Tag: Learning

  • Harvard’s President Undercuts Academic Freedom and Learning

    Harvard’s President Undercuts Academic Freedom and Learning

    In a recent podcast, Harvard president Alan Garber said some things about teaching that I found at best odd, and at worse pretty much nonsense, because if we’re talking about teaching and learning—supposedly the core of the undergraduate experience at Harvard and elsewhere—it doesn’t make any sense.

    As reported by the Harvard Crimson student newspaper, reflecting on the present challenges to institutions around accusations of intolerance and hostility to free debate, Garber came down firmly on the side of not debating (bold is mine): “I’m pleased to say that I think there is real movement to restore balance in teaching and to bring back the idea that you need to be objective in the classroom.”

    It came as news to me that it is a goal to be “objective” in the classroom, because objectivity is not a value that I associate with writing instruction, my primary field of expertise. As I’ve written here previously, my first-year writing students often struggled with this notion, believing that it was their job to not only be objective but in also to be “authoritative,” which had them adopting strange approaches to expression as they tried to BS themselves and the audience in a weird performance of fake erudition.

    Instead, I introduced students to the values that I believe properly attach to personal expression through writing—which is what all scholarship is, after all—values like transparency, openness, fairness, accuracy and curiosity (among others).

    They need to practice these things in order to build trust with their audience in the effort to be convincing, not as some kind of objective authority, but as someone who has proven themselves trustworthy through the deployment of sound writing practices and respect for the audience.

    As I told students, this is no guarantee of people agreeing with you or adopting your position, but in my view, the job of the writer is to be as clear as possible with their own positioning in order to foster an ongoing, in fact never-ending, academic conversation in which people with different perspectives come together to communicate across topics in ways that fundamentally illuminate those topics for the benefit of an interested and engaged audience.

    I don’t think any of this is controversial and has, in fact, been the underlying engine of academic inquiry for, I don’t know … ever? That faculty having opinions rooted in their expertise and then expressing those opinions somehow became controversial is not a problem with the academic conversation.

    I admit that this framing of discourse is a little quaint in an era where attention is the primary (perhaps only) coin of the realm and attempting to be accurate, transparent and fair seems to matter very little, but one of the great things about the essentially conservative nature of higher education institutions is that we get to cling to out-of-fashion notions because we believe they are consistent with our underlying values.

    I wonder where Garber got this notion that objectivity in the classroom is something that used to be the norm. I don’t remember my Econ 101 professor in fall 1988 regaling the class with a balanced discussion of socialist and Marxist (or even New Deal) economic theory. Instead, I was subjected to what would become bog-standard neoliberal notions about markets, competition and deregulation—notions that are highly contested within the field of economics.

    Which is as it should be! This is the work of academia.

    It’s possible that Garber is paying a little bit of lip service to audiences he knows have been critical of what they perceive as the ideological biases in higher ed, but it is enervating to see a college president validate critiques that have been overwhelmingly applied in bad faith to undermine institutions. If you don’t believe me, perhaps you should consider the testimony of former Republican governor of Indiana Eric Holcomb, who spent a semester teaching at an elite university, expecting to find an ideological monoculture, but experienced the opposite—a place of open debate, differing viewpoints and productive intellectual exchange.

    Holcomb was “surprised,” but he shouldn’t have been, because those of us who work within higher education know that the critique Garber is validating is overwhelmingly untrue.

    Oh, that elite institution where Holcomb found not objective presentation of information but open debate? Harvard.

    What is a bigger threat to free expression on campuses, faculty expressing opinions in classrooms, or institutional leaders publicly declaring it’s important for faculty to keep things “objective”?

    One of Garber’s rationales for championing objectivity was that this approach would be in the interest of students, saying, “How many students would actually be willing to go toe-to-toe against a professor who’s expressed a firm view about a controversial issue?”

    Harvard students, or at least one Harvard student, Adam Chiocco, also writing at The Harvard Crimson, reject this rationale, pointing out that one of the things that draws students to Harvard is the faculty, who have deep expertise and “the most refined and developed perspectives in academia.” Garber is essentially asking faculty to shelve that expertise in the service of what, exactly?

    Chiocco isn’t having it. As he says, “When a professor offers their perspective, students can see how an expert in a field thinks through an issue, how their arguments are structured, and often gain new ways to analyze sources. Good professors will then invite disagreement with their views, challenging students to contemplate and present thoughtful questions and objections.”

    This is happening in thousands of classrooms across the country every single hour of the day. While there are outlier exceptions who may abuse the privilege of their position, we know, and Garber knows, as former governor Holcomb knows, that they are by far the exception.

    Chiocco again: “For all involved, binding expertise to the ideal of neutrality constricts the possibilities for meaningful learning.”

    I don’t think the freedom of students to learn and faculty to teach is helped by a university president giving credence to a fiction or offering a vision that is inconsistent with what we know to be good educational practices.

    There are obviously bigger threats to academic freedom right now, like Texas A&M censoring Plato and canceling graduate courses on ethics because a professor can’t promise to guide discussion according to the dictates of a politically partisan legislature.

    But part of fighting those larger forces is making the affirmative case for the work faculty and students do. President Garber failed that part of his duty with his podcast remarks.

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  • Using generative tools to deepen, not replace, human connection in schools

    Using generative tools to deepen, not replace, human connection in schools

    Key points:

    For the last two years, conversations about AI in education have tended to fall into two camps: excitement about efficiency or fear of replacement. Teachers worry they’ll lose authenticity. Leaders worry about academic integrity. And across the country, schools are trying to make sense of a technology that feels both promising and overwhelming.

    But there’s a quieter, more human-centered opportunity emerging–one that rarely makes the headlines: AI can actually strengthen empathy and improve the quality of our interactions with students and staff.

    Not by automating relationships, but by helping us become more reflective, intentional, and attuned to the people we serve.

    As a middle school assistant principal and a higher education instructor, I’ve found that AI is most valuable not as a productivity tool, but as a perspective-taking tool. When used thoughtfully, it supports the emotional labor of teaching and leadership–the part of our work that cannot be automated.

    From efficiency to empathy

    Schools do not thrive because we write faster emails or generate quicker lesson plans. They thrive because students feel known. Teachers feel supported. Families feel included.

    AI can assist with the operational tasks, but the real potential lies in the way it can help us:

    • Reflect on tone before hitting “send” on a difficult email
    • Understand how a message may land for someone under stress
    • Role-play sensitive conversations with students or staff
    • Anticipate barriers that multilingual families might face
    • Rehearse a restorative response rather than reacting in the moment

    These are human actions–ones that require situational awareness and empathy. AI can’t perform them for us, but it can help us practice and prepare for them.

    A middle school use case: Preparing for the hard conversations

    Middle school is an emotional ecosystem. Students are forming identity, navigating social pressures, and learning how to advocate for themselves. Staff are juggling instructional demands while building trust with young adolescents whose needs shift by the week.

