecting students with their international peers and sparking meaningful discussions about culture, community, and shared challenges. We know how teachers can effectively use the Codebook and how Global Cities workshops extend the reach of this approach to a larger audience of K-12 teachers. This research was designed to determine whether the same tool could be used to assess global competency learning in other virtual exchange programs, not only Global Cities’ Global Scholars program.

    These studies make clear that the Codebook can reliably identify global learning in diverse contexts and help educators see where and how their students are developing global competency skills in virtual exchange curricula. You can examine the tool (the Codebook) here. You can explore the full research findings here.

    The first study looked at two AFS Intercultural Programs curricula, Global You Changemaker and Global Up Teen. The second study analyzed student work from The Open Canopy‘s Planetary Health and Remembering the Past learning journeys.

    In the AFS Intercultural Programs data, researchers found clear examples of students from across the globe showing Appreciation for Diversity and Cultural Understanding. In these AFS online discussion boards, students showed evidence they were learning about their own and other cultures, expressed positive attitudes about one another’s cultures, and demonstrated tolerance for different backgrounds and points of view. Additionally, the discussion boards offered opportunities for students to interact with each other virtually, and there were many examples of students from different parts of the world listening to one another and interacting in positive and respectful ways. When the curriculum invited students to design projects addressing community or global issues, they demonstrated strong evidence of Global Engagement as well.

    Students in The Open Canopy program demonstrated the three most prevalent indicators of global learning that reflect core skills essential to effective virtual exchange: listening to others and discussing issues in a respectful and unbiased way; interacting with people of different backgrounds positively and respectfully; and using digital tools to learn from and communicate with peers around the world. Many of the Remembering the Past posts were especially rich and coded for multiple indicators of global learning.

    Together, these studies show that global competency can be taught–and measured. They also highlight simple, but powerful strategies educators everywhere can use:

    • Structured opportunities for exchange help students listen and interact respectfully with one another
    • Virtual exchange prompts students to share their cultures and experiences across lines of difference in positive, curious ways
    • Assignments that include reflection questions–why something matters, not just what it is–help students think critically about culture and global issues
    • Opportunities for students to give their opinion and to decide to take action, even hypothetically, builds their sense of agency in addressing global challenges

    The Codebook is available free to all educators, along with hands-on professional development workshops that guide teachers in using the tool to design curriculum, teach intentionally, and assess learning. Its comprehensive set of indicators gives educators and curriculum designers a menu of options–some they might not have initially considered–that can enrich students’ global learning experiences.

    Our message to educators is simple: A community of educators (Global Ed Lab), a research-supported framework, and practical tools can help you teach students global competency and evaluate their work.

    The question is no longer whether we need more global competency education. We clearly do. Now with the Codebook and the Global Ed Lab, teachers can learn how to teach this subject matter effectively and use tools to assess student learning.

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  • Teaching visual literacy as a core reading strategy in the age of AI

    Teaching visual literacy as a core reading strategy in the age of AI

    Key points:

    Many years ago, around 2010, I attended a professional development program in Houston called Literacy Through Photography, at a time when I was searching for practical ways to strengthen comprehension, discussion, and reading fluency, particularly for students who found traditional print-based tasks challenging. As part of the program, artists visited my classroom and shared their work with students. Much of that work was abstract. There were no obvious answers and no single “correct” interpretation.

    Instead, students were invited to look closely, talk together, and explain what they noticed.

    What struck me was how quickly students, including those who struggled with traditional reading tasks, began to engage. They learned to slow down, describe what they saw, make inferences, and justify their thinking. They weren’t just looking at images; they were reading them. And in doing so, they were rehearsing many of the same strategies we expect when reading written texts.

    At the time, this felt innovative. But it also felt deeply intuitive.

    Fast forward to today.

    Students are surrounded by images and videos, from photographs and diagrams to memes, screenshots, and, increasingly, AI-generated visuals. These images appear everywhere: in learning materials, on social media, and inside the tools students use daily. Many look polished, realistic, and authoritative.

    At the same time, AI has made faking easier than ever.

    As educators and school leaders, we now face urgent questions around misinformation, academic integrity, and critical thinking. The issue is no longer just whether students can use AI tools, but whether they can interpret, evaluate, and question what they see.

    This is where visual literacy becomes a frontline defence.

