Category: digital

  • Helping students evaluate AI-generated content

    Helping students evaluate AI-generated content

    Key points:

    Finding accurate information has long been a cornerstone skill of librarianship and classroom research instruction. When cleaning up some materials on a backup drive, I came across an article I wrote for the September/October 1997 issue of Book Report, a journal directed to secondary school librarians. A generation ago, “asking the librarian” was a typical and often necessary part of a student’s research process. The digital tide has swept in new tools, habits, and expectations. Today’s students rarely line up at the reference desk. Instead, they consult their phones, generative AI bots, and smart search engines that promise answers in seconds. However, educators still need to teach students the ability to be critical consumers of information, whether produced by humans or generated by AI tools.

    Teachers haven’t stopped assigning projects on wolves, genetic engineering, drug abuse, or the Harlem Renaissance, but the way students approach those assignments has changed dramatically. They no longer just “surf the web.” Now, they engage with systems that summarize, synthesize, and even generate research responses in real time.

    In 1997, a keyword search might yield a quirky mix of werewolves, punk bands, and obscure town names alongside academic content. Today, a student may receive a paragraph-long summary, complete with citations, created by a generative AI tool trained on billions of documents. To an eighth grader, if the answer looks polished and is labeled “AI-generated,” it must be true. Students must be taught how AI can hallucinate or simply be wrong at times.

    This presents new challenges, and opportunities, for K-12 educators and librarians in helping students evaluate the validity, purpose, and ethics of the information they encounter. The stakes are higher. The tools are smarter. The educator’s role is more important than ever.

    Teaching the new core four

    To help students become critical consumers of information, educators must still emphasize four essential evaluative criteria, but these must now be framed in the context of AI-generated content and advanced search systems.

    1. The purpose of the information (and the algorithm behind it)

    Students must learn to question not just why a source was created, but why it was shown to them. Is the site, snippet, or AI summary trying to inform, sell, persuade, or entertain? Was it prioritized by an algorithm tuned for clicks or accuracy?

    A modern extension of this conversation includes:

    • Was the response written or summarized by a generative AI tool?
    • Was the site boosted due to paid promotion or engagement metrics?
    • Does the tool used (e.g., ChatGPT, Claude, Perplexity, or Google’s Gemini) cite sources, and can those be verified?

    Understanding both the purpose of the content and the function of the tool retrieving it is now a dual responsibility.

    2. The credibility of the author (and the credibility of the model)

    Students still need to ask: Who created this content? Are they an expert? Do they cite reliable sources? They must also ask:

    • Is this original content or AI-generated text?
    • If it’s from an AI, what sources was it trained on?
    • What biases may be embedded in the model itself?

    Today’s research often begins with a chatbot that cannot cite its sources or verify the truth of its outputs. That makes teaching students to trace information to original sources even more essential.

    3. The currency of the information (and its training data)

    Students still need to check when something was written or last updated. However, in the AI era, students must understand the cutoff dates of training datasets and whether search tools are connected to real-time information. For example:

    • ChatGPT’s free version (as of early 2025) may only contain information up to mid-2023.
    • A deep search tool might include academic preprints from 2024, but not peer-reviewed journal articles published yesterday.
    • Most tools do not include digitized historical data that is still in manuscript form. It is available in a digital format, but potentially not yet fully useful data.

    This time gap matters, especially for fast-changing topics like public health, technology, or current events.

    4. The wording and framing of results

    The title of a website or academic article still matters, but now we must attend to the framing of AI summaries and search result snippets. Are search terms being refined, biased, or manipulated by algorithms to match popular phrasing? Is an AI paraphrasing a source in a way that distorts its meaning? Students must be taught to:

    • Compare summaries to full texts
    • Use advanced search features to control for relevance
    • Recognize tone, bias, and framing in both AI-generated and human-authored materials

    Beyond the internet: Print, databases, and librarians still matter

    It is more tempting than ever to rely solely on the internet, or now, on an AI chatbot, for answers. Just as in 1997, the best sources are not always the fastest or easiest to use.

    Finding the capital of India on ChatGPT may feel efficient, but cross-checking it in an almanac or reliable encyclopedia reinforces source triangulation. Similarly, viewing a photo of the first atomic bomb on a curated database like the National Archives provides more reliable context than pulling it from a random search result. With deepfake photographs proliferating the internet, using a reputable image data base is essential, and students must be taught how and where to find such resources.

    Additionally, teachers can encourage students to seek balance by using:

    • Print sources
    • Subscription-based academic databases
    • Digital repositories curated by librarians
    • Expert-verified AI research assistants like Elicit or Consensus

    One effective strategy is the continued use of research pathfinders that list sources across multiple formats: books, journals, curated websites, and trusted AI tools. Encouraging assignments that require diverse sources and source types helps to build research resilience.

    Internet-only assignments: Still a trap

    Then as now, it’s unwise to require students to use only specific sources, or only generative AI, for research. A well-rounded approach promotes information gathering from all potentially useful and reliable sources, as well as information fluency.

    Students must be taught to move beyond the first AI response or web result, so they build the essential skills in:

    • Deep reading
    • Source evaluation
    • Contextual comparison
    • Critical synthesis

    Teachers should avoid giving assignments that limit students to a single source type, especially AI. Instead, they should prompt students to explain why they selected a particular source, how they verified its claims, and what alternative viewpoints they encountered.

    Ethical AI use and academic integrity

    Generative AI tools introduce powerful possibilities including significant reductions, as well as a new frontier of plagiarism and uncritical thinking. If a student submits a summary produced by ChatGPT without review or citation, have they truly learned anything? Do they even understand the content?

    To combat this, schools must:

    • Update academic integrity policies to address the use of generative AI including clear direction to students as to when and when not to use such tools.
    • Teach citation standards for AI-generated content
    • Encourage original analysis and synthesis, not just copying and pasting answers

    A responsible prompt might be: “Use a generative AI tool to locate sources, but summarize their arguments in your own words, and cite them directly.”