    Some days, the work feels like equal parts counselor, coach, and crisis navigator.

    One of the ways I’ve leveraged AI is by simulating difficult conversations before they happen. For example:

    • A student is anxious about returning to class after an incident
    • A teacher feels unsupported and frustrated
    • A family is confused about a schedule change or intervention plan

    By giving the AI a brief description and asking it to take on the perspective of the other person, I can rehearse responses that center calm, clarity, and compassion.

    This has made me more intentional in real interactions–I’m less reactive, more prepared, and more attuned to the emotions beneath the surface.

    Empathy improves when we get to “practice” it.

    Supporting newcomers and multilingual learners

    Schools like mine welcome dozens of newcomers each year, many with interrupted formal education. They bring extraordinary resilience–and significant emotional and linguistic needs.

    AI tools can support staff in ways that deepen connection, not diminish it:

    • Drafting bilingual communication with a softer, more culturally responsive tone
    • Helping teachers anticipate trauma triggers based on student histories
    • Rewriting classroom expectations in family-friendly language
    • Generating gentle scripts for welcoming a student experiencing culture shock

    The technology is not a substitute for bilingual staff or cultural competence. But it can serve as a bridge–helping educators reach families and students with more warmth, clarity, and accuracy.

    When language becomes more accessible, relationships strengthen.

    AI as a mirror for leadership

    One unexpected benefit of AI is that it acts as a mirror. When I ask it to review the clarity of a communication, or identify potential ambiguities, it often highlights blind spots:

    • “This sentence may sound punitive.”
    • “This may be interpreted as dismissing the student’s perspective.”
    • “Consider acknowledging the parent’s concern earlier in the message.”

    These are the kinds of insights reflective leaders try to surface–but in the rush of a school day, they are easy to miss.

    AI doesn’t remove responsibility; it enhances accountability. It helps us lead with more emotional intelligence, not less.

    What this looks like in teacher practice

    For teachers, AI can support empathy in similarly grounded ways:

    1. Building more inclusive lessons

    Teachers can ask AI to scan a lesson for hidden barriers–assumptions about background knowledge, vocabulary loads, or unclear steps that could frustrate students.

    2. Rewriting directions for struggling learners

    A slight shift in wording can make all the difference for a student with anxiety or processing challenges.

    3. Anticipating misconceptions before they happen

    AI can run through multiple “student responses” so teachers can see where confusion might arise.

    4. Practicing restorative language

    Teachers can try out scripts for responding to behavioral issues in ways that preserve dignity and connection.

    These aren’t shortcuts. They’re tools that elevate the craft.

    Human connection is the point

    The heart of education is human. AI doesn’t change that–in fact, it makes it more obvious.

    When we reduce the cognitive load of planning, we free up space for attunement.
    When we rehearse hard conversations, we show up with more steadiness.
    When we write in more inclusive language, more families feel seen.
    When we reflect on our tone, we build trust.

    The goal isn’t to create AI-enhanced classrooms. It’s to create relationship-centered classrooms where AI quietly supports the skills that matter most: empathy, clarity, and connection.

    Schools don’t need more automation.

    They need more humanity–and AI, used wisely, can help us get there.

    Latest posts by eSchool Media Contributors (see all)

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  • “Say My Name, Say My Name”: Why Learning Names Improves Student Success – Faculty Focus

    “Say My Name, Say My Name”: Why Learning Names Improves Student Success – Faculty Focus

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  • “Say My Name, Say My Name”: Why Learning Names Improves Student Success – Faculty Focus

    “Say My Name, Say My Name”: Why Learning Names Improves Student Success – Faculty Focus

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  • Transfer and Learning Mobility in 2026 and Beyond

    Transfer and Learning Mobility in 2026 and Beyond

    Nearly four in 10 adult Americans have tried to transfer credit toward a college degree or credential. Of those, 58 percent lost credits in the process. For some, the consequences were severe: using up financial aid and repeating classes they’d already passed. Sixteen percent reported giving up on higher education altogether because the transfer process was simply too difficult.

    These aren’t just statistics. They represent learners and workers who lost time, money and faith in a system that promised them opportunity.

    Many have been trying to address these issues, and great work is underway. But the effort to transform transfer and learning mobility still lacks a coordinated and sustained focus at scale. Transfer and learning mobility are still treated as niche issues affecting a small percentage of students, rather than an increasingly common reality for today’s learners that should compel higher education to evolve. We have not yet achieved the fundamental mindset shifts, or built the supportive infrastructure, that are needed to treat all learning fairly, but the pressure is on. And with pressure comes opportunity.

    Year 5 of Connecting With You on ‘Beyond Transfer’

    Welcome to year five of the “Beyond Transfer” column on Inside Higher Ed—a column that seeks to elevate the voices of expert practitioners, researchers, advocates, policymakers, students and others who are seeking to overhaul not just the transfer experience, but the entire ecosystem related to ensuring that all Americans benefit from their hard-earned and hard-learned skills and competencies and receive the economic mobility they deserve.

    Each year, we kick off the column with some reflections on what we’ve learned through listening to and collaborating with all of you. At Sova, we’ve had the privilege of working at multiple levels of the transfer and learning mobility ecosystem (hereafter “transfer”): facilitating national expert groups such as the Beyond Transfer Policy Advisory Board and the Learning Evaluation and Recognition for the Next Generation (LEARN) Commission (co-convened with the American Association of Collegiate Registrars and Admissions Officers); advancing state-level work from California’s AB 928 Associate Degree for Transfer Intersegmental Implementation Committee to the Texas Transfer Alliance led by Educate Texas; supporting institutional collaborations such as the Acceleration to Credits Working Group and the CCC-CSU Transfer Collaborative; leveraging AI to transform the learning mobility experience through the AI Transfer and Articulation Infrastructure Network (ATAIN); and elevating student voice through our social media platforms.

    As we look ahead, we are connecting the dots on some insights that, while not new, have been at the forefront of our minds over the last year.

    • Credit loss is prevalent, damaging and unfair. Matt Giani, Lauren Schudde and Tasneem Sultana present a rigorous analysis of credit loss in Texas and describe its damaging consequences. In their study of almost 29,000 community college–to–public university first-time transfers, 83 percent of transfers experienced some credit loss. Perhaps most alarming is that this credit loss was among those who followed the rules and transferred to a discipline-aligned program of study (i.e., maintained the same major after transfer).
    • Transfer of credit is a shared American experience. In these politically divisive times, it’s rare to find a topic where common ground is still possible, but transfer is an issue that resonates across party lines. As referenced earlier, a recent survey of adult Americans conducted by Public Agenda for Sova and the Beyond Transfer Policy Advisory Board illuminates both how prevalent transfer is and how Americans’ experiences with transfer shape their attitudes toward colleges and universities. Not only have four in 10 Americans sought to transfer credit, but it’s also the case that a large majority of Americans across the ideological spectrum agree that colleges and universities should be held accountable for honoring learning and accepting credits.
    • The lack of change in transfer and learning mobility is harming higher ed’s reputation. The survey found that those who tried to transfer credit were more likely to feel that higher education institutions care more about making money than about educating students. At a time of declining public trust in higher ed, this is a dangerous signal. In recent focus groups on public attitudes toward college affordability and value conducted by Sova with support from Lumina Foundation, problems with credit transfer have been raised spontaneously by participants in every focus group conducted thus far (12 focus groups across four states).

