What Michigan schools reveal about reversing chronic absenteeism — from hechingerreport.org by Jill Barshay
Time-intensive home visits show promise

Absenteeism is a huge and seemingly intractable problem for the nation’s public schools. And Michigan has one of the worst attendance rates in the country.

Yet a new study released in May offers hope. Researchers found that some Michigan schools appear to be substantially better than others at getting students to show up, and identified one intervention — frequent home visits to families whose children are absent from class — that was used more often by schools making a difference.

The findings are a reminder that “best practices” recommendations often overstate what researchers actually know. Schools can make a meaningful difference in attendance, but identifying genuinely successful schools is hard, isolating why they succeed is even harder, and simple solutions rarely hold up under scrutiny.

 

What AI-Enabled Education Actually Looks Like When It’s Working for Workforce Students — from gettingsmart.com by Stephen Griffin

Key Points

  • Institutions can use AI to make skills, pathways, and job outcomes visible to students and employers in ways traditional transcripts cannot.
  • Academic affairs, workforce development, career services, and employers need a shared definition of readiness and competency before tools can deliver meaningful value.

The second is portable competency records. Learning and employment records — AI-enabled documentation of what a student knows and can do, expressed in language employers recognize — are the infrastructure that makes credentials legible across the education-to-employment continuum. When a student can show an employer not just “completed Supply Chain Management 101” but “demonstrated proficiency in inventory optimization, route planning, and logistics software at the industry-recognized level,” the credential stops being abstract. It becomes evidence. Building these records requires investment in tools, yes — but more importantly, it requires faculty, workforce development staff, and employer partners to agree on what competency actually looks like before the technology is ever purchased.


 

 

When Students Don’t Show Up, Everyone Pays — from thelearningcounsel.com by Dr. Atiya Y. Perkins

The imagined student is disengaged, indifferent, choosing video games over algebra. The actual student is frequently exhausted. She may be the oldest child in a household where a parent is ill, which means she is responsible for getting younger siblings fed and out the door before she can even think about her own way to school. He may be managing an untreated health condition because his family cannot afford consistent medical care. They may have moved three times since September and still haven’t fully sorted out transportation to a building they barely feel they belong to.

Toldson’s research documents more than 70 distinct barriers that contribute to chronic absenteeism, and very few of them have anything to do with motivation. Housing instability, food insecurity, unaddressed mental health needs, and unreliable transportation all appear on that list. So does something we rarely discuss openly: the growing number of students who have caregiving responsibilities that would overwhelm even the most capable and supported adults.

Understanding this should fundamentally change how we respond. A court referral does not help a student whose bus route was eliminated. A warning letter does not make a family that moved last month feel more at home. Instead, these warnings and referrals actively damage the relationship between schools and the families we most need to reach, at precisely the moment when trust is the only currency that matters.

 

Inside the latest global research on school cellphone bans — from hechingerreport.org by Jill Barshay
First wave of studies raises questions about other digital distractions and cellphones at home

But the first wave of rigorous research on those policies — including two major U.S. studies — does not point neatly in one direction. Some studies have found modest academic gains from cellphone restrictions. Others have found little to no effect on test scores, even when student phone use dropped sharply. Some studies suggest benefits for low-achieving students, others for girls, and still others for boys. In some places, attendance or student well-being improved. In others, they didn’t.

The scientific process can be messy. Cultural differences may explain why the bans are more effective in some places than others. But almost any education reform will get different results in different places, even within a single country. And the current confusion may also stem from how difficult it is to study cellphone bans in the real world.

Ideally, researchers would randomly assign some students to surrender their phones while others kept them, and then measure the effect on academic performance — the equivalent of a clinical trial for an education policy. But those experiments are difficult to enforce in schools, and so far only one study, conducted among college students in India, has attempted a randomized controlled trial. It produced a notably strong improvement in course grades for lower achieving students.

Instead, most studies rely on rougher real world comparisons that capture only partial effects of cellphone restrictions.

