I have been blogging for years, almost two decades here on Blogger in fact. I have been discussing education for longer. But this is something new I hope.
I am calling this site "The Learning Innovation Lab" to talk about what is new in education, learning, and related technologies. For the near future, this will also be a place for me to discuss ideas I have on the future of education.
Like my other blogs, I'll let this one grow organically.
Looking back, moving forward
This week, we’ve walked through four major waves of technology in education:
* Computers (1980s–1990s) brought the promise of interactive learning, but also deepened divides in access.
* The Internet (1990s–2000s) connected classrooms to the world, but left students and educators wrestling with digital literacy.
* Devices like iPads and Chromebooks (2000s–2020s) gave each student a portal to learning, but raised new questions about engagement and equity.
* AI (2020s–Today) now personalizes feedback, accelerates planning, and unlocks creativity, but also challenges our definitions of authorship, ethics, and integrity.
Each innovation opened doors, but each left gaps we still grapple with. Access, equity, literacy, and engagement remain constant themes. What changes with each technological leap is how they manifest.
As educators and leaders, our challenge is to ensure that every leap forward doesn’t widen the distance between those who can benefit and those who cannot. The tools may evolve, but the responsibility to teach wisely and equitably remains the same.
👉 The lesson? Technology can amplify both opportunity and inequality. Our role is to tip the balance toward opportunity.
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A new frontier of learning
If the computer, Internet, and device revolutions reshaped the where and how of learning, AI is reshaping the who. By the early 2020s, tools like ChatGPT, adaptive learning platforms, and AI-powered grading assistants were no longer theoretical, they were in classrooms, on teacher laptops, and in student pockets.
AI promises personalization on a scale we’ve never seen. A student struggling with algebra can get instant feedback. An English learner can generate practice dialogues. Teachers can automate lesson planning or even test assessments against AI to ensure rigor.
What AI offers:
* Tailored, real-time support for students via personal feedback.
* Enhanced productivity for educators through planning, grading, and scaffolding.
* New modes of creativity and problem-solving. New ways to practice critical thinking.
But here’s the gap:
AI raises questions that go far beyond the classroom: authorship, privacy, ethics, and overreliance. And this is not even considering the environmental impact of all those NVIDIA processors generating heat for a picture of a bunny drinking their morning coffee. Just like in the 1980s, access isn’t equal; schools with resources can pilot AI programs, while others struggle to keep up. And the definition of academic integrity is shifting again.
*👉 The lesson? Technology doesn’t replace educators; it demands we redefine our role.*
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One device per student
In 2010, Apple released the first iPad, promising an education-friendly device that could replace textbooks. Around the same time, Google Chromebooks (2011) offered a low-cost alternative, making one-to-one computing programs realistic for schools across the globe.
These devices transformed learning spaces. Suddenly, every student had a portal to the digital world. Lessons became multimedia-rich, interactive, and portable. Assignments could be distributed, submitted, and graded instantly. Accessibility features like text-to-speech, magnification, and translation supported learners in new ways.
Back in the 1990s, I was working on a project for a sub-$100 computer that students could use. These internet-connected devices solved this problem. While the price point is still a bit higher than I anticipated, they do what I had wanted back then.
What tablets and Chromebooks delivered:
* Personalized learning platforms at scale.
* Greater inclusivity for diverse learning needs.
* Learning beyond the classroom, anytime, anywhere.
But here’s the gap:
Screens also changed the social fabric of classrooms. Screens also brought distractions into the classroom. Teachers had to compete with games, messaging, and endless tabs. Device management became a new layer of complexity. And despite progress, equity issues persisted: some students had brand-new devices, others worked on outdated hand-me-downs or lacked internet access at home. The gap widens more.
*👉 The lesson? Access is not the same as engagement. Equal access is even in question.*
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The world came into the classroom
By the mid-1990s, the Internet had begun its steady march into schools. Dial-up tones gave way to high-speed connections, and suddenly students could look up more than what was in a textbook. By the 2000s, resources like online encyclopedias, digital libraries, and early e-learning platforms changed research forever.
I remember when the classroom walls felt like they had dissolved. Students were collaborating on projects with peers in other states, even countries. Teachers could send home assignments by email instead of handouts. Learning became global. Online databases transformed research from hours in the library to minutes at a screen.
The World Wide Web, as it was most known back then, delivered on the promises I thought CD-ROMs were going to fufill; massive (alomst 800 megabytes) collections of data with images and even some animations or movies. The web was unlimited, you just had to know where to look.
What the Internet unlocked:
* Access to vast digital libraries and primary sources.
* The rise of distance learning and virtual classrooms.
* Collaboration tools that made students publishers, not just consumers.
But here’s the gap:
The web didn’t come with a guidebook. Students could find information instantly, but not always evaluate it. Accuracy, safety, and digital citizenship became pressing concerns. Access shifted from “Can we get online?” to “Can we navigate wisely?”
With the world at your fingertips, you have access to both good and bad information in equal measure.
*👉 The lesson? Every new tool requires new literacies.*
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The first wave of digital learning
In 1983, the Apple IIe arrived in classrooms. By 1984, the first Macintosh promised a “computer for the rest of us.” These machines were symbols of a new era. For many students, they were the first encounter with a screen that could “think.”