    In closing: The librarian’s role is more critical than ever

    Today’s information landscape is more complex and powerful than ever, but more prone to automation errors, biases, and superficiality. Students need more than access; they need guidance. That is where the school librarian, media specialist, and digitally literate teacher must collaborate to ensure students are fully prepared for our data-rich world.

    While the tools have evolved, from card catalogs to Google searches to AI copilots, the fundamental need remains to teach students to ask good questions, evaluate what they find, and think deeply about what they believe. Some things haven’t changed–just like in 1997, the best advice to conclude a lesson on research remains, “And if you need help, ask a librarian.”

    Steven M. Baule, Ed.D., Ph.D.
    Latest posts by Steven M. Baule, Ed.D., Ph.D. (see all)

    Source link

  • 5 AI tools for classroom creativity

    5 AI tools for classroom creativity

    Key points:

    • AI tools enhance K-12 creativity and innovation through interactive projects
    • A new era for teachers as AI disrupts instruction
    • Report details uneven AI use among teachers, principals
    • For more news on AI and creativity, visit eSN’s Digital Learning hub

    As AI becomes more commonplace in classrooms, it gives students access to creative tools that enhance learning, exploration, and innovation. K-12 students can use AI tools in various ways to boost creativity through art, storytelling, music, coding, and more.

    More News from eSchool News

    HVAC projects to improve indoor air quality. Tutoring programs for struggling students. Tuition support for young people who want to become teachers in their home communities.

    Data shows growing GenAI adoption in K-12

    Almost 3 in 5 K-12 educators (55 percent) have positive perceptions about GenAI, despite concerns and perceived risks in its adoption, according to updated data from Cengage Group’s “AI in Education” research series.

    Unlocking potential: The power of online courses in our school

    Our school has built up its course offerings without having to add headcount. Along the way, we’ve also gained a reputation for having a wide selection of general and advanced courses for our growing student body.

    Strategies to help girls stay engaged in STEM learning

    Ensuring that girls feel supported and empowered in STEM from an early age can lead to more balanced workplaces, economic growth, and groundbreaking discoveries.

    5 vocabulary strategies powered by technology

    In my work with middle school students, I’ve seen how critical that period of development is to students’ future success. One area of focus in a middle schooler’s development is vocabulary acquisition.

    The power of goal setting: A skill for lifelong success

    For students, the mid-year stretch is a chance to assess their learning, refine their decision-making skills, and build momentum for the opportunities ahead.

    The importance of teacher-student relationships

    Middle school marks the transition from late childhood to early adolescence. Developmental psychologist Erik Erikson describes the transition as a shift from the Industry vs. Inferiority stage into the Identity vs. Role Confusion stage.

    Virtual art meets language learning: A tech-enhanced ESL experience

    Art has a unique power in the ESL classroom–a magic that bridges cultures, ignites imagination, and breathes life into language. For English Language Learners (ELLs), it’s more than an expressive outlet.

    Leadership is key to protecting student data privacy in 2025

    In the year 2025, no one should have to be convinced that protecting data privacy matters. For education institutions, it’s really that simple of a priority–and that complicated.

    Ethical PD: Doing right by the teachers who do right by the world

    Teachers are superheroes. Every day, they rise to the challenge, pouring their hearts into shaping the future. They stay late to grade papers and show up early to tutor struggling students.

    Want to share a great resource? Let us know at submissions@eschoolmedia.com.

    Source link

  • Capability for change – preparing for digital learning futures

    Capability for change – preparing for digital learning futures

    Digital transformation is an ongoing journey for higher education institutions, but there is something quite distinctive about the current moment.

    The combination of financial uncertainty, changing patterns of student engagement, and the seismic arrival of artificial intelligence is pointing to a future for higher education learning and teaching and a digital student experience that will certainly have some core elements in common with current practice but is likely in many respects to look rather different.

    At the moment I see myself and my colleagues trying to cling to what we always did and what we always know. And I really do think the whole future of what we do and how we teach our students, and what we teach our students is going to accelerate and change very, very quickly now, in the next five years. Institutional leader

    Our conversations with sector leaders and experts over the past six months indicate an ambition to build consistent, inclusive and engaging digital learning environments and to deploy data much more strategically. Getting it right opens up all kinds of possibilities to extend the reach of higher education and to innovate in models for engagement. But future change demands different kinds of technological capabilities, and working practices, and institutions are saying that they are hindered by legacy systems, organisational silos, and a lack of a unified vision.

    Outdated systems do not “talk to each other,” and on a cultural level as departments and central teams also do not “talk to each other” – or may struggle to find a common language. And rather than making life easier, many feel that technology creates significant inefficiencies, forcing staff to spend more time on administrative tasks and less on what truly matters.

    I think the problem always is when we hope something’s going to make it more efficient. But then it just adds a layer of complexity into what we’re doing…I think that’s what we struggle with – what can genuinely deliver some time savings and efficiencies as opposed to putting another layer in a process? Institutional leader

    In the spirit of appreciative inquiry, our report Capability for change – preparing for digital learning futures draws on a series of in depth discussions with leaders of learning and teaching, and digital technology, digital experts and students’ union representatives. We explore the sorts of change that are already in train, and surface insight about how institutions are thinking in terms of building whole-organisation capabilities. “Digital dexterity” – the ability to deploy technology strategically, efficiently, and innovatively to achieve core objectives – may be yet another tech buzzword, but it captures a sense of where organisations are trying to get to.

    While immediate financial pressures may require cutting costs and reprofiling investment, long term sustainability depends on moving forward with change, finding ways, not to do more with less but to do things differently. To realise the most value from technology investment institutional leaders need to find ways to ensure that across the institution staff teams have the knowledge, the motivation and the tools to deploy technology in the service of student success.