    Teaching students to read images critically, to see them as constructed texts rather than neutral data, strengthens the same skills we rely on for strong reading comprehension: inference, evidence-based reasoning, and metacognitive awareness.

    From photography to AI: A conversation grounded in practice

    Recently, I found myself returning to those early classroom experiences through ongoing professional dialogue with a former college lecturer and professional photographer, as we explored what it really means to read images in the age of AI.

    A conversation that grew out of practice

    Nesreen: When I shared the draft with you, you immediately focused on the language, whether I was treating images as data or as signs. Is this important?

    Photographer: Yes, because signs belong to reading. Data is output. Signs are meaning. When we talk about reading media texts, we’re talking about how meaning is constructed, not just what information appears.

    Nesreen: That distinction feels crucial right now. Students are surrounded by images and videos, but they’re rarely taught to read them with the same care as written texts.

    Photographer: Exactly. Once students understand that photographs and AI images are made up of signs, color, framing, scale, and viewpoint, they stop treating images as neutral or factual.

    Nesreen: You also asked whether the lesson would lean more towards evaluative assessment or summarizing. That made me realize the reflection mattered just as much as the image itself.

    Photographer: Reflection is key. When students explain why a composition works, or what they would change next time, they’re already engaging in higher-level reading skills.

    Nesreen: And whether students are analyzing a photograph, generating an AI image, or reading a paragraph, they’re practicing the same habits: slowing down, noticing, justifying, and revising their thinking.

    Photographer: And once they see that connection, reading becomes less about the right answer and more about understanding how meaning is made.

    Reading images is reading

    One common misconception is that visual literacy sits outside “real” literacy. In practice, the opposite is true.

    When students read images carefully, they:

    • identify what matters most
    • follow structure and sequence
    • infer meaning from clues
    • justify interpretations with evidence
    • revise first impressions

    These are the habits of skilled readers.

    For emerging readers, multilingual learners, and students who struggle with print, images lower the barrier to participation, without lowering the cognitive demand. Thinking comes first. Language follows.

    From composition to comprehension: Mapping image reading to reading strategies

    Photography offers a practical way to name what students are already doing intuitively. When teachers explicitly teach compositional elements, familiar reading strategies become visible and transferable.

    What students notice in an image What they are doing cognitively Reading strategy practiced
    Where the eye goes first Deciding importance Identifying main ideas
    How the eye moves Tracking structure Understanding sequence
    What is included or excluded Considering intention Analyzing author’s choices
    Foreground and background Sorting information Main vs supporting details
    Light and shadow Interpreting mood Making inferences
    Symbols and colour Reading beyond the literal Figurative language
    Scale and angle Judging power Perspective and viewpoint
    Repetition or pattern Spotting themes Theme identification
    Contextual clues Using surrounding detail Context clues
    Ambiguity Holding multiple meanings Critical reading
    Evidence from the image Justifying interpretation Evidence-based responses

    Once students recognise these moves, teachers can say explicitly:

    “You’re doing the same thing you do when you read a paragraph.”

    That moment of transfer is powerful.

    Making AI image generation teachable (and safe)

    In my classroom work pack, students use Perchance AI to generate images. I chose this tool deliberately: It is accessible, age-appropriate, and allows students to iterate, refining prompts based on compositional choices rather than chasing novelty.

    Students don’t just generate an image once. They plan, revise, and evaluate.

    This shifts AI use away from shortcut behavior and toward intentional design and reflection, supporting academic integrity rather than undermining it.

    The progression of a prompt: From surface to depth (WAGOLL)

    One of the most effective elements of the work pack is a WAGOLL (What A Good One Looks Like) progression, which shows students how thinking improves with precision.

    • Simple: A photorealistic image of a dog sitting in a park.
    • Secure: A photorealistic image of a dog positioned using the rule of thirds, warm colour palette, soft natural lighting, blurred background.
    • Greater Depth: A photorealistic image of a dog positioned using the rule of thirds, framed by tree branches, low-angle view, strong contrast, sharp focus on the subject, blurred background.

    Students can see and explain how photographic language turns an image from output into meaningful signs. That explanation is where literacy lives.

    When classroom talk begins to change

    Over time, classroom conversations shift.

    Instead of “I like it” or “It looks real,” students begin to say:

    • “The creator wants us to notice…”
    • “This detail suggests…”
    • “At first I thought…, but now I think…”

    These are reading sentences.

    Because images feel accessible, more students participate. The classroom becomes slower, quieter, and more thoughtful–exactly the conditions we want for deep comprehension.