When I think back to the earliest days of computers in classrooms, I remember the buzz of curiosity. Students are often found crowding around a glowing green screen (or amber or blue), typing BASIC code from magazines, playing Oregon Trail, or experimenting with simple simulations. Computers promised a world where learning was interactive, not just passive.
While my school had a computer lab filled with TRS-80 computers (and a shared hard disk of 10 MB!), they were not something everyone used. Our typing class, for example, was still done on IBM Selectric typewriters. Word Processing was not something we had heard of yet.
What computers gave us:
* “Interactive” simulations that brought abstract concepts to life. I say interactive in quote;, they were nothing like the interactives we use today.
* Tools for writing, problem-solving, and creativity.
* The first hints of individualized learning.
But here’s the gap:
These machines were expensive. Many schools had only a small computer lab, and access was uneven. Teacher training lagged behind, leaving many devices underused. Instead of leveling the field, computers often deepened the digital divide.
Access was limited to students who had passed particular math courses, so divide began wide and only got wider.
*👉 The lesson? Innovation without equity is just another barrier.*
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I am revisiting ideas from the past year on AI topics in Higher Education. The landscape for the tools changes quickly, but can universities and institutions of higher education keep up?
*When AI can lecture, answer, and grade, what’s left for the professor?*
The answer: everything that makes learning human.
The educator’s role is shifting:
* From content delivery → to coaching. Faculty provide mentorship, context, and wisdom beyond the facts.
* From gatekeeping → to guiding. They no longer guard knowledge, they guide students in applying it responsibly.
* From static expertise → to adaptive expertise. They model how to learn, unlearn, and relearn in a world of constant change.
AI will not replace professors. It will redefine them. The institutions that invest in this transition will lead the next chapter of higher education.
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I am revisiting ideas from the past year on AI topics in Higher Education. The landscape for the tools changes quickly, but can universities and institutions of higher education keep up?
Not every student enters the classroom with equal access.
That’s always been true. Starting in the 1980, there was a gap between schools that had computers and those that did not. Then it became the A/V media gap, then the Internet gap, and on to iPads and smart devices. The gap remained, and some school districts had to do the best they could to catch up. My Ph.D. advisor called it the Matthew Principle. One of my first projects in the 1990s was to figure out a sub $100 computer for students in low-income school districts. The challenges were daunting.
But with AI, the gap could widen, or close, depending on institutional choices.
Here’s the fork in the road:
* Widening the gap: Students with personal devices, tech fluency, and confidence get ahead.
* Closing the gap: Institutions integrate AI openly, provide training, and treat it as a universal resource.
* The new divide: Tomorrow’s gap won’t just be technology or internet access, it will be AI literacy.
Equity isn’t automatic. Without intentional design, the students who most need opportunity will be the last to receive it. It is up to faculty, instructors, administrators, and instructional designers to design this equity into the curricula we are responsible for.
I am revisiting ideas from the past year on AI topics in Higher Education. The landscape for the tools changes quickly, but can universities and institutions of higher education keep up?
*AI is either the burden or the breakthrough.*
Some faculty see AI as more work, new rules, new risks, new confusion. Others see it as relief, fewer hours grading, easier prep, faster feedback.
Which way it goes depends on support.
* With training: AI streamlines routine tasks, freeing faculty to mentor and research.
* Without training: It becomes a distraction, creating frustration instead of relief.
* With collaboration: Instructional designers can help faculty reimagine assignments instead of just policing them.
AI won’t magically lighten the load. But with the right strategy, it can restore time to the parts of teaching that matter most.
Faculty should be encouraged to leverage AI tools where they can, for workload, even for simple processes. But as with students, a clear directive needs to come from the institutions on what is acceptable AI use.
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I am revisiting ideas from the past year on AI topics in Higher Education. The landscape for the tools changes quickly, but can universities and institutions of higher education keep up?
*Plagiarism doesn’t look like it used to.*
Copy-and-paste cheats were yesterday’s challenge. Today, AI can generate unique content on demand, no stolen words, no easy detection, yet the same core question remains: What does it mean to claim your own work?
The path forward:
* Move beyond detection. Policing tools alone are unreliable and undermine trust. Even the best ones are only playing "catch-up" to the tools used to generate content.
* Set clear boundaries. Faculty and students need consistent definitions of acceptable AI use. A solid AI policy of what is and what is not acceptable helps with any confusion or accidental plagiarism.
* Teach responsible use. Like calculators or spellcheck, AI must be framed as a tool for learning, not replacing it.
Academic integrity has always been about honesty and ownership. AI doesn’t erase those values, it demands we reinforce them in new ways.
I am revisiting ideas from the past year on AI topics in Higher Education. The landscape for the tools changes quickly, but can universities and institutions of higher education keep up?
*When AI can write the essay, how do we measure the student?*
Traditional assessments, essays, quizzes, and take-home projects, were all designed for a world where human output was the baseline. Now, AI can perform these tasks with remarkable fluency, leaving educators wondering if we’re measuring learning or prompt engineering.
Here’s what must change:
* Shift to authentic performance. Evaluate collaboration, application, and reflection, the tasks AI cannot easily mimic.
* Assess the process, not just the product. Document how students engage, iterate, and learn with AI tools in the mix.
* Integrate AI fluency into rubrics. Judge how responsibly and effectively learners use these tools, not just whether they avoid them.
Assessment isn’t broken, it’s evolving. The institutions that adapt now will lead in defining academic excellence in the AI era.
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