    How institutions are building organisational capability

    Running through all our conversations was a tension, albeit a potentially productive one: there needs to be much more consistency and clarity about the primary strategic objectives of the institution and the core technology platforms and applications that enable them. But the effect of, in essence, imposing a more streamlined “central” vision, expectations and processes should be to enable and empower the academic and professional teams to do the things that make for a great student experience. Our research indicates that institutions are focusing on three areas: leadership and strategy; digital capabilities of institutional staff; and breaking down the vertical silos that can hamper effective cross-organisational working.

    A number of reflections point to strategy-level improvements – such as ensuring there is strategic alignment between institutional objectives for student success, and technology and digital strategies; listening to the feedback from students and staff about what they need from technology; setting priorities, and resourcing those priorities from end to end from technology procurement to deployment and evaluation of impact. One institutional leader described what happens when digital strategies get lost in principles and forget to align with the wider success of the organisation:

    The old strategy is fairly similar, I imagine, to many digital strategies that you would have seen – it talks about being user focused, talks about lean delivery, talks about agile methodologies, product and change management and delivering value through showing, not telling. So it was a very top level strategy, but really not built with outcomes at its absolute core, like, what are the things that are genuinely going to change for people, for students? Institutional leader

    Discussions of staff digital capabilities recognised that institutional staff are often hampered by organisational complexity and bureaucracy which too often is mirrored in the digital sphere. One e-learning professional suggested that there is a need for research to really understand why there is a tendency towards proliferation of processes and systems, and confront the impact on staff workloads.

    There may also be limits to what can reasonably be expected from teaching staff in terms of digital learning design:

    You need to establish minimum benchmarks and get everyone to that place, and then some people will be operating well beyond that. You can be clear about basic benchmark expectations around student experience – and then beyond that you need to put in actual support [such as learning design experts] to implement the curriculum framework. E-learning professional

    But the broader insight on staff development was around shifting from provision of training on how to operate systems or tools to a more context-specific exploration of how the available technologies and data can help educators achieve their student success ambitions. Value is more systematically created across the organisation when those academic and professional teams who work directly with students are able to use the technology and data available creatively to enhance their practice and to problem solve.

    Where data has been used before it’s very much sat with senior colleagues in the institution. And you know it’s helped in decision making. But the next step is to try and empower colleagues at the coal face to use data in their day to day interventions with their students… How can they use the data to inform how they support their students? Institutional leader

    Decisive leadership may be successful in setting priorities and streamlining the processes and technologies that underpin them; strong focus on professional development may engage and enable institutional staff. But culture change will come when institutions find ways to systematically build “horizontals” across silos – mechanisms for collaborative and shared activity that bridge different perspectives, languages and disciplinary and professional cultures.

    Some examples we saw included embedding digital professionals in faculties and academic business processes such as recruitment panels, convening of cross-organisation thinking on shared challenges, and appointment of “change agent” roles with a skillset and remit to roam across boundaries.

    Technology providers must be part of the solution – acting as strategic partners rather than suppliers. One way to do that is to support institutions to pilot, test, and develop proof of concept before they decide to invest in large-scale change. Another is to work with institutions to understand how technology is deployed in practice, and the evolving needs of user communities. To be a great partner to the higher education sector means having a deep understanding not only of the technological capabilities that could help the sector but how these might weave into an organisation’s wider mission and values. In this way, technology providers can help to build capability for change.

    This article is published in association with Kortext. You can download the Capability for change report on Kortext’s website. The authors would like to thank all those who shared their insight to inform the report. 

    Source link

  • Report details uneven AI use among teachers, principals

    Report details uneven AI use among teachers, principals

    Key points:

    English/language arts and science teachers were almost twice as likely to say they use AI tools compared to math teachers or elementary teachers of all subjects, according to a February 2025 survey from the RAND Corporation that delves into uneven AI adoption in schools.

    “As AI tools and products for educational purposes become more prevalent, studies should track their use among educators. Researchers could identify the particular needs AI is addressing in schools and–potentially–guide the development of AI products that better meet those needs. In addition, data on educator use of AI could help policymakers and practitioners consider disparities in that use and implications for equitable, high-quality instruction across the United States,” note authors Julia H. Kaufman, Ashley Woo, Joshua Eagan, Sabrina Lee, and Emma B. Kassan.

    One-quarter of ELA, math, and science teachers used AI tools for instructional planning or teaching in the 2023–2024 school year. Nearly 60 percent of surveyed principals also reported using AI tools for their work in 2023-2024.

    Among the one-quarter of teachers nationally who reported using AI tools, 64 percent said that they used them for instructional planning only, whether for their ELA, math, or science instruction; only 11 percent said that they introduced them to students but did not do instructional planning with them; and 25 percent said that they did both.

    Although one-quarter of teachers overall reported using AI tools, the report’s authors observed differences in AI use by subject taught and some school characteristics. For instance, close to 40 percent of ELA or science teachers said they use AI, compared to 20 percent of general elementary education or math teachers. Teachers and principals in higher-poverty schools were less likely to report using AI tools relative to those in lower-poverty schools.

    Eighteen percent of principals reported that their schools or districts provided guidance on the use of AI by staff, teachers, or students. Yet, principals in the highest-poverty schools were about half as likely as principals in the lowest-poverty schools to report that guidance was provided (13 percent and 25 percent, respectively).

    Principals cited a lack of professional development for using AI tools or products (72 percent), concerns about data privacy (70 percent) and uncertainty about how AI can be used for their jobs (70 percent) as factors having a major or minor influence on their AI use.

    The report also offers recommendations for education stakeholders:

    1. All districts and schools should craft intentional strategies to support teachers’ AI use in ways that will most improve the quality of instruction and student learning.

    2. AI developers and decision-makers should consider what useful AI applications have the greatest potential to improve teaching and learning and how to make those applications available in high-poverty contexts.