    Visual literacy as a bridge, not an add-on

    Visual literacy is not an extra subject competing for time. It is a bridge, especially in the age of AI.

    By teaching students how to read images, schools strengthen:

    • reading comprehension
    • inference and evaluation
    • evidence-based reasoning
    • metacognitive awarenes

    Most importantly, students learn that literacy is not about rushing to answers, but about noticing, questioning, and constructing meaning.

    In a world saturated with AI-generated images, teaching students how to read visually is no longer optional.

    It is literacy.

    Author’s note: This article grew out of classroom practice and professional dialogue with a former college lecturer and professional photographer. Their contribution informed the discussion of visual composition, semiotics, and reflective image-reading, without any involvement in publication or authorship.

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  • How can we create the conditions to inspire young leaders to say ‘yes’ to teaching careers?

    How can we create the conditions to inspire young leaders to say ‘yes’ to teaching careers?

    This audio is auto-generated. Please let us know if you have feedback.

    Beatrice Viramontes is the executive director of Teach For America Bay Area in California, a nonprofit that prepares diverse, talented individuals to become teachers.

    There’s no shortage of polling or think pieces trying to wrap our collective heads around the youngest group of American adults: Gen Z. While those efforts provide myriad valuable insights, one thing in particular sticks out — members of Gen Z bring to the table unique perspectives on working, careers and what they’re looking for in a job.

    This is a headshot of Beatrice Viramontes, executive director of Teach For America Bay Area in California.

    Beatrice Viramontes

    Permission granted by Beatrice Viramontes

     

    While conversations about the role of the American teacher have long been happening, the arrival of Gen Z to the workplace has forced the conversation to the forefront of priorities for those of us in education. That conversation overlaps with another long-running crisis in education: a shortage of teachers, especially in the most underserved public schools. 

    In a 2024 poll, Educators for Excellence found that only 16% of teachers said they would recommend the profession to others. On top of that, the percentage of teachers who said they planned to stay in the classroom for their entire career was 77%, down nine percentage points from 2022.

    At Teach For America Bay Area, which I lead, we’ve created a collaborative alongside local partners to tackle a key question: How can we create the conditions to inspire young leaders to say yes to a career in teaching and sustain great teachers — of many generations — in the profession?

    We are not the first to begin engaging with this important question. In fact, we’re learning from examples from across the country in the hopes that we can bring to our own community solutions that are working elsewhere. 

    In reimagining the role of the classroom teacher, we can connect with what Gen Z folks are looking for in a job, ignite their spark for education, improve staffing and teacher retention in our schools and, most importantly, best serve our students. 

    Here’s one way we can do this. 

    If you were to walk into most American public elementary school classrooms today, you’d likely see the following: one elementary school teacher, in front of her roster of maybe about 30 children. She’d likely be with that group of children all day — leading their lessons in math, reading, writing, science and social studies. She’d accompany them to lunch and recess, and perhaps would get a break when they went to music, art or PE for an hour. 

    Each day, she has to prepare, internalize and execute those lessons and adjust them to meet all of her students’ various needs — in math, reading, writing, science and social studies. 

    This is probably the elementary school model you grew up with. I know I did. But this “one teacher, one classroom” model, while surely effective for some, doesn’t mesh well with the interests of the next generation entering the workforce, or with the learning needs of all students. 

    There is limited agency and flexibility — in many cases, it’s pretty rigid. It’s linked with fewer people entering the education profession and more people leaving it. 

    It also hasn’t seen a “refresh” in decades. Additionally, it contributes to the burnout of teachers from many generations, not to mention the impact on students. Meanwhile, our world is rapidly evolving and changing. We need to rethink this model in order to accelerate outcomes for students and attract great talent into the teaching profession. 

    In 2019, the Next Education Workforce initiative at Arizona State University created a pilot team-based approach at a single school to try to tackle this workforce design challenge in traditional education. In 2022, they launched a learning cohort for schools interested in exploring new types of staffing models — working with 100 educator teams across 10 school systems in Arizona and California. 

    The Center on Reinventing Public Education has been examining the progress along the way. 

    ASU NEW developed an innovative staffing strategy — allowing multiple teachers to work together across different subjects within a single school, rather than one teacher instructing one classroom of students. In this approach, four to five teachers are taking responsibility for about 100 students, depending on the grade level. 

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