 

Deans for Impact Releases New Edition of The Science of Learning  — from deansforimpact.org
Second edition of seminal report reflects new research amidst growing momentum for evidence-based instruction in teacher preparation and PK-12.

AUSTIN, Texas (May 19, 2026) – Deans for Impact (DFI) today released the second edition of The Science of Learning, a report translating cognitive-science research into practical implications for teaching. The updated edition includes new research on memory, attention, motivation, and learning misconceptions, offering educators a research-based foundation for understanding how to support durable student learning.

First released in 2015, The Science of Learning is DFI’s most widely-used and cited resource, with more than one million downloads. Since its publication, DFI has supported nearly 300 teacher-preparation programs to make instructional quality a priority in the way teachers are prepared, directly impacting more than 110,000 teachers over the last decade.

The second edition arrives at a moment when more than 40 states have made meaningful investments in strengthening evidence-based instruction, particularly in early literacy, mathematics, and the use of high-quality instructional materials. The science of learning supports future teachers to build a comprehensive foundation for instructional decision-making that cuts across content areas and grade levels.

The report has been endorsed by more than 100 field experts and leading organizations across the United States and internationally.

Download the report at deansforimpact.org/thescienceoflearning.


An example excerpt:

 

“The sad fact is that we don’t teach learners how to be good at learning. Whether K12, higher ed, or organizations, it’s just not there.”

 

from Clark Quinn’s posting entitled, Thoughts on meta-coaching!

 

From DSC:
I agree. We could do a much better job at this.

 

LinkedIn Grad’s Guide 2026: Starting your career in the AI era — from linkedin.com by Gianna Prudente
To help you head off in the right direction, we’ve identified where those starting their careers are finding opportunity, based on data from millions of LinkedIn member profiles.

While all of this is happening, colleges are still catching up. Many students are graduating without having spent much time learning how AI actually fits into day-to-day work — even as employers seek out those exact skills.

“Colleges are moving into an era of, we’ll let the faculty decide, which leads to a very uneven experience for students because some faculty are really into AI and other faculty are not,” says Jeff Selingo, a higher education strategist. “Employers are the same; they don’t really know how to act around early careers.”

Taken together, new grads are entering a uniquely challenging environment: fewer traditional entry points, slower turnover and a workplace that’s evolving faster than the systems preparing people for it.

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I knew my writing students were using AI. Their confessions led to a powerful teaching moment — from theguardian.com by Micah Nathan
The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words

For a few moments, all was quiet except the classroom’s ticking radiators. Then, a teary-eyed confession: one of the ostensible authors said she only used AI because she was scared of looking stupid, of being criticized for bad writing. She said she loved writing stories and hated having used AI. But she couldn’t stop herself, recounting a sequence similar to an addict’s descent: at first she fed her story into AI for a grammar check, it suggested line edits and she accepted, then it asked if she wanted structural edits, then it offered to rewrite the entire piece.

The other would-be author admitted he had never written a short story before and he had an idea but didn’t know where to start. I asked him why he didn’t reach out to me for help. He shrugged.

One of the other students raised her hand, saying she didn’t understand why it was bad for AI to write stories as long as the stories are based on their ideas. More students spoke: one wanted to know how using AI was any different from using a human editor. Another wanted me to answer why, at a university that launched one of the world’s first AI research programs in 1959, were we even having this debate? Isn’t AI meant to make everyone’s life easier? Less stressful? Isn’t the point of AI to free humans from the tedium of rote tasks?

The conversation that followed their confessions was one of the most productive teaching moments of my eight years at MIT. Writing, I told them, isn’t supposed to be easy, and of course it can be tedious but that doesn’t make it rote. Writing isn’t just the production of sentences – it’s the training of endurance by way of sustained attention. It’s a way of learning what one thinks by attempting to say it. 


This $10K AI School Promises to Future-Proof Your Career — from builtin.com by Matthew Urwin
Khan Academy, TED and ETS are starting a new program to equip students and professionals with the skills to thrive in an increasingly AI-driven economy. Here’s what you need to know.