    Credit transfer is too often built upon unfair contradictions and expectations. Consider, for example:

    • Students are encouraged and even expected to explore their options and pursue a broad education, and yet they are simultaneously forced to choose a preparatory pathway aligned to a receiving institution’s requirements. Because they cannot know where they will be accepted for transfer, they are forced to bet their credits on a single guess.
    • Learners are expected to accept admissions offers before they know how their prior coursework and other learning experiences will be applied to completion.
    • Courses evaluated for transfer are reviewed to ensure they are equivalent to a receiving institution’s courses, without acknowledgment that a single receiving institution may also have multiple faculty (and graduate students) teaching similar courses in a variety of ways and preparation within the receiving institution is uneven as well.
    • Impressive reform efforts in transfer and learning mobility are underway in many settings, with state policy influencers playing important roles. There is much to celebrate, from the leadership of large transfer-sending institutions such as the Alamo Colleges District and Maricopa Community Colleges, to technology initiatives such as ATAIN and Transfer Explorer, to the individual champions who dedicate their personal time in spaces like Transfer Nation to create knowledge and community.

    The Texas Transfer Alliance, with the generous support of Ascendium Education Philanthropy, is leading statewide work focused on building a single, regional Target Pathway that provides all students—regardless of whether they started in high school dual credit or in community college—with clarity through a 60-credit pathway by program that meets requirements for high school graduation, associate degree and eligibility for transfer to multiple bachelor’s-granting institutions in the region. Texas policies related to funding (e.g., HB 8) and data transparency (e.g., SB 25 and SB 3039) are creating the conditions that urge institutions to initiate reforms such as these.

    • Accreditors are beginning to shift and evolve. Much as most Americans are calling for accountability for credit transfer, accreditors are also calling for change. Writing on behalf of the seven federally recognized accrediting commissions overseeing approximately 3,000 institutions, the Council of Regional Accrediting Commissions (C-RAC) stated:

    “Institutions should commit to a default in learning evaluation that credits are applied to program completion unless there is evidence that the required learning outcomes are not met. Decision-making should not be based upon anecdotes, assumptions about quality, locations where earned, or an unexamined history of ‘how things have always been done.’”

    While this may seem like common sense to a layperson, this represents a significant mindset shift. As the arbiters of quality and gatekeepers for federal financial aid, increased accreditor attention to transfer stands to motivate institutional behavior in meaningful ways.

    • And yet, reform efforts in transfer and learning mobility remain slow and episodic. The field has not yet launched a movement equal in scope and depth to the size of the problem we are facing. Higher ed was built to privilege some learners and types of learning over others. Confronting this bias head-on and committing to a new modus operandi is necessary for higher education to evolve and maintain its relevance with today’s learners.

    The Path Forward

    As we dive headlong into 2026, we’re placing our bets on a few fronts.

    The first front is changing assumptions and mindsets. There are a number of ways we are urging the field to shift the lens on transfer and learning mobility. For example, in vertical transfer, the large majority of students cannot know to which institution they will be accepted and able to transfer. That is how the system is designed. It is therefore no longer acceptable for each receiving institution to consider it fair to impose a slew of differing transfer requirements, as it makes it impossible for a student to choose a 60-credit preparatory pathway that works across potential transfer destinations. The Target Pathways work in Texas is designed to ensure students are eligible for transfer to multiple institutions. That needs to become the gold standard.

    Secondly, we need a mindset shift akin to the goal (not yet fully realized) of developmental education redesign. Traditional prerequisite remediation operates on the assumption that students are not “college-ready” unless they prove they are through placement tests. The corequisite approach—backed by solid evidence of greatly improved student outcomes—begins with the assumption, instead, that the large majority of students are ready to start in college-level courses and institutions have a responsibility to support the success of the students they admit through how they design and teach credit-bearing courses.

    In transfer and learning mobility today, the prevailing mindset sounds a lot like that of traditional prerequisite remediation: Students are assumed to not be “transfer-ready” unless they prove it through a process that interrogates their transfer coursework and other prior learning experiences—often including reviews of textbooks, assignments and other minutiae—to prove similarity to “equivalent” courses at the receiving institution. Similar to the goal of dev ed redesign and aligned to how accreditors are shifting their thinking, what would it look like to shift the mindset to: The large majority of learners have been prepared enough by the sum of their learning experiences to be ready for further education and all institutions have a responsibility to support their success after transfer?

    In addition to work on mindsets, we are focused in a few other key areas:

    • Use tech/AI to leapfrog. AI can’t solve all our problems and we know it comes with many new ones, but learning mobility will be transformed as technology finally allows us to move beyond slow, manual, course-to-course reviews that result in limited credit mobility and confusing and conflicting information for learners. Tech offers opportunities to identify equivalencies at a level that human review will never achieve and provide students with exciting navigation support, blowing open the gates that currently restrict credit transfer, as ATAIN seeks to do.
    • Demand transparency for credential applicability. A combination of policy innovation in states (e.g., SB 3039 in Texas) and advances in technology (e.g., the articulation coverage score) lead us to a moment where we can—and must—focus in on transparency about whether learners and workers are getting credit that accelerates them toward their goals.
    • Give learners real clarity and guarantees. Collaborate across partners to build one Target Pathway for a region (by program) and layer on guaranteed program-level admissions programs with targeted financial aid, dedicated advising and belonging initiatives that create a giant vacuum that pulls students through to completion.
    • Shift incentives through policy. So long as institutions continue to operate in a world that primarily incentivizes enrollment, nothing will change. Policymakers must step in and change the incentive structures that drive institutional behavior—both the financial and reputational incentives. What does it mean to recognize and reward institutions when they not only accept transfer students, but commit to the work of ensuring all credit for prior learning is counted toward credentials so that learners and workers are supported to complete in a timely manner? In its recent report, the LEARN Commission points to the opportunity for policymakers to enhance transparency and create new incentives that accelerate institutional change.

    The question isn’t whether the current transfer credit system is broken. The data makes that clear. The question is whether higher education has the courage to take on this challenge in a coordinated, sustained and scaled way. Too many learners are losing credits, losing money and losing hope. It’s time to do better.

    The authors are members of Sova’s Transfer and Learning Mobility team. Learn more about Beyond Transfer at sova.org/beyond-transfer or follow “Beyond Transfer” on Instagram @beyondtransfer and Transfer Nation California on LinkedIn or Instagram @transfernationca.