    3. Researchers should work hand-in-hand with AI developers to study use cases and develop a body of evidence on effective AI applications for school leadership, teaching, and learning.

    Laura Ascione
    Latest posts by Laura Ascione (see all)

    Source link

  • Student experience is becoming more transactional – but that doesn’t make it less meaningful

    Student experience is becoming more transactional – but that doesn’t make it less meaningful

    It seems that few can agree about what the future student experience will look like but there is a growing consensus that for the majority of higher education institutions (bar a few outliers) it will – and probably should – look different from today.

    For your institution, that might look like a question of curriculum – addressing student demand for practical skills, career competencies and civic values to be more robustly embedded in academic courses. It might be about the structure of delivery – with the Lifelong Learning Entitlement funding per credit model due to roll out in the next few years and the associated opportunity to flex how students access programmes of study and accrue credit. It might be a question of modality and responding to demands for flexibility in accessing learning materials remotely using technology.

    When you combine all these changes and trends you potentially arrive at a more fragmented and transient model of higher education, with students passing through campus or logging in remotely to pick up their higher education work alongside their other commitments. Academic community – at least in the traditional sense of the campus being the locus of daily activity for students and academics – already appears at risk, and some worry that there is a version of the future in which it is much-reduced or disappears altogether.

    Flexibility, not fragmentation

    With most higher education institutions facing difficult financial circumstances without any immediate prospect of external relief, the likelihood is that cost-saving measures reduce both the institutional capacity to provide wraparound services and the opportunities for the kind of human-to-human contact that shows up organically when everyone is co-located. Sam Sanders

    One of the challenges for higher education in the decade ahead will be how to sustain motivation and engagement, build connection and belonging, and support students’ wellbeing, while responding to that shifting pattern of how students practically encounter learning.

    The current model still relies on high-quality person to person interaction in classrooms, labs, on placement, in accessing services, and in extra-curricular activities. When you have enough of that kind of rich human interaction it’s possible to some extent to tolerate a degree of (for want of a better word) shonky-ness in students’ functional and administrative interactions with their institution.

    That’s not a reflection of the skills and professionalism of the staff who manage those interactions; it’s testament to the messiness of decades of technology systems procurement that has not kept up with the changing demands of higher education operational management. The amount of institutional resource devoted to maintaining and updating these systems, setting up workarounds when they don’t serve desired institutional processes, and extracting and translating data from them is no longer justifiable in the current environment.

    Lots of institutional leaders accept that change is coming. Many are leading significant transformation and reform programmes that respond to one or more of the changes noted above. But they are often trying – at some expense – to build a change agenda on top of a fragile foundational infrastructure. And this is where a change in mindset and culture will be needed to allow institutions to build the kind of student experiences that we think are likely to become dominant within the next decade.

    Don’t fear the transactional

    Maintaining quality when resources are constrained requires a deep appreciation of the “moments that matter” in student experience – those that will have lasting impact on students’ sense of academic identity and connection, and by association their success – and those that can be, essentially, transactional. Pete Moss

    If, as seems to be the case, the sector is moving towards a world in which students need a greater bulk of their interaction with their institution to be in that “transactional” bucket two things follow:

    One is that the meaningful bits of learning, teaching, academic support and student development have to be REALLY meaningful, enriching encounters for both students and the staff who are educating them – because it’s these moments that will bring the education experience to life and have a transformative effect on students. To some degree how each institution creates that sense of meaningfulness and where it chooses to focus its pedagogical efforts may act as a differentiator to guide student choice.

    The second is that the transactional bits have to REALLY work – at a baseline be low-friction, designed with the user in mind, and make the best possible use of technologies to support a more grab-and-go, self-service, accessible-anywhere model that can be scaled for a diverse student body with complicated lives.

    Transactional should not mean ‘one-size-fits-all’ – in fact careful investment in technology should mean that it is possible to build a more inclusive experience through adapting to students’ needs, whether that’s about deploying translation software, integrating assistive technologies, or natural language search functionality. Lizzie Falkowska

    Optimally, institutions will be seeking to get to the point where it is possible to track a student right from their first interaction with the institution all the way through becoming an alumnus – and be able to accommodate a student being several things at once, or moving “backwards” along that critical path as well as “forwards.” Having the data foundations in place to understand where a student is now, as well as where they have come from, and even where they want to get to, makes it possible to build a genuinely personalised experience.

    In this “transactional” domain, there is much less opportunity for strategic differentiation with competitor institutions – though there is a lot of opportunity for hygiene failure, if students who find their institution difficult to deal with decide to take their credits and port them elsewhere. Institutional staff, too, need to be able to quickly and easily conduct transactional business with the institution, so that their time is devoted as much as possible to the knowledge and student engagement work that is simply more important.

    Critically, the more that institutions adopt common core frameworks and processes in that transactional bucket of activity, the more efficient the whole sector can be, and the more value can be realised in the “meaningful” bucket. That means resisting the urge to tinker and adapt, letting go of the myth of exceptionalism, and embracing an “adopt not adapt” mindset.

    Fixing the foundations

    To get there, institutions need to go back to basics in the engine-room of the student experience – the student record system. The student system of 15-20 years ago was a completely internally focused statutory engine, existing for award board grids and HESA returns. Student records is now seen as a student-centric platform that happens to support other outputs and outcomes, both student-facing interactions, and management information that can drive decision-making about where resource input is generating the best returns.

    The breadth of things in the student experience that need to be supported has expanded rapidly, and will continue to need to be adapted. Right now, institutions need their student record system to be able to cope with feeding data into other platforms to allow (within institutional data ethics frameworks) useful reporting on things like usage and engagement patterns. Increasingly ubiquitous AI functionality in information search, student support, and analytics needs to be underpinned by high quality data or it will not realise any value when rolled out.

