Welcome to the October 2026 edition of the Learning Research Digest.

Last month, the main theme in the published research was this: Learning improves when the learner is doing the right sort of cognitive work.

This month, the published research asks related and perhaps harder question: How do we actually know whether the “cognitive effort” that learners put in actually produces learning?

Engagement, completion, confidence, self-reported gains a perfect quiz score and a long AI conversation can all look like learning — but did learners actually learn anything?

This is the big question that the research explores in this month’s edition.

🔥 Hot Off the Research Press This Month:

This month, new research shows:

• Why “more active” does not always mean “more effective”. Students who generated their own examples spent longer on the task and felt more confident — but learned less than those who classified carefully chosen examples.

• How to make learner errors useful rather than harmful. Deliberately creating, explaining and repairing a flawed solution improved people’s ability to solve new problems more than practising the correct method or fixing somebody else’s mistakes.

• Where personalised feedback can fall short. Data-informed messages got more students to practise and encouraged them to start homework earlier, but did not improve exam results or final grades.

• Why learner self-report is weak evidence of metacognitive growth. Ninety of 94 findings relied on learners saying they had become more reflective or strategic. Almost none tested whether they could actually monitor and manage their learning more effectively.

• A better way to judge mastery than a one-off pass. One perfect practice session predicted excellent performance two weeks later, but only learners who succeeded across three separate sessions showed no significant forgetting after eight weeks.

• How to stop AI-supported design turning into prompt ping-pong. Stronger lesson plans were associated with a clear evaluate → revise → re-evaluate cycle. Weaker work became trapped in repeated clarification loops that added frustration without improving the output.

Let’s dive in!

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Welcome to the September 2026 edition of the Learning Research Digest.

Last month, we landed on one big idea: AI supports learning best when the human learner keeps ownership of the thinking.

This month, the research dives deeper into what “keeping ownership of the thinking” actually means and looks like in practice.

🔥 Hot Off the Research Press This Month:

• Two experiments challenge the age-old assumption that more time and effort means more learning. The task that took longer actually produced worse memory. What mattered was not how hard learners worked, but what kind of thinking they were doing.

• Retrieval practice may do more than help us remember — it may help us make sense of what we remember. Two weeks after a realistic learning experience, people who had actively recalled it connected it more strongly to what they already knew, without losing the detail of the original experience.

• Practising the easy parts of a complex skill may not prepare people to do the complex skill itself. Across 55 studies and 3,492 participants, practising the more complex task transferred to simpler tasks; practising the simpler tasks did not reliably transfer back.

• Feeling like you understand something is not the same as actually understanding it. Students’ judgements of their own performance were shaped not just by how well they had done, but by how capable they thought they were and how easy or difficult the task felt. Better performers were not necessarily better judges of their own performance.

• Sending people learning content is not the same as teaching them. Across five randomised trials, targeted phone tutoring plus SMS improved numeracy by 0.32 SD. SMS alone produced an effect of just 0.08 SD. The big difference was not the technology — it was checking what learners could do and adapting the teaching accordingly.

Let’s dive in!

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Welcome to the July 2026 edition of the Learning Research Digest.

For three editions we’ve been circling one question: when AI becomes part of a learning experience, what determines whether it builds capability or erodes it?

Last month, Strömberg (2026) showed that most learners, left to their own devices, use AI in ways that weaken their learning — while a smaller group uses the same technology in ways that deepen it. What we didn’t know was whether that split is simply who learners are, or whether it can be designed away.

This month we have the first strong experimental evidence that it can….

🔥 Hot Off the Research Press This Month:

• In a preregistered experiment with 1,176 students, the feedback design that most improved today’s draft finished last when learners were tested later — without the AI

• Two minutes of self-judgement before the AI speaks protected long-term learning — and protected the least experienced learners most

• Inside 120 real learner–AI conversations: replacement-mode use showed zero higher-order thinking. Zero. The full metacognitive chain appeared only in collaborator mode — just 7.5% of conversations

• Left entirely without guidance, postgraduate researchers drew their own ethical line — in exactly the place the learning science draws it: delegate the labour, keep the thinking

• Intention barely predicts whether people keep using AI. Motivational experience predicts it four times more strongly — measured by a model instructional designers have owned for nearly forty years

• 49% of education leaders call AI a strategic priority. 7% have made anyone accountable for it.
  