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  • AI use is on the rise, but is guidance keeping pace?

    AI use is on the rise, but is guidance keeping pace?

    Key points:

    The rapid rise of generative AI has turned classrooms into a real-time experiment in technology use. Students are using AI to complete assignments, while teachers are leveraging it to design lessons, streamline grading, and manage administrative tasks.

    According to new national survey data from RAND, AI use among both students and educators has grown sharply–by more than 15 percentage points in just the past one to two years. Yet, training and policy have not kept pace. Schools and districts are still developing professional development, student guidance, and clear usage policies to manage this shift.

    As a result, educators, students, and parents are navigating both opportunities and concerns. Students worry about being falsely accused of cheating, and many families fear that increased reliance on AI could undermine students’ critical thinking skills.

    Key findings:

    During the 2024-2025 school year, AI saw rapid growth.

    AI use in schools surged during the 2024-2025 academic year. By 2025, more than half of students (54 percent) and core subject teachers (53 percent) were using AI for schoolwork or instruction–up more than 15 points from just a year or two earlier. High school students were the most frequent users, and AI adoption among teachers climbed steadily from elementary to high school.

    While students and parents express significant concern about the potential downsides of AI, school district leaders are far less worried.

    Sixty-one percent of parents, 48 percent of middle school students, and 55 percent of high school students believe that increased use of AI could harm students’ critical-thinking skills, compared with just 22 percent of district leaders. Additionally, half of students said they worry about being falsely accused of using AI to cheat.

    Training and policy development have not kept pace with AI use in schools.

    By spring 2025, only 35 percent of district leaders said their schools provide students with training on how to use AI. Meanwhile, more than 80 percent of students reported that their teachers had not explicitly taught them how to use AI for schoolwork. Policy guidance also remains limited–just 45 percent of principals said their schools or districts have policies on AI use, and only 34 percent of teachers reported policies specifically addressing academic integrity and AI.

    The report offers recommendations around AI use and guidance:

    As AI technology continues to evolve, trusted sources–particularly state education agencies–should provide consistent, regularly updated guidance on effective AI policies and training. This guidance should help educators and students understand how to use AI as a complement to learning, not a replacement for it.

    District and school leaders should clearly define what constitutes responsible AI use versus academic dishonesty and communicate these expectations to both teachers and students. In the near term, educators and students urgently need clarity on what qualifies as cheating with AI.

    Elementary schools should also be included in this effort. Nearly half of elementary teachers are already experimenting with AI, and these early years are when students build foundational skills and habits. Providing age-appropriate, coherent instruction about AI at this stage can reduce misuse and confusion as students progress through school and as AI capabilities expand.

    Ultimately, district leaders should develop comprehensive AI policies and training programs that equip teachers and students to use AI productively and ethically across grade levels.

    Laura Ascione
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  • 25 predictions about AI and edtech

    25 predictions about AI and edtech

    eSchool News is counting down the 10 most-read stories of 2025. Story #2 focuses on predictions educators made for AI in 2025.

    When it comes to education trends, AI certainly has staying power. As generative AI technologies evolve, educators are moving away from fears about AI-enabled cheating and are embracing the idea that AI can open new doors for teaching and learning.

    AI tools can reduce the administrative burden so many educators carry, can personalize learning for students, and can help students become more engaged in their learning when they use the tools to brainstorm and expand on ideas for assignments and projects. Having AI skills is also essential for today’s students, who will enter a workforce where AI know-how is becoming more necessary for success.

    So: What’s next for AI in education? We asked educators, edtech industry leaders, stakeholders, and experts to share some predictions about where they think AI is headed in 2025. (Here’s our list of 50 predictions for edtech in 2025.)

    Here’s what they had to say:

    In 2025, online program leaders will begin to unlock the vast potential of generative AI, integrating it more deeply into the instructional design process in ways that can amplify and expedite the work of faculty and instructional designers. This technology, already making waves in instruction and assessment, stands poised to transform the creation of online courses. By streamlining time-intensive tasks, generative AI offers the promise of automation, replication, and scalability, enabling institutions to expand their online offerings at an unprecedented pace. The key is that we maintain rigorous standards of quality–and create clear guardrails around the ethical use of AI at a time when increasingly sophisticated models are blurring the lines between human design–and artificial intelligence. Generative AI holds extraordinary promise, but its adoption must be grounded in practices that prioritize equitable and inclusive access, transparency, and educational excellence.
    –Deb Adair, CEO, Quality Matters

    In 2025, education in the United States will reflect both the challenges and opportunities of a system in transition. Uncertainty and change at the federal level will continue to shift decision-making power to states, leaving them with greater autonomy but also greater responsibility. While this decentralization may spark localized innovation, it is just as likely to create uneven standards. In some states, we’ve already seen benchmarks lowered to normalize declines, a trend that could spread as states grapple with resource and performance issues. This dynamic will place an even greater burden on schools, teachers, and academic leaders. As those closest to learners, they will bear the responsibility of bridging the gap between systemic challenges and individual student success. To do so effectively, schools will require tools that reduce administrative complexity, enabling educators to focus on fostering personal connections with students–the foundation of meaningful academic growth. AI will play a transformative role in this landscape, offering solutions to these pressures. However, fragmented adoption driven by decentralized decision-making will lead to inequities, with some districts leveraging AI effectively and others struggling to integrate it. In this complex environment, enterprise platforms that offer flexibility, integration, and choice will become essential. 2025 will demand resilience and creativity, but it also offers all of us an opportunity to refocus on what truly matters: supporting educators and the students they inspire.
    —Scott Anderberg, CEO, Moodle

    As chatbots become more sophisticated, they’re rapidly becoming a favorite among students for their interactive and personalized support, and we can expect to see them increasingly integrated into classrooms, tutoring platforms, and educational apps as educators embrace this engaging tool for learning. Additionally, AI is poised to play an even larger role in education, particularly in test preparation and course planning. By leveraging data and predictive analytics, AI-driven tools will help students and educators create more tailored and effective learning pathways, enhancing the overall educational experience.
    —Brad Barton, CTO, YouScience 

    As we move into 2025,  we’ll move past the AI hype cycle and pivot toward solving tangible classroom challenges. Effective AI solutions will integrate seamlessly into the learning environment, enhancing rather than disrupting the teaching experience. The focus will shift to practical tools that help teachers sustain student attention and engagement–the foundation of effective learning. These innovations will prioritize giving educators greater flexibility and control, allowing them to move freely around the classroom while effortlessly managing and switching between digital resources. An approach that ensures technology supports and amplifies the irreplaceable human connections at the heart of learning, rather than replacing them.
    –Levi Belnap, CEO, Merlyn Mind

    The year 2025 is set to transform science education by implementing AI-driven learning platforms. These platforms will dynamically adjust to the student’s interests and learning paces, enhancing accessibility and inclusivity in education. Additionally, virtual labs and simulations will rise, enabling students to experiment with concepts without geographical constraints. This evolution will make high-quality STEM education more universally accessible.
    –Tiago Costa, Cloud & AI Architect, Microsoft; Pearson Video Lesson Instructor 