    Going further, as institutions start to explore opportunities for strategic collaboration, co-design of qualifications and pathways in response to regional skills demands, or start to diversify their portfolio to capture the benefits of the LLE funding model, moving toward a common data framework and standards will be a key enabler for new opportunities to emerge.

    The extent to which the sector is able to adopt a common set of standards and interoperability expectations for student records is the extent to which it can move forward collectively with establishing a high quality baseline for managing the bit of student experience that might be “transactional” in their function, but that will matter greatly as creating the foundations for the bits that really do create lasting value.

    This article is published in association with KPMG.

    Source link

  • How does the higher education sector sustain digital transformation in tough times?

    How does the higher education sector sustain digital transformation in tough times?

    Higher education institutions are in a real bind right now. Financial pressures are bearing down on expenditure, and even those institutions not at immediate risk are having to tighten their belts.

    Yet institutions also need to continue to evolve and improve – to better educate and support students, enable staff to do their teaching and research, strengthen external ties, and remain attractive to international students. The status quo is not appealing – not just because of competitive and strategic pressures but also because for a lot of institutions the existing systems aren’t really delivering a great experience for students and staff. So, when every penny counts, where should institutions invest to get the best outcomes? Technology is rarely the sole answer but it’s usually part of the answer, so deciding which technologies to deploy and how becomes a critical organisational capability.

    Silos breed cynicism

    Digital transformation is one of those areas that’s historically had a bit of a tricky reputation. I suspect your sense of the reason for this depends a bit on your standpoint but my take (as a moderately competent user of technology but by no means expert) is that technology procurement and deployment is an area that tends to expose some of higher education’s historic vulnerabilities around coordinated leadership and decision-making, effective application of knowledge and expertise, and anticipation of, and adaptability to change.

    So in the past there’s been a sense, not of this exact scenario, but some variation on it: the most senior leaders don’t really have the knowledge or expertise about technology and are constantly getting sold on the latest shiny thing; the director of IT makes decisions without fully coordinating with the needs and workflows of the wider organisation; departments buy in tech for their own needs but don’t coordinate with others. There might even be academic or digital pedagogy expertise in the organisation whose knowledge remains untapped in trying to get the system to make sense. And then the whole thing gets tweaked and updated to try to adapt to the changing needs, introducing layer upon layer of complexity and bureaucracy and general clunkiness, and everyone heaves a massive sigh every time a new system gets rolled out.

    This picture is of course a cynical one but it’s striking in our conversations about digital transformation with the sector how frequently these kinds of scenarios are described. The gap between the promise of technology and the reality of making it work is one that can breed quite a lot of cynicism – which is the absolute worst basis from which to embark on any journey of change. People feel as if they are expected to conform to the approved technology, rather than technology helping them do their jobs more effectively.

    Towards digital maturity

    Back in 2023 Jisc bit the bullet with the publication of its digital transformation toolkit, which explicitly sought to replace what in some cases had been a rather fragmented siloed approach with a “whole institution” framework. When Jisc chief executive Heidi Fraser-Krauss speaks at sector events she frequently argues that technology is the easy bit – it’s the culture change that is hard. Over the past two years Jisc director for digital transformation (HE) Sarah Knight and her team have been working with 24 institutions to test the application of the digital transformation framework and maturity model, with a report capturing the learning of what makes digital transformation work in practice published last month.

    I book in a call with Sarah because I’m curious about how institutions are pursuing their digital transformation plans against the backdrop of financial pressure and reductions in expenditure. When every penny counts, institutions need to wring every bit of value from their investments, and technology costs can be a significant part of an institution’s capital and non-staff recurrent expenditure.

    “Digital transformation to us is to show the breadth of where digital touches a university,” says Sarah. “Traditionally digital tended to sit more with ‘digital people’ like CIOs and IT teams, but our framework has shown how a whole-institution approach is needed. For those just starting out, our framework helped to focus attention on the breadth of things to consider such as digital culture, engaging staff and students, digital fluency, capability, inclusivity, sustainability – and all the principles underpinning digital transformation.”

    Advocating a “whole institution approach” may seem counter-intuitive – making what was already a complicated set of decisions even more so by involving more people. But without creating a pipeline of information flow up, down and across the institution, it’s impossible to see what people need from technology, or understand how the various processes in place in different parts of the university are interacting with the technologies available to see where they could be improved.

    “The digital maturity assessment brought people into the conversation at different levels and roles. Doing that can often show up where there is a mismatch in experience and knowledge between organisational leaders and staff and students who are experiencing the digital landscape,” says Sarah.

    Drawing on knowledgeable voices whose experience is closer to the lived reality of teaching and research is key. “Leaders are saying they don’t need to know everything about digital but they do need to support the staff who are working in that space to have resources, and have a seat at table and a voice.”

    Crucially, working across the institution in this way generates an evidence base that can then be used to drive decision-making about the priorities for investment of resources, both money and time. In the past few years, some institutions have been revising their digital strategies and plans, recognising that with constrained finances, they may need to defer some planned investments, or sequence their projects differently, mindful of the pressures on staff.

    For Sarah, leaders who listen, and who assume they don’t already know what’s going on, are those who are the most likely to develop the evidence base that can best inform their decisions:

    “When you have leaders who recognise the value of taking a more evidence-informed approach, that enables investment to be more strategically targeted, so you’re less likely to see cuts falling in areas where digital is a priority. Institutions that have senior leadership support, data informed decision making, and evidence of impact, are in the best place to steer in a direction that is forward moving and find the core areas that are going to enable us to reach longer term strategic goals.”

    In our conversation I detect a sense of a culture shift behind some of the discussions about how to do digital transformation. Put it like this: nobody is saying that higher education leaders of previous decades didn’t practice empathy, careful listening, and value an evidence base. It’s just that when times are tough, these qualities come to the fore as being among the critical tools for institutional success.