Let’s dive in!

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Welcome to the June 2026 edition of the digest. Last month, the field gave the central tension a name: AI optimised for task completion can undermine the cognitive processes that produce durable learning. This month, the trade-off was measured at scale — and the finding tells us where the design lever sits.

🔥 Hot Off the Research Press This Month

The AI learning penalty, measured at scale. A 26,811-student panel study with credible causal identification finds AI raises homework scores 18% and lowers monthly exam scores 20%, with high-stakes entrance exams falling 18–24% — full penalty emerging only after two years. The losses are concentrated among the ~80% of users whose behaviour matches homework outsourcing; the ~20% who maintain engagement are unaffected.

What coordinated GenAI governance in higher education looks like. A 35-person global Delphi panel across 22 countries and six continents reaches near-unanimous consensus: not policy alone, not guidelines alone, but a hybrid. The framework names eight core areas and a six-part mechanism to keep it current.

AI literacy is three things — and most programmes teach only one. A PRISMA systematic review of 206 articles finds AI literacy is technical, personal/interpersonal, and ethical/critical. The ethical/critical dimension addresses six of seven major obstacles to GenAI use. Programmes leading with prompt engineering are starting in the wrong place.

The offloading pattern in microcosm. A two-cycle design-based study with 70 Japanese undergraduates finds a GenAI-supported jigsaw method consistently produces idea expansion beyond expert materials — but high-level innovation is rare. When the team refined the design to integrate AI more deeply, human-generated ideas dropped sharply and AI-generated ideas rose.

Students like AI chatbots — but AI literacy, not tech literacy, predicts uptake. A mixed-methods study with 121 students found 80.5% rated themselves “High” or “Very High” on tech proficiency but only 15.3% rated their AI understanding “Excellent.” The intrusive pop-up alone dominated non-user complaints.

Prompting can be taught — but only in simulation, so far. ARPG+ is a real-time coaching system grounded in cognitive load theory and the zone of proximal development. It produced a 143% improvement in prompt quality and 91% learner independence in late-session turns. All evaluations are on LLM-based simulated learners; classroom validation is named as the necessary next step.

Why agentic AI needs designed-in friction. A conceptual paper from a Kyoto-based team reviews six pedagogical principles through the lens of proactive AI agents and proposes designed-in friction, dynamic fading, a Teacher-in-the-Loop architecture, and AI usage restraint as the design countermeasures.

Here’s the full breakdown and the practical implications for your work:

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Welcome to the May edition of the Learning Research Digest — my monthly round-up of the most useful peer-reviewed and pre-print research on AI and learning published in the last 30 days.

Last month, six papers converged on a paradox the field is increasingly forced to take seriously: AI is making students better at completing tasks while leaving them worse at learning.

This month, Khosravi and colleagues have given that tension a name — the learning–performance paradox — and proposed a framework for designing AI that resolves it. The five empirical studies that follow are field tests of what happens when AI is — or is not — deliberately designed for learning rather than performance.

One note before we dive in: the anchor paper this month is an arXiv preprint, not (yet) a peer-reviewed journal article. I’ve included it because the author lineup (Khosravi, Gašević, Baker, Buckingham Shum, DiCerbo, Denny, Lodge) is essentially the senior bench of the AI-in-education field, and because the framework genuinely changes how the rest of the month’s empirical work hangs together.

🔥 Hot off the research press

This month’s papers, in plain English:

• The learning–performance paradox. AI optimised for task completion can actively undermine the processes that produce durable learning. Khosravi et al. argue we need a different class of AI — learning companions — and offer a three-foundation framework, illustrated through five real-world case studies.

• Students trusted GenAI feedback more than tutor feedback — and improved their learning strategies because of it. In a blinded 9-week study, students rated GenAI feedback higher on “genuineness,” and the GenAI group improved on the Task Strategies dimension of self-regulated learning.