Summary: The Khan TED Institute is a higher-education program that will teach students and workers how to use AI through interactive learning. The program’s AI-centric curriculum is an unproven approach, though, casting doubt on whether it will actually improve learning outcomes and career prospects.


 

Want Students to Build a Healthier Relationship With Technology? Start With The Arts — from techlearning.com by Adrianna Marshall
Arts classrooms demonstrate what technology integration at its best can look like

But at a moment defined by rapid AI adoption and ongoing debates about screen time, the argument for protecting and investing in arts education needs to take on a new tone. The arts continue to be one of the most effective places in school for students to build healthier, more intentional relationships with technology.

In short, in the age of AI, we need the arts more than ever.

Digital composition software, notation tools, and recording platforms allow students to experiment, revise, and refine their ideas in ways that would have been far more time-consuming a decade ago. Students can layer tracks, hear immediate playback, annotate their own scores, and collaborate across devices. The same is true in other contexts besides music; in visual arts, for instance, a variety of digital drawing and painting platforms enable students to practice with new mediums, styles, and techniques without having to worry about supplies or messes. But in either case, the core intellectual work of looking and listening critically, understanding structure, and making aesthetic choices remains entirely human and part of the learning.


From DSC:
I agree. At one of my previous positions, I spent 10 years supervising a digital studio — helping professors and students use a variety of applications to create things. The applications were from Adobe, Apple, and a variety of smaller vendors. The deliverables could be graphics, edited soundtracks, music, videos, flyers, posters, collages, edited photographs, presentations, websites, and more. I longed for people to discover the power of multimedia to communicate their messages, tell stories, stir emotion, powerfully engage themselves (and others), and unleash their creativity.

There were several obstacles to our digital studio being more impactful at that institution. It was under the IT department, not the academic side of the house. It was in the basement of the library, where few students and faculty traveled. During those years, it was highly uncommon for faculty members to require multimedia-based assignments — so many students had to WANT to develop these skills on their own time. The majority of students didn’t see the value in developing the types of digital skills that we were trying to build…or they didn’t have the time.


Also relevant/see:


 

Why Sal Khan’s AI revolution hasn’t happened yet, according to Sal Khan — from chalkbeat.org by Matt Barnum

Three years ago, as Khan Academy founder Sal Khan rolled out an AI-powered tutoring chatbot, he predicted a revolution in learning.

So far, the revolution hasn’t happened, he acknowledges.

“For a lot of students, it was a non-event,” Khan told me recently about his eponymous chatbot, Khanmigo. “They just didn’t use it much.”

Khan gives this analogy: Imagine he walked into a class, sat in the back of the room, and waited for students to seek out help. “Some will; most won’t,” he said. That’s been the experience with AI tutoring, he said. It doesn’t necessarily make students motivated to learn or fill in gaps in knowledge needed to ask questions.

“AI is going to help,” said Khan of this reimagined Khan Academy. “But I think our biggest lever is really investing in the human systems.”

 

“Learning ecosystems begin with people.” — Getting Smart


ASU/GSV Summit

There’s something about walking into a space like the ASU+GSV Summit that feels a little like stepping into a living, breathing idea. You hear fragments of possibility in passing conversations, see it in the way people lean in a little closer during sessions, feel it in the quiet moments when something lands and you know it’s going to stay with you. This year, what lingered wasn’t just the talk of innovation; it was a deeper pull toward something more human. A reminder that before we build better systems, we have to create better conditions for dreaming. And there’s a kind of quiet joy that emerges when educators find each other in that work, when ideas connect, and you can feel the bridges across networks and ecosystems getting stronger in real time.

And dreaming is not a given. It requires space, safety, and adults who understand the weight of what they’re holding. The most powerful moments weren’t about what we can do for learners, but how we show up with them. Adults who are still learning, still stretching, still willing to have their thinking reshaped are the ones who make room for young people to imagine beyond what they’ve seen. That kind of space doesn’t happen by accident. It’s protected. It’s intentional. It’s built by people who know their non-negotiables, who draw clear lines around dignity and belonging so learners can take risks without fear of losing themselves in the process.