    Spirit of collaboration

    There’s a wider culture shift going on in the sector as well, as financial pressures and the sense that a competitive approach is not serving higher education well turns minds towards where the sector could be more collaborative in its approach. Digital is an area that can sometimes be thought of as a competitive space – but arguably that’s mistaking the tech for the impact you hope it will have. Institutions working on digital transformation are better served by learning from others’ experience, and finding opportunities to pool resources and risk, than by going it alone.

    “Digital can be seen as a competitive space, but collaboration outweighs and has far more benefits than competition,” says Sarah. “We can all learn together as a sector, as long as we can keep sharing that spirit of internal and external collaboration we can continue that momentum and be stronger together.”

    This is especially relevant for those institutions whose leaders may secretly feel they are “behind the curve” on digital transformation and experience a sense of anxiety that their institution needs to scramble to “catch up”. The metaphor of the race is less than helpful in this context, creating anxiety rather than a sense of strategic purpose. Sarah believes that no institution can legitimately consider itself “ahead of the curve” – and that all should have the opportunity to learn from each other:

    “We are all on a journey, so some might be ahead in some aspects but definitely not all,” says Sarah. “No-one is behind the curve but everyone is approaching this in a slightly different way, so don’t feel ‘we have to do this ourselves’; use networks and seek help – that is our role as Jisc to support the sector.”

    Jisc is hosting Digifest in Birmingham on 11-12 March – sign up here for online access to sessions.

    Source link

  • How is artificial intelligence actually being used in higher education?

    How is artificial intelligence actually being used in higher education?

    With a wide range of applications, including streamlining administrative tasks and tailoring learning experiences, AI is being used in innovative ways to enhance higher education.

    Course design and content preparation

    AI tools are changing the way academic staff approach course design and content preparation. By leveraging AI, lecturers can quickly generate comprehensive plans, create engaging sessions, and develop quizzes and assignments.

    For instance, tools like Blackboard Ultra can create detailed course plans and provide suggestions for content organisation and course layout. They can produce course materials in a fraction of the time it would traditionally take and suggest interactive elements that could increase student engagement.

    AI tools excel at aligning resources with learning outcomes and institutional policies. This not only saves time but also allows lecturers to focus more on delivering high-quality instruction and engaging with students.

    Enhancing learning experience

    AI and virtual reality (VR) scenarios and gamified environments are offering students unique, engaging learning experiences that go beyond traditional lectures. Tools like Bodyswaps use VR to simulate realistic scenarios for practicing soft and technical skills safely. These immersive and gamified environments enhance learning by engaging students in risk-free real-world challenges and provide instant feedback, helping them learn and adjust more effectively.

    Self-tailored learning

    AI also plays a role in supporting students to tailor learning materials to meet their individual and diverse needs. Tools like Jamworks can enhance student interaction with lecture content by converting recordings into organised notes and interactive study materials, such as flashcards.

    Similarly, Notebook LLM offers flexibility in how students engage with their courses by enabling them to generate content in their preferred form such as briefing documents, podcasts, or taking a more conversational approach. These tools empower students to take control of their learning processes, making education more aligned with their individual learning habits and preferences.

    Feedback and assessment

    Feedback and assessment is the most frequently referenced area when discussing how reductions in workload could be achieved with AI. Marking tools like Graide, Keath.ai, and Learnwise are changing this process by accelerating the marking phase. These tools leverage AI to deliver consistent and tailored feedback, providing students with clear, constructive insights to enhance their academic work. However, the adoption of AI in marking raises valid ethical concerns about its acceptability such as the lack of human judgement and whether AI can mark consistently and fairly.

    Supporting accessibility

    AI can play a crucial role in enhancing accessibility within educational environments, ensuring that learning materials are inclusive and accessible to all students. By integrating AI-driven tools such as automated captioning, and text-to-speech applications, universities can significantly improve the accessibility of digital resources.

    AI’s capability to tailor learning materials is particularly beneficial for students with diverse educational needs. It can reformat text, translate languages, and simplify complex information to make it more digestible. This ensures that all students, regardless of their learning abilities or language proficiency, have equal opportunities to access and understand educational content.

    Despite the benefits, the use of AI tools like Grammarly raises concerns about academic integrity. These tools have the potential to enhance or even alter students’ original work, which may lead to questions about the authenticity of their submissions. This issue highlights the need for clear guidelines and ethical considerations in the use of AI to support academic work without compromising integrity.

    Another significant issue is equity of access to these tools. Many of the most effective AI-driven accessibility tools are premium services, which may not be affordable for all students, potentially widening the digital divide.

    Student support – chatbots

    AI chatbots are increasingly recognised as valuable tools in the tertiary education sector, streamlining student support and significantly reducing staff workload. These increasingly sophisticated systems are adept at managing a wide array of student queries, from routine administrative questions to more detailed academic support, thereby allowing human resources to focus on tasks requiring more nuanced and personal interactions. They can be customised to meet the specific needs of a university, ensuring that they provide accurate and relevant information to students.

    Chatbots such as LearnWise are designed to enhance student interactions by providing more tailored and contextually aware responses. For instance, on a university’s website, if a student expresses interest in gaming, they can suggest relevant courses, highlight the available facilities and include extra curriculum activities available, integrating seamlessly with the student’s interests and academic goals. This level of tailoring enhances the interaction quality and improves the student experience.

    Administrative efficiency

    AI is positively impacting the way administrative tasks are handled within educational institutions, changing the way everyday processes are managed. By automating routine and time-consuming tasks, AI technologies can alleviate the administrative load on staff, allowing them to dedicate more time to strategic and student-focused activities.

    AI tools such as Coplot and Gemini can help staff draft, organise, and prioritise emails. These tools can suggest responses based on the content received, check the tone of emails and manage scheduling by integrating with calendar apps, and remind lecturers of pending tasks or follow-ups, enhancing efficiency within the institution.