    In the two years since GenAI was unleashed, K-12 leaders have ridden the wave of experimentation and uncertainty about the role this transformative technology should have in classrooms and districts. 2025 will see a shift toward GenAI strategy development, clear policy and governance creation, instructional integration, and guardrail setting for educators and students. K-12 districts recognize the need to upskill their teachers, not only to take advantage of GenAI to personalize learning, but also so they can teach students how to use this tech responsibly. On the back end, IT leaders will grapple with increased infrastructure demands and ever-increasing cybersecurity threats.
    —Delia DeCourcy, Senior Strategist, Lenovo Worldwide Education Team

    AI-driven tools will transform the role of teachers and support staff in 2025: The advent of AI will allow teachers to offload mundane administrative tasks to students and provide them more energy to be at the “heart and soul” of the classroom. Moreover, more than two-thirds (64 percent) of parents agreed or strongly agreed that AI should help free teachers from administrative tasks and help them build connections with the classroom. Impact of technological advancements on hybrid and remote learning models in 2025: AI is revolutionizing the online learning experience with personalized pathways, tailored skills development and support, and enhanced content creation. For example, some HBS Online courses, like Launching Tech Ventures, feature an AI course assistant bot to help address learners’ questions and facilitate successful course completion. While the long-term impact remains uncertain, AI is narrowing the gap between online and in-person education. By analyzing user behavior and learning preferences, AI can create adaptive learning environments that dynamically adjust to individual needs, making education more engaging and effective. 
    –David Everson, Senior Director of Marketing Solutions, Laserfiche

    In education and digital publishing, artificial intelligence (AI) will continue transitioning from novelty applications to solutions that address real-world challenges facing educators and students. Successful companies will focus on data security and user trust, and will create learner-centered AI tools to deliver personalized experiences that adapt to individual needs and enhance efficiency for educators, enabling them to dedicate more time to fostering meaningful connections with students. The ethical integration of AI technologies such as retrieval-augmented generation (RAG) is key to this evolution. Unlike traditional large language models that ingest information from the Internet at large, RAG delivers AI outputs that are grounded in authoritative, peer-reviewed content, reducing the risk of misinformation while safeguarding the integrity of intellectual property. Thoughtfully developed AI tools such as this will become partners in the learning journey, encouraging analysis, problem-solving, and creativity rather than fostering dependence on automated responses. By taking a deliberate approach that focuses on ethical practices, user-centered design, and supporting the cultivation of essential skills, successful education companies will use AI less as innovation for its own sake and more as a means to provide rich and memorable teaching and learning experiences.
    —Paul Gazzolo, Senior Vice President & Global General Manager, Gale, a Part of Cengage Group

    Adaptive learning technologies will continue to personalize curriculum and assessment, creating a more responsive and engaging educational journey that reflects each student’s strengths and growth areas. Generative AI and other cutting-edge advancements will be instrumental in building solutions that optimize classroom support, particularly in integrating assessment and instruction. We will see more technology that can help educators understand the past to edit materials in the present, to accelerate teachers planning for the future.
    —Andrew Goldman, EVP, HMH Labs

    We’ll witness a fundamental shift in how we approach student assessment, moving away from conventional testing models toward more authentic experiences that are seamless with instruction. The thoughtful integration of AI, particularly voice AI technology, will transform assessment from an intermittent event into a natural part of the learning process. The most promising applications will be those that combine advanced technology with research-validated methodologies. Voice-enabled assessments will open new possibilities for measuring student knowledge in ways that are more natural and accessible, especially for our youngest learners, leveraging AI’s capabilities to streamline assessment while ensuring that technology serves as a tool to augment, rather than replace, the critical role of teachers.
    –Kristen Huff, Head of Measurement, Curriculum Associates

    AI is already being used by many educators, not just to gain efficiencies, but to make a real difference in how their students are learning. I suspect in 2025 we’ll see even more educators experimenting and leveraging AI tools as they evolve–especially as more of the Gen Z population enters the teaching workforce. In 2024, surveyed K-12 educators reported already using AI to create personalized learning experiences, provide real-time performance feedback, and foster critical thinking skills. Not only will AI usage continue to trend up throughout 2025, I do believe it will reach new heights as more teachers begin to explore GenAI as a hyper-personalized asset to support their work in the classroom. This includes the use of AI as an official teacher’s assistant (TA), helping to score free response homework and tests and providing real-time, individualized feedback to students on their education journey.
    –John Jorgenson, CMO, Cambium Learning Group

    The new year will continue to see the topic of AI dominate the conversation as institutions emphasize the need for students to understand AI fundamentals, ethical considerations, and real-world applications outside of the classroom. However, a widening skills gap between students and educators in AI and digital literacy presents a challenge. Many educators have not prioritized keeping up with rapid technological advancements, while students–often exposed to digital tools early on–adapt quickly. This gap can lead to uneven integration of AI in classrooms, where students sometimes outpace their instructors in understanding. To bridge this divide, comprehensive professional development for teachers is essential, focusing on both technical skills and effective teaching strategies for AI-related topics. Underscoring the evolving tech in classrooms will be the need for evidence of outcomes, not just with AI but all tools. In the post-ESSER era, evidence-based decision-making is crucial for K-12 schools striving to sustain effective programs without federal emergency funds. With the need to further justify expenditures, schools must rely on data to evaluate the impact of educational initiatives on student outcomes, from academic achievement to mental health support. Evidence helps educators and administrators identify which programs truly benefit students, enabling them to allocate resources wisely and prioritize what works. By focusing on measurable results, schools can enhance accountability, build stakeholder trust, and ensure that investments directly contribute to meaningful, lasting improvements in learning and well-being.
    —Melissa Loble, Chief Academic Officer, Instructure

    With AI literacy in the spotlight, lifelong learning will become the new normal. Immediate skills need: The role of “individual contributors” will evolve, and we will all be managers of AI agents, making AI skills a must-have. Skills of the future: Quantum skills will start to be in demand in the job market as quantum development continues to push forward over the next year. Always in-demand skills: The overall increase in cyberattacks and emerging risks, such as harvest now and decrypt later (HNDL) attacks, will further underscore the continued importance of cybersecurity skills. Upskilling won’t end with AI. Each new wave of technology will demand new skills, so lifelong learners will thrive. AI will not be siloed to use among technology professionals. The democratization of AI technology and the proliferation of AI agents have already made AI skills today’s priority. Looking ahead, quantum skills will begin to grow in demand with the steady advance of the technology. Meanwhile cybersecurity skills are an evergreen need.
    —Lydia Logan, VP of Global Education & Workforce Development, IBM

