On 04/06/2026,
in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, digital learning, education, education technology, emerging technologies, engagement / engaging students, future, game-changing environment, generational differences, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, K-12 related, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning theories, online learning, participation, personalized/customized learning, research, student-related, teachers, technologies for your home, United States, universities,
by Daniel Christian
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.
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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.
On 03/13/2026,
in attention, education, experimentation, graphics, ideas, K-12 related, learning, learning ecosystem, Learning Experience Design, learning spaces, learning theories, multimedia, pedagogy, strategy, student-related, teachers,
by Daniel Christian
An Unconventional Seating Plan Designed to Benefit Focus and Learning — from edutopia.org by Tyler Rablin
After years of search and experimentation, this teacher finally hit on a room layout that allowed for efficient shifting between whole class, small group, and independent work.
I used to be an obsessive classroom rearranger—every six weeks or so I would find myself looking for a new desk arrangement that would improve some aspect of our work in the room. So when I finally found a desk arrangement that I didn’t want to change for the rest of the year, I knew I was on to something good.
The idea started developing when I stumbled across an article about an Australian classroom arrangement based on three “archetypal learning spaces”: campfires, caves, and watering holes. Essentially, the idea is that students need a physical space to work independently (a cave), spaces to gather informally (campfires), and a space to gather as a whole to learn from an expert (the watering hole).
Using Trauma-Informed Practices in Early Elementary Classrooms — from edutopia.org by Emily Barbour
Small changes in language and classroom routines can increase connection and improve learning for young students.
Trauma-informed practices invite a shift from reactive to proactive systems. To design classrooms that are grounded in safety and care, teachers need to embed predictability, co-regulation, and relationship-building into daily routines. Seemingly small changes like morning choice, intentional language, and shared commitments can transform the environmental conditions for students to properly regulate, feel connected, and fully access learning.
Replacing Morning Work With Morning Choice
The largest positive shift in my classroom culture occurred when I replaced traditional morning work with morning choice bins. When I began our day with worksheets, it felt like I started each day with an uphill battle. The mornings began with redirecting behavior instead of building meaningful relationships.
Reducing the Cognitive Load of Math Tasks With Strategy Cards — from edutopia.org by Katherine Efremkin
When students create a visual resource to scaffold problem-solving, they can approach independent work with more confidence and focused attention.
All three of these areas of the brain need to be activated and work together in order for a student to be successful with independent math work. To help ensure that students are able to successfully shift between their problem-solving ability, thinking, and actions to attack different parts of a problem, I teach students to create strategy cards.
These cards help reduce the cognitive load, enabling students not only to become more successful and independent within their arithmetic work, but also to dive deeper into the conceptual understanding of math concepts.
On 02/02/2026,
in adjunct faculty, education, faculty/staff, homeschooling/homeschoolers, instructional design, K-12 related, learning, learning ecosystem, Learning Experience Design, learning theories, liberal arts, lifelong learning, memory, metacognition / metacognitive skills, pedagogy, productivity / tips and tricks, student-related, teachers, teaching & learning, teaching online, United States, universities, vendors,
by Daniel Christian
The Essential Retrieval Practice Handbook — from edutopia.org
Retrieval practice is one of the most effective ways to strengthen learning. Here’s a collection of our best resources to use in your classroom today.
January 29, 2026
Also see:
What is retrieval practice? — from retrievalpractice.org
When we think about learning, we typically focus on getting information into students’ heads. What if, instead, we focus on getting information out of students’ heads?
On 01/12/2026,
in business, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, creativity, digital video, education technology, emerging technologies, experimentation, Google, human-computer interaction (HCI), instructional design, learning, learning ecosystem, Learning Experience Design, learning theories, multimedia, platforms, tools, training / L&D, vendors,
by Daniel Christian
Which AI Video Tool Is Most Powerful for L&D Teams? — from by Dr. Philippa Hardman
Evaluating four popular AI video generation platforms through a learning-science lens
Happy new year! One of the biggest L&D stories of 2025 was the rise to fame among L&D teams of AI video generator tools. As we head into 2026, platforms like Colossyan, Synthesia, HeyGen, and NotebookLM’s video creation feature are firmly embedded in most L&D tech stacks. These tools promise rapid production and multi-language output at significantly reduced costs —and they deliver on a lot of that.
But something has been playing on my mind: we rarely evaluate these tools on what matters most for learning design—whether they enable us to build instructional content that actually enables learning.
So, I spent some time over the holiday digging into this question: do the AI video tools we use most in L&D create content that supports substantive learning?
To answer it, I took two decades of learning science research and translated it into a scoring rubric. Then I scored the four most popular AI video generation platforms among L&D professionals against the rubric.
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For an AI-based tool or two — as they regard higher ed — see:
5 new tools worth trying — from wondertools.substack.com by Jeremy Kaplan
YouTube to NotebookLM: Import a Whole Playlist or Channel in One Click
YouTube to NotebookLM is a remarkably useful new Chrome extension that lets you bulk-add any YouTube playlists, channels, or search results into NotebookLM. for AI-powered analysis.