    Staff frequently deal with extensive documentation, from student reports to research papers and institutional policies. AI tools can assist in checking, proofreading and summarising papers and reports, and can help with data analysis, generating insights, graphs and graphics to help make data more easily digestible.

    How is AI being used in your institution?

    At Jisc we are collating practical case studies to create a comprehensive overview of how AI is being used across tertiary education. This includes a wide range of examples supporting the effective integration of AI into teaching and administration which will be used to highlight best practice, support those just getting started with the use of AI, overcome challenges being faced across the sector and to highlight the opportunities available to all.

    We want to hear how AI is being used at your organisation, from enhancing everyday tasks to complex and creative use cases. You can explore these resources and find out how to contribute by visiting the Jisc AI Resource Hub.

    For more information around the use of digital and AI in tertiary education, sign up to receive on-demand access to key sessions from Jisc’s flagship teaching and learning event – Digifest running 11–12 March.

    Source link

  • 6 recommendations for AI in classrooms

    6 recommendations for AI in classrooms

    Key points:

    As states move forward with efforts to adopt artificial intelligence, the nonprofit Southern Regional Education Board’s Commission on AI in Education has released its first six recommendations for schools and postsecondary institutions.

    Because of its broad membership, regional breadth, early creation and size, SREB President Stephen L. Pruitt said the commission is poised to produce critical recommendations that will inform not only Southern education decision makers but those throughout the nation.

    “AI is fundamentally changing the classroom and workplace,” Pruitt said. “With that in mind, this commission is working to ensure they make recommendations that are strategic, practical and thoughtful.”

    The commission is set to meet for another year and plans to release a second set of recommendations soon. Here are the first six:

    Policy recommendation #1: Establish state AI networks
    States should establish statewide artificial intelligence networks so people, groups and agencies can connect, communicate, collaborate and coordinate AI efforts across each state. These statewide networks could eventually form a regional group of statewide AI network representatives who could gather regularly to share challenges and successes.

    Policy recommendation #2: Develop targeted AI guidance
    States should develop and maintain targeted guidance for distinct groups using, integrating or supporting the use of AI in education. States should include, for example, elementary students, middle school students, high school students, postsecondary students, teachers, administrators, postsecondary faculty and administrators and parents.

    Policy recommendation #3: Provide high-quality professional development
    State K-12 and postsecondary agencies should provide leadership by working with local districts and institutions to develop plans to provide and incentivize high-quality professional development for AI. The plans should aim to enhance student learning.

    Policy recommendation #4: Integrate into standards & curricula
    States should integrate into statewide K-12 standards and curricula the AI knowledge and skills students need to prepare them for success in the workforce.

    Policy recommendation #5: Assess local capacity and needs
    States should develop and conduct AI needs assessments across their states to determine the capacity of local districts, schools and postsecondary institutions to integrate AI successfully. These should be designed to help states determine which institution, district or school needs state support, what type of support and at what level. 

    Policy recommendation #6: Develop resource allocation plans
    States should develop detailed resource allocation plans for AI implementation in schools, school districts and institutions of postsecondary education to ensure that the implementation of AI is successful and sustainable.
    These plans should inform state fiscal notes related to education and AI.

    The 60-plus member commission was established in February of 2024. Members include policymakers and education and business leaders throughout the 16-state SREB region.

    For more information about the commission please see the following links:

    Latest posts by eSchool Media Contributors (see all)

    Source link

  • AI in K-12 instruction: Insights from instructional coaches

    AI in K-12 instruction: Insights from instructional coaches

    Key points:

    As artificial intelligence (AI) becomes an integral part of modern education, instructional coaches play a pivotal role in guiding teachers on its implementation, bridging the gap between emerging educational technologies and effective classroom practices.

    As trusted mentors and professional development leaders, they guide teachers in implementing AI tools thoughtfully, ensuring that technology enhances student learning while aligning with pedagogical best practices. This article briefly synthesizes responses from instructional coaches regarding their experiences, challenges, and recommendations for integrating AI into K-12 education.  

    Ten instructional coaches, all with advanced degrees, had the following insights into the instructional use of AI in K12 education. They all have more than 10 years of experience in education and work across all three types of school environments: urban, suburban, and rural.

    The coaches reported that AI is used for various instructional purposes. The most-cited applications included providing feedback on student work, creating professional development materials, supporting writing and content generation, creating course content, and enhancing accessibility for students with special needs. Many coaches note that AI tools assisted in grading assignments, offering real-time feedback, and supporting differentiated instruction. AI-powered feedback helps teachers provide more personalized responses without increasing their workload.  Regarding professional development, AI is being used to generate training content for teachers, ensuring they stay updated on educational trends. Coaches are leveraging AI to curate research, synthesize best practices, and develop instructional strategies tailored to their schools.  They encourage teachers and students to utilize AI for brainstorming, outlining essays, and improving writing mechanics.  

    Perceived impact of AI on instruction 

    The vast majority of instructional coaches expressed positive expectations regarding AI’s potential to reduce educator workload, create personalized learning experiences, and improve access for students with disabilities. However, perspectives on AI’s overall impact on education varied. While most believe AI has positively influenced instruction, a few remain cautious about its potential risks.  One coach suggested that allowing students to utilize the tools in a structured setting and teaching them to use AI as a tool is one of the biggest potentials for generative AI in education. About three-fourths of coaches feel that AI will reduce teacher workload by automating repetitive tasks such as grading and data analysis.