    This coming year, we’ll see real progress in using technology, particularly GenAI, to free up teachers’ time. This will enable them to focus on what they do best: working directly with students and fostering the deep connections crucial for student growth and achievement. GenAI-powered assistants will streamline lesson planning after digesting information from a sea of assessments to provide personalized recommendations for instruction to an entire class, small groups, and individual students. The bottom line is technology that never aims to replace a teacher’s expertise–nothing ever should–but gives them back time to deepen relationships with students.
    —Jack Lynch, CEO, HMH

    Looking to 2025, I anticipate several key trends that will further enhance the fusion of educators, AI and multimodal learning. AI-powered personalization enhanced by multimedia: AI will deliver personalized learning paths enriched with various content formats. By adapting to individual learning styles–whether visual, auditory, or kinesthetic–we can make education more engaging and effective. Expansion of multimodal learning experiences: Students will increasingly expect learning materials that engage multiple senses. Integrating short-form videos created and vetted by actual educators, interactive simulations, and audio content will cater to different learning preferences, making education more inclusive and effective. Deepening collaboration with educators: Teachers will play an even more critical role in developing and curating multimodal content. Their expertise ensures that the integration of technology enhances rather than detracts from the learning experience.
    –Nhon Ma, CEO & Co-founder, Numerade

    AI and automation become a competitive advantage for education platforms and systems. 2025 will be the year for AI to be more infused in education initiatives and platforms. AI-powered solutions have reached a tipping point from being a nice-to-have to a must-have in order to deliver compelling and competitive education experiences. When we look at the education sector, the use cases are clear. From creating content like quizzes, to matching students with education courses that meet their needs, to grading huge volumes of work, enhancing coaching and guidance for students, and even collecting, analyzing and acting on feedback from learners, there is so much value to reap from AI. Looking ahead, there could be additional applications in education for multimodal AI models, which are capable of processing and analyzing complex documents including images, tables, charts, and audio.
    —Rachael Mohammed, Corporate Social Responsibility Digital Offerings Leader, IBM

    Agentic and Shadow AI are here. Now, building guardrails for safe and powerful use will be key for education providers and will require new skillsets. In education, we expect the start of a shift from traditional AI tools to agents. In addition, the mainstream use of AI technology with ChatGPT and OpenAI has increased the potential risk of Shadow AI (the use of non-approved public AI applications, potentially causing concerns about compromising sensitive information). These two phenomena highlight the importance of accountability, data and IT policies, as well as control of autonomous systems. This is key mostly for education providers, where we think there will be greater attention paid to the AI guardrails and process. To be prepared, educators, students, and decision makers at all levels need to be upskilled in AI, with a focus on AI ethics and data management. If we invest in training the workforce now, they will be ready to responsibly develop and use AI and AI agents in a way that is trustworthy.
    —Justina Nixon-Saintil, Vice President & Chief Impact Officer, IBM

    Rather than replacing human expertise, AI can be used as a resource to allow someone to focus more of their time on what’s truly important and impactful. As an educator, AI has become an indispensable tool for creating lesson plans. It helps generate examples, activity ideas, and anticipate future students’ questions, freeing me to focus on the broader framework and the deeper meaning of what I’m teaching.
    –Sinan Ozdemir, Founder & Chief Technology Officer, Shiba Technologies; Author, Quick Start Guide to Large Language Models 

    Data analytics and AI will be essential towards tackling the chronic absenteeism crisis. In 2025, the conversation around belonging will shift from abstract concepts to concrete actions in schools. Teachers who build strong relationships with both students and families will see better attendance and engagement, leading more schools to prioritize meaningful connection-building over quick-fix solutions. We’ll see more districts move toward personalized, two-way school communications that create trust with parents and the larger school community. In order to keep up with the growing need for this type of individualized outreach, schools will use data analytics and AI to identify attendance and academic patterns that indicate students are at risk of becoming chronically absent. It won’t be dramatic, but we’ll see steady progress throughout the year as schools recognize that student success depends on creating environments where both students and families feel valued and heard.
    —Dr. Kara Stern, Director of Education and Engagement, SchoolStatus

    As access to AI resources gains ground in classrooms, educators will face a dire responsibility to not only master these tools but to establish guidelines and provide best practices to ensure effective and responsible use. The increasing demand for AI requires educators to stay informed about emerging applications and prioritize ethical practices, ensuring AI enhances rather than impedes educational outcomes.. This is particularly critical in STEM fields, where AI has already transformed industries and is shaping career paths, providing new learning opportunities for students. To prevent the exacerbation of the existing STEM gap, educators must prioritize equitable access to AI resources and tools, ensuring that all students, regardless of background, have the opportunity to engage with and fully understand these technologies. This focus on equity is essential in leveling the playing field, helping bridge disparities that could otherwise limit students’ future success. Achieving these goals will require educators to engage in professional development programs designed to equip them with necessary skills and content knowledge to implement new technology in their classrooms. Learning how to foster inclusive environments is vital to cultivating a positive school climate where students feel motivated to succeed. Meanwhile, professionally-trained educators can support the integration of new technologies to ensure that every student has the opportunity to thrive in this new educational landscape.
    —Michelle Stie, Vice President, Program Design & Innovation, NMSI

    Artificial intelligence (AI) is poised to increase in use in K-12 classrooms, with literacy instruction emerging as a key area for transformative impact. While educators may associate AI with concerns like cheating, its potential to enhance human-centered teaching is gaining recognition. By streamlining administrative tasks, AI empowers teachers to focus on connecting with students and delivering personalized instruction. One trend to watch is AI’s role in automating reading assessments. These tools reduce the time educators spend administering and analyzing tests, offering real-time insights that guide individualized instruction. AI is also excelling at pinpointing skill gaps, allowing teachers to intervene early, particularly in foundational reading areas.  Another emerging trend is AI-driven reading practice. Tools can adapt to each student’s needs, delivering engaging, personalized reading tutoring with immediate corrective feedback. This ensures consistent, intentional practice–a critical factor in literacy growth. Rather than replacing teachers, AI frees up educator time for what matters most: fostering relationships with students and delivering high-quality instruction. As schools look to optimize resources in the coming year, AI’s ability to augment literacy instruction can be an important tool that maximizes students’ growth, while minimizing teachers’ work.
    —Janine Walker-Caffrey, Ed.D., Chief Academic Officer, EPS Learning

    We expect a renewed focus on human writing with a broader purpose–clear communication that demonstrates knowledge and understanding, enhanced, not replaced by available technology. With AI making basic elements of writing more accessible to all, this renaissance of writing will emphasize the ability to combine topical knowledge, critical thinking, mastery of language and AI applications to develop written work. Instead of being warned against using generative AI, students will be asked to move from demand–asking AI writing tools to produce work on their behalf, to command–owning the content creation process from start to finish and leveraging technology where it can be used to edit, enhance or expand original thinking. This shift will resurface the idea of co-authorship, including transparency around how written work comes together and disclosure of when and how AI tools were used to support the process. 
    —Eric Wang, VP of AI, Turnitin