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What to try
- Find or create YouTube playlists on topics of interest. Then use this extension to ingest those playlists into NotebookLM. The videos are automatically indexed, and within minutes you can create reports, slides, and infographics to enhance your learning.
- Summarize a playlist or channel with an audio or video overview. Or create quizzes, flash cards, data tables, or mind maps to explore a batch of YouTube videos. Or have a chat in NotebookLM with your favorite video channel. Check my recent post for some YouTube channels to try.
On 11/13/2025,
in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, assessment, bots, change, cloud-based computing / apps / other cloud-related, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate universities / corporate training, digital learning, education technology, emerging technologies, Europe, faculty/staff, future of higher education, game-changing environment, generational differences, global / globalization, Google, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, law schools, learner profiles, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, learning theories, liberal arts, library / librarians, lifelong learning, Microsoft, online learning, productivity / tips and tricks, skills, society, student-related, teachers, teaching & learning, tools, training / L&D, trends, UK, United States, universities, vendors, vision/possibilities, workplace,
by Daniel Christian
Gen AI Is Going Mainstream: Here’s What’s Coming Next — from joshbersin.com by Josh Bersin
I just completed nearly 60,000 miles of travel across Europe, Asia, and the Middle East meeting with hundred of companies to discuss their AI strategies. While every company’s maturity is different, one thing is clear: AI as a business tool has arrived: it’s real and the use-cases are growing.
A new survey by Wharton shows that 46% of business leaders use Gen AI daily and 80% use it weekly. And among these users, 72% are measuring ROI and 74% report a positive return. HR, by the way, is the #3 department in use cases, only slightly behind IT and Finance.
What are companies getting out of all this? Productivity. The #1 use case, by far, is what we call “stage 1” usage – individual productivity.
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From DSC:
Josh writes: “Many of our large clients are now implementing AI-native learning systems and seeing 30-40% reduction in staff with vast improvements in workforce enablement.”
While I get the appeal (and ROI) from management’s and shareholders’ perspective, this represents a growing concern for employment and people’s ability to earn a living.
And while I highly respect Josh and his work through the years, I disagree that we’re over the problems with AI and how people are using it:
Two years ago the NYT was trying to frighten us with stories of AI acting as a romance partner. Well those stories are over, and thanks to a $Trillion (literally) of capital investment in infrastructure, engineering, and power plants, this stuff is reasonably safe.
Those stories are just beginning…they’re not close to being over.
“… imagine a world where there’s no separation between learning and assessment…” — from aiedusimplified.substack.com by Lance Eaton, Ph.D. and Tawnya Means
An interview with Tawnya Means
So let’s imagine a world where there’s no separation between learning and assessment: it’s ongoing. There’s always assessment, always learning, and they’re tied together. Then we can ask: what is the role of the human in that world? What is it that AI can’t do?
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Imagine something like that in higher ed. There could be tutoring or skill-based work happening outside of class, and then relationship-based work happening inside of class, whether online, in person, or some hybrid mix.
The aspects of learning that don’t require relational context could be handled by AI, while the human parts remain intact. For example, I teach strategy and strategic management. I teach people how to talk with one another about the operation and function of a business. I can help students learn to be open to new ideas, recognize when someone pushes back out of fear of losing power, or draw from my own experience in leading a business and making future-oriented decisions.
But the technical parts such as the frameworks like SWOT analysis, the mechanics of comparing alternative viewpoints in a boardroom—those could be managed through simulations or reports that receive immediate feedback from AI. The relational aspects, the human mentoring, would still happen with me as their instructor.
Part 2 of their interview is here:
On 10/22/2025,
in Bias | Diversity Equity & Inclusion (DE&I), Daniel S. Christian, Emotion, health, learning, learning theories, metacognition / metacognitive skills, psychology, society,
by Daniel Christian
From DSC:
I love the graphic below of the Dunning-Kruger Effect:
— graphic via a teacher at one of our daughters’ schools
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The Dunning-Kruger effect is a cognitive bias where people with low ability in a task tend to overestimate their own competence, while high-ability individuals often underestimate theirs. This happens because those with low competence lack the metacognitive skills to recognize their own shortcomings, leading them to believe they are performing better than they are. Examples include a new driver who thinks they are better than average, or a novice who is confident in their ability to diagnose a medical issue based on a quick online search.
Examples in different fields
- Driving: Many drivers believe they are above average, a statistical impossibility.
- Healthcare: Patients may overestimate their ability to self-diagnose serious conditions after a quick search and disregard expert medical advice.
- Workplace: Employees may overestimate their performance compared to their colleagues.
- Social Media: The Dunning-Kruger effect can be seen online, where individuals with a superficial understanding of a topic may argue confidently with experts.