    Concerns about AI in education 

    While AI presents numerous benefits, instructional coaches also raised concerns about its potential drawbacks, including ethical dilemmas, student engagement challenges, and equity issues. Despite its advantages, instructional coaches identified several challenges and ethical concerns. They worry some students will use AI tools without critically engaging with the material, leading to passive learning and an overreliance on generative tools. Some had concerns that AI-generated content could reduce the need for creativity and independent thought. Coaches worry that AI makes it easier for students to plagiarize or rely on generated answers without truly understanding concepts which can negatively impact academic integrity. Coaches cite technical challenges as well. Educators face issues with AI tool reliability, compatibility with existing learning management systems (LMS), and steep learning curves. The coaches mentioned that some schools lack the infrastructure to support meaningful widespread AI integration. 

    Several ethical and privacy concerns were mentioned. AI tools collect and store student data, raising concerns about data privacy and security–particularly with younger students who may be less aware or concerned about revealing personally identifiable information (PII). They mention the need for clear guidelines on responsible AI use to prevent bias and misinformation.

    Coaches emphasize the importance of verifying AI-generated materials for accuracy. They suggest teachers be encouraged to cross-check AI-produced responses before using them in instruction. They recommend robust integrating discussions on digital literacy, AI biases, and the ethical implications of generative AI into classroom conversations. Schools need to train educators and students on responsible AI usage. Some schools restrict AI for creative writing, critical thinking exercises, and certain assessments to ensure students develop their own ideas–an idea that coaches recommend. Coaches suggest embedding AI literacy into existing courses, ensuring students understand how AI works, its limitations, and its ethical implications. 

    Equity concerns are a serious issue for instructional coaches. Schools should ensure all students have equal access to AI tools. AI should be leveraged to bridge learning gaps, not widen them. Making sure all students have access to the same suite of tools is essential to create a level playing field for all learners. Instructional coaches generally agree that AI is not just a passing trend, but an integral part of the future of education. There is a concern that generative AI tools will reduce the human interaction of the teaching and learning process. For instance, interpersonal relationships are not developed with AI-based tutoring systems in the same way they can be developed and encouraged with traditional tutoring processes.

    The integration of AI in K-12 education presents both opportunities and challenges. Instructional coaches largely recognize AI’s potential to enhance learning, improve efficiency, academic integrity, and maintain human-centered learning experiences. As AI continues to evolve, educators must be proactive in shaping how it is used, ensuring it serves as a tool for empowerment rather than dependency. Future efforts should focus on professional development for educators, AI literacy training for students, and policies ensuring equitable AI access across diverse school settings.

    Latest posts by eSchool Media Contributors (see all)

    Source link

  • Carousel Digital Signage Integrates with CrisisGo to Empower Safer School Communities

    Carousel Digital Signage Integrates with CrisisGo to Empower Safer School Communities

    MINNEAPOLIS, MINNESOTA – Carousel Digital Signage announces a new technology partnership with CrisisGo that enables K-12 schools and businesses to deliver emergency alerts and other safety messages to digital displays with immediacy. The integration is enabled through an open API that triggers visual alerts, interactive maps and more to Carousel Cloud digital signage networks via the Common Alerting Protocol (CAP), a global standard that supports the digital exchange of emergency alerts and public warnings over multiple networks.

    CrisisGo’s Safety iResponse platform offers a comprehensive suite of advanced alerting software and tools that empower school districts to create safer and more secure learning environments. Its features include real-time alerting, incident management and parent notification, all of which combine to help schools respond to emergencies in an efficient and effective manner. The platform also immediately shares emergency alerts with local law enforcement when police assistance is needed.

    Direct integration of the two software platforms on a common IT network ensures consistent monitoring of incoming triggers from Safety iResponse to Carousel Cloud. Upon recognizing an incoming alert, Carousel Cloud disseminates the active alert as a priority for instant takeover of all  targeted screens. Upon resolution, Carousel Cloud immediately removes the alert and resumes normal operations, eliminating the need to schedule expiration times or manually clear the system. That accelerates the important process of reunification to ensure all students, teachers and other staff members are accounted for and safe.

    “Carousel Cloud’s ability to recognize an all-clear message is a differentiator from other digital signage solutions that we have evaluated,” said Jacob Lewis, Chief Security Officer, CrisisGo. “Carousel Cloud will also recognize the type of event our system is addressing and exactly where the alerts need to go, which could be select screens, schoolwide, or across an entire multi-campus network. This seamless interoperability represents an important step in our multimodal strategy for mass notification, which also includes delivery to all computers and mobile devices that are connected to our software.”

    The CrisisGo partnership represents the latest technology integration between Carousel Digital Signage and emergency alerting platforms aimed at strengthening school safety in K-12 environments. Lewis says that while K-12 remains the top priority for CrisisGo’s integrated solution with Carousel, he anticipates potential expansion into other verticals including corporate enterprise and manufacturing.

    “Our collaboration with CrisisGo represents the next step in our efforts to keep students and faculty informed, safe and resilient across all grade levels,” said Eric Henry, SVP of Business Architecture, Carousel Digital Signage. “Carousel Cloud’s open platform enables clean and reliable interoperability with CrisisGo, and our common integration with the CAP protocol ensures immediate dissemination of important visual alerts that will help school districts keep all campuses safe and secure.”

    About Carousel Digital Signage

    Carousel is Digital Signage Content Management Software that is easy to use, scalable, and reliable. With a deep feature set and strong technology partnerships Carousel gives you the most value in digital signage. Carousel Digital Signage is a division of Tightrope Media Systems. You can reach the Carousel team at (866) 866-4118, or visit  www.carouselsignage.com.

    About CrisisGo

    CrisisGo has been leading the K-12 industry since 2013, setting the standard for school safety. Our comprehensive emergency and safety management platform empowers schools with real-time alerting, incident management, visitor management, threat and behavioral intervention features, and reunification solutions. CrisisGo also offers comprehensive training to equip staff and teachers with handling emergencies. CrisisGo consistently innovates to enhance K-12 security, partnering with educators and administrators to create safe and nurturing learning environments and redefining school safety for a brighter future in education.

    eSchool News Staff
    Latest posts by eSchool News Staff (see all)

    Source link