Across conversations on pathways, experience, and AI, there was a steady undercurrent. Knowledge alone isn’t carrying the day anymore. Young people need chances to test, to try, to wrestle with ideas in real contexts. That’s where wisdom starts to take shape. AI showed up as a partner in that work, not the main character, but a tool that can expand thinking when used well. Still, the heartbeat of it all is human. It’s the relationships, the networks, the shared belief that we don’t have to do this alone. When adults come together to learn, to challenge each other, and to build something bigger than their own corner, they create the kind of ecosystems where young people don’t just prepare for the future, they begin to shape it.


Also from Getting Smart:

 
 
 

The quest to build a better AI tutor — from hechingerreport.org by Jill Barshay
Researchers make progress with an older ed tech idea: personalized practice

One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.

A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a study of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.

But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.

The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.

The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months.

To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.

 

Michigan schools may be leaning harder on subs. See your district’s shift in teaching staff. — from mlive.com by Jackie Smith

School districts across Michigan could be increasingly leaning on new and substitute teachers in the classroom, according to the latest K-12 staffing data tracked by the state.

Michigan’s Center for Educational Performance and Information updated staffing counts for districts through the current 2025-26 school year in late March, and the numbers largely confirm trends illustrated in other datasets.

The total number of teachers is on the rise ? with fewer sticking around more than a handful of years ? even as student enrollment goes down, and districts are continuing to use subs to fill in the gaps.

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From DSC:
One of our daughters obtained the credentials to teach in the elementary schools of Michigan. She was a very relational teacher and she taught at several schools over several years, but the straw that broke the camel’s back was when she taught at a school where:

  • They would have to evacuate the classrooms at times if a student was going through the roof (emotion-wise)
  • The students hit the principal
  • The students often didn’t listen to or obey her instructions — which constantly tested her patience and drained her energy
  • Many of the parents were not on the same team as the teachers — for a variety of complex reasons
  • …and for other reasons as well.

The system was discouraging. It was too much to bear. So the system lost another good teacher. 


Also see:

Michigan’s teacher shortage could be stabilizing, but data shows there’s a catch — from mlive.com by Jackie Smith

Michigan’s K-12 teacher workforce could be stabilizing, but schools across the state may be increasingly relying on educators working virtually or across multiple districts and those who are not fully certified, according to the latest data.

The Education Policy Innovation Collaborative (EPIC) at Michigan State University released its 2026 teacher shortage report earlier this month, which tracks hiring and vacancy trends, as well as what subjects are particularly impacted by fluctuations.

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Special education positions see the biggest vacancy rates
The vacancy rate for special education teachers is nearly double is nearly double the statewide average overall.

According to the report, more than 5% of special ed full-time equivalent positions were vacant in fall 2024.

MSU’s Education Policy Innovation Collaborative attributed at least some of that to the higher attrition from teachers that special ed positions see compared to other disciplines.

 

Why Educators Must Become AI Literate, And How to Start  — from edmentum.com by Priten Soundar-Shah

Much of our focus these few years has been spent helping students learn how to use AI responsibly, especially to combat cheating and plagiarism, and also with consideration given to productive learning, critical thinking, and online safety. But we are still behind on building fundamental literacy for teachers. Recent data supports this literacy gap. For example, Microsoft Education found that 80% of teachers say they are using AI, but 60% have received no or little training. We cannot continue to expect teachers to build student AI literacy without defining what success looks like for educator literacy, and there we’re falling short.

In some instances, AI literacy in the classroom is being defined as the ability to use chat tools to produce some sort of outcome. By that standard, we’re doing much better than we were three years ago. Students and teachers are increasingly turning to AI tools to produce study aids, outlines, drafts, and other content. And, some schools do provide training that is often concentrated on a particular vendor’s tool and how to use it effectively in the classroom.

However, we are leaving out the training that is necessary to help educators learn how to decide when to use or not use the technology and what the implications of that are. For example, I’ve spoken to teachers who have access to a variety of AI tools, have received training on how to use them, but still don’t incorporate them into their workflow, because they don’t know if it’s “right.”

 
© 2025 | Daniel Christian