    GenAI and AI writing detection tools will evolve, adding advanced capabilities to match each other’s detectability flex. End users are reaching higher levels of familiarity and maturity with AI functionality, resulting in a shift in how they are leveraged. Savvy users will take a bookend approach, focusing on early stage ideation, organization and expansion of original ideas as well as late stage refinement of ideas and writing. Coupling the use of GenAI with agentic AI applications will help to overcome current limitations, introducing multi-source analysis and adaptation capabilities to the writing process. Use of detection tools will improve as well, with a focus on preserving the teaching and learning process. In early stages, detection tools and indicator reports will create opportunities to focus teaching on addressing knowledge gaps and areas lacking original thought or foundation. Later stage detection will offer opportunities to strengthen the dialogue between educators and students, providing transparency that will reduce student risk and increase engagement.
    —Eric Wang, VP of AI, Turnitin

    Advanced AI tools will provide more equitable access for all students, inclusive of reaching students in their home language, deaf and hard of hearing support through AI-enabled ASL videos, blind and visually impaired with real time audio descriptions, tactiles, and assistive technology.
    –Trent Workman, SVP for U.S. School Assessments, Pearson 

    Generative AI everywhere: Generative AI, like ChatGPT, is getting smarter and more influential every day, with the market expected to grow a whopping 46 percent every year from now until 2030. By 2025, we’ll likely see AI churning out even more impressive text, images, and videos–completely transforming industries like marketing, design, and content creation. Under a Trump administration that might take a more “hands-off” approach, we could see faster growth with fewer restrictions holding things back. That could mean more innovative tools hitting the market sooner, but it will also require companies to be careful about privacy and job impacts on their own. The threat of AI-powered cyberattacks: Experts think 2025 might be the year cybercriminals go full throttle with AI. Think about it: with the advancement of the technology, cyberattacks powered by AI models could start using deepfakes, enhanced social engineering, and ultra-sophisticated malware. If the Trump administration focuses on cybersecurity mainly for critical infrastructure, private companies could face gaps in support, leaving sectors like healthcare and finance on their own to keep up with new threats. Without stronger regulations, businesses will have to get creative–and fast–when it comes to fighting off these attacks.
    –Alon Yamin, Co-Founder & CEO, Copyleaks

    Laura Ascione
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  • 25 of Our Top Stories About Schools, Students and Learning – The 74

    25 of Our Top Stories About Schools, Students and Learning – The 74

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  • Learning Data Trends You Must Know in 2026

    Learning Data Trends You Must Know in 2026

    Learning data has played a larger role in the planning and operations of education systems. In 2026, the focus will shift from reporting what happened to actually using data to make informed decisions. Institutions are already tracking a wider range of learning conditions. System‑level indicators are being used to understand how students experience education in real settings. As data governance expectations mature, this evolution is a strategic opportunity and an operational requirement.

     

    The State of Learning Data in 2025: A Retrospective

    In 2025, learning data practices moved beyond experimentation and into daily operations. Several patterns stood out across the sector.

    As many platforms started responding dynamically to learner behavior, AI‑driven personalization and real‑time analytics became harder to ignore. The U.S. Department of Education’s AI report shows how real‑time data signals support educators with decision‑making tools like content pacing and targeted feedback. It also highlights why human oversight and transparency in AI‑supported systems are necessary.

    At the same time, institutions began using large‑scale datasets to identify intervention points earlier. CoSN’s 2025–26 emerging technology trends show that K–12 leaders are using aggregated engagement data to inform decisions earlier in the academic year.

    With the expansion of personalization, concerns about privacy and bias also increased. Ethical AI and federated learning models gained traction. Distributed data approaches that limit centralized storage while still enabling learning insights became more relevant, particularly for organizations serving multiple districts or states.

    Another notable shift was the rise of immersive and multimodal data sources. Deloitte’s analysis of higher‑education trends shows growing use of simulations, virtual labs, and experiential learning environments, all of which generate complex engagement data that goes beyond clicks or completion rates.

     

    5 Must-Know Learning Data Trends in 2026

    1. From Retrospective to Predictive Data Analytics

    The shift from retrospective analysis to predictive insights is the most vital learning data trend as we move into 2026. Dashboards that explain what already happened are giving way to models that signal what is likely to happen next.

    Predictive retention models are becoming central to student‑success strategies. Enrollment data from the National Student Clearinghouse show continued volatility in postsecondary enrollment, reinforcing the importance of early identification of at‑risk students rather than reactive interventions.

    Adaptive learning systems increasingly use AI‑driven signals to adjust content difficulty, recommend resources, or trigger educator outreach before learners disengage. Institutions are also applying predictive analytics to enrollment forecasting and resource planning, helping leaders prepare for demand shifts rather than responding after the fact.

    For 2026, the value lies in proactive decision‑making.

    • K–12 Districts: Predictive signals support early‑warning systems for attendance, disengagement, and dropout risk.
    • Higher Education: Predictive advising models help institutions support persistence and degree completion more effectively.
    • EdTech Companies: Usage analytics can identify friction points in the learner experience before they affect retention or outcomes.

    The shift toward prediction marks a practical change in how learning data is used.

    2. Ethical, Privacy‑First Data Governance

    As learning data becomes more powerful, governance expectations are tightening. In 2026, ethical and privacy‑first data practices will be foundational, not optional.

    Federated learning and decentralized analytics models are gaining relevance because they reduce the need to move or duplicate sensitive student data. Federal guidance on student privacy emphasizes minimizing data exposure while still enabling legitimate educational use, particularly when advanced analytics or AI are involved.

    At the same time, compliance requirements are becoming more explicit. Updated FERPA resources and guidance reinforce schools’ responsibilities around data access, consent, and transparency, while COPPA and state‑level privacy laws continue to evolve.

    In 2026, strong governance will not slow innovation. It will determine which organizations are trusted to scale it.

    3. Data Unification Across Platforms and Systems

    Learning data still sits in separate systems. LMS platforms track activity. SIS tools store records. Assessment and engagement tools add another layer. As a result, information often remains fragmented. As noted in market analysis, interoperability challenges continue to slow integration across these systems. When data are brought together, their role changes.

    What unification enables:

    • Attendance and grades establish academic context
    • Engagement signals reveal patterns as they emerge
    • Assessment outcomes confirm where support is effective

    Viewed together, this information supports earlier and more informed decisions across instruction and operations. District leaders are actively pushing for integrated data  environments to make this possible at scale.

    By 2026, leadership teams will expect consolidated learner views rather than disconnected reports generated by individual systems.

    4. Analytics for Product‑Led Growth in EdTech

    For EdTech companies, analytics are no longer limited to reporting usage. They increasingly influence how products evolve.

    Teams are using analytics to understand how features are adopted, where learners disengage, and which workflows support sustained use. Feature‑level usage data are becoming a core input for continuous‑improvement decisions across learning products.

    Common areas of focus include:

    • Feature adoption across different learner groups
    • Drop‑off points within learning flows
    • Signals that indicate confusion or friction

    Product teams are also relying more on controlled testing to validate changes before scaling them. Evidence‑based iteration is increasingly tied to quality and accreditation expectations, reinforcing the role of analytics in product decision‑making.

    By 2026, EdTech companies that consistently use analytics to guide product iteration will be better positioned to respond to changing learner needs.

    5. Visual, Explainable Analytics for Educators

    As learning data grows in volume, usability becomes a limiting factor. Information that cannot be interpreted quickly rarely informs day‑to‑day decisions in classrooms or academic teams.

    Clear and accessible data presentation has long been tied to better decision‑making in education systems, particularly when insights are intended for non‑technical users. This emphasis on clarity becomes more important as analytics move closer to instructional practice.

    Educators tend to engage with analytics when:

    • Signals are easy to interpret
    • Alerts include context, not just flags
    • Recommendations are tied to observable evidence

    By 2026, trust in learning analytics will depend less on model sophistication and more on  whether educators can understand where insights come from and how to act on them.

     

    Segment Spotlight: Unique Needs and Data Trends

    Different segments are solving different problems with learning data.

    K–12 School Districts

    • Early‑warning indicators
    • Attendance and behavior trends
    • Equity and access signals

    Higher Education

    • Enrollment forecasting
    • Learner‑pathway analysis
    • Retention monitoring

    EdTech Product Teams

    • Feature‑adoption metrics
    • Cohort‑behavior analysis
    • Real‑time engagement signals

     

    Preparing for 2026 and Beyond: Actionable Recommendations

    Focus on execution, not frameworks

    • Define where prediction adds value
    • Set clear rules for data access and use
    • Reduce duplication across systems
    • Present insights in educator‑friendly formats
    • Reassess data maturity as tools evolve

     

    Preparing for the Next Phase of Learning Data

    The next phase of learning data will be shaped not by how much insight organizations generate, but by how consistently they act on it. As data move closer to everyday decisions, they start influencing instruction, product design, and learner support in real ways.

    That shift brings opportunity, but it also raises expectations. Insight needs to be usable. Systems need to be trustworthy. Decisions need to be grounded in evidence, not noise.

    Organizations that treat learning data as a practical tool rather than a theoretical asset will be better positioned for what 2026 demands.

     

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  • This math platform leverages AI coaching to help students tackle tough concepts

    This math platform leverages AI coaching to help students tackle tough concepts

    eSchool News is counting down the 10 most-read stories of 2025. Story #5 focuses on a math platform that offers AI coaching for maximum impact.

    Math is a fundamental part of K-12 education, but students often face significant challenges in mastering increasingly challenging math concepts.

    Many students suffer from math anxiety, which can lead to a lack of confidence and motivation. Gaps in foundational knowledge, especially in early grades and exacerbated by continued pandemic-related learning loss, can make advanced topics more difficult to grasp later on. Some students may feel disengaged if the curriculum does not connect to their interests or learning styles.

    Teachers, on the other hand, face challenges in addressing diverse student needs within a single classroom. Differentiated instruction is essential, but time constraints, large class sizes, and varying skill levels make personalized learning difficult.

    To overcome these challenges, schools must emphasize early intervention, interactive teaching strategies, and the use of engaging digital tools.

    Last year in New York City Public Schools, Franklin Delano Roosevelt High School (FDR) teachers started using a real-time AI math coaching platform from Edia to give students instant access to math support.

    Edia aligns with Illustrative Mathematics’ IM Math, which New York City Public Schools adopted in 2024 as part of its “NYC Solves” initiative–a program aiming to help students develop the problem-solving, critical thinking, and math skills necessary for lifetime success. Because Edia has the same lessons and activities built into its system, learning concepts are reinforced for students.

    FDR started using Edia in September of 2024, first as a teacher-facing tool until all data protection measures were in place, and now as an instructional tool for students in the classroom and at home.

    The math platform’s AI coaching helps motivate students to persevere through tough-to-learn topics, particularly when they’re completing work at home.

    “I was looking for something to have a back-and-forth for students, so that when they need help, they’d be able to ask for it, at any time of the day,” said Salvatore Catalano, assistant principal of math and technology at FDR.

    On Edia’s platform, an AI coach reads students’ work and gives them personalized feedback based on their mistakes so they can think about their answers, try again, and master concepts.

    Some FDR classes use Edia several days a week for specific math supports, while others use it for homework assignments. As students work through assignments on the platform, they must answer all questions in a given problem set correctly before proceeding.

    Jeff Carney, a math teacher at FDR, primarily uses the Edia platform for homework assignments, and said it helps students with academic discovery.

    “With the shift toward more constructivist modes of teaching, we can build really strong conceptual knowledge, but students need time to build out procedural fluency,” he said. “That’s hard to do in one class session, and hard to do when students are on their own. Edia supports the constructivist model of discovery, which at times can be slower, but leads to deeper conceptual understanding–it lets us have that class time, and students can build up procedural fluency at home with Edia.”

    On Edia, teachers can see every question a student asks the AI coach as they try to complete a problem set.

    “It’s a nice interface–I can see if a student made multiple attempts on a problem and finally got the correct answer, but I also can see all the different questions they’re asking,” Carney said. “That gives me a better understanding of what they’re thinking as they try to solve the problem. It’s hugely helpful to see how they’re processing the information piece by piece and where their misconceptions might be.”

    As students ask questions, they also build independent research skills as they learn to identify where they struggle and, in turn, ask the AI coach the right questions to target areas where they need to improve.

    “We can’t have 30 kids saying, ‘I don’t get it’–there has to be a self-sufficient aspect to this, and I believe students can figure out what they’re trying to do,” Carney said.

    “I think having this platform as our main homework tool has allowed students to build up that self-efficacy more, which has been great–that’s been a huge help in enabling the constructivist model and building up those self-efficacy skills students need,” he added.

    Because FDR has a large ELL population, the platform’s language translation feature is particularly helpful.

    “We set up students with an Illustrative Math-aligned activity on Edia and let them engage with that AI coaching tool,” Carney said. “Kids who have just arrived or who are just learning their first English words can use their home languages, and that’s helpful.”

    Edia’s platform also serves as a self-reflection tool of sorts for students.

    “If you’re able to keep track of the questions you’re asking, you know for yourself where you need improvement. You only learn when you’re asking the good questions,” Catalano noted.

    The results? Sixty-five percent of students using Edia improved their scores on the state’s Regents exam in algebra, with some demonstrating as much as a 40-point increase, Catalano said, noting that while increased scores don’t necessarily mean students earned passing grades, they do demonstrate growth.

    “Of the students in a class using it regularly with fidelity, about 80 percent improved,” he said.

    For more spotlights on innovative edtech, visit eSN’s Profiles in Innovation hub.

    Laura Ascione
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