• A built-in journaling course raised intrinsic motivation. The AI assistant did not — but it raised engagement over time. A 22-day field experiment with 179 students showed the structured course did the motivational work; the AI assistant sustained behavioural engagement through a feedback loop.

• AI’s effect on engagement runs through Community of Inquiry — and digital literacy decides whether it works. A mixed-methods study across nine Chinese universities (N=400) showed digital literacy positively moderates every pathway between AI use and engagement.

• Cultural framing alone won’t drive AI adoption. A Sub-Saharan African study found personal innovativeness and individualism-collectivism predicted AI use — but power distance, uncertainty avoidance, and indulgence had no effect.

• In a 6-week RCT on creative work, AI’s effect ran through instructional coherence — not cognitive load. A randomised experiment with 120 visual design students found expert-rated work was significantly better in the AI group, but the cognitive load pathways theory predicted weren’t significant. Instructional alignment was.

Let’s dive in!

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Welcome to the March-April 2026 edition of the digest. Research published in the last month has converged on a question that’s deeply practical and urgent:

When AI becomes part of the learning experience, what determines whether it deepens learning — or quietly undermines it?

🔥 Hot Off the Research Press This Month

• AI scaffolds performance but may not build competence. A decade of meta-analytic evidence reveals a troubling split: learners do well when learning with AI, but then struggle without it.

• AI feedback drives motivation more than cognition. A 40-study meta-analysis finds personalised AI feedback has a far stronger effect on motivation (g = 0.82) than on learning outcomes (g = 0.58).

• Game design works for learning — and the evidence is now pretty definitive. A meta-analysis of 193 studies finds that learning designed as a game produces large effects (d = 0.87). Adding game elements to existing instruction is slightly less effective but still strong (d = 0.75).

• GenAI makes virtual patients unpredictable — and that’s a feature. Nursing students built communication skills by navigating AI-driven conversation variability. At the same time, “robotic voices” negatively impacted the empathy that clinical practice demands.

• Offloading to AI can deepen learning — past a certain threshold. A study of 912 students across three countries found that delegating enough cognitive work to AI frees up mental capacity for higher-order reflection. Small amounts of offloading don’t help, but substantial offloading does.

• Giving students a prompt template changes how they use GenAI. When students used structured prompts to guide their AI interactions during peer review, their feedback became more specific, more explanatory, and better at identifying weaknesses — beating both free-form AI use and peer-only feedback.

Here’s the full breakdown and the practical implications for your work:

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This month’s research lands squarely on a question many of us are now asking in practice:

If AI is becoming part of the learning ecosystem, what does good instructional design look like—both with and without AI in the loop?

🔥 Hot Off the Research Press This Month

• Model‑based AI integration pays off. AI‑enhanced BOPPPS - a structured, six‑phase lesson‑planning and teaching model used to design active, outcomes‑aligned classes - boosts self‑directed learning and satisfaction, not just scores.

• Interface design is learning design. Visual feedback and gamified elements can either scaffold maths reasoning or derail it.

• Fluency gaps persist without consistent practice. Reading and maths programmes show big gains only when practices are systematic and multi‑component.

• Core strategies still matter more than tools. Differentiated, cooperative and explicit instruction remain powerful levers in early years.

• Design‑based learning offers a blueprint for agency. Hands‑on, problem‑centred projects build deep understanding and higher‑order skills.

• What is instructional design, really? A new systematic review revisits the field’s foundational definitions—and finds a persistent gap between theory and practice.

• Educational design research develops teachers, not just learners. A major new review shows EDR reshapes how teachers think and teach, not just what students do.

Threading through it all this month is a question the field is only beginning to ask seriously: how do we design the conditions in which teachers can use these tools well?

Let’s dive in 🚀

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Happy new year, and welcome to Volume 38 of the Learning Research Digest. It’s been another exceptional month of research on instructional design.

🔥 Hot Off the Research Press This Month

• Theory sequencing beats intuition. Deliberately ordering learning theories at individual, group, and systems levels reliably improves learning outcomes.

• Interleaving works better than blocking. Mixing problem types drives transfer; module-by-module structure doesn’t—especially for long-term retention.

• Design scaffolding > realism. How you scaffold learning matters far more than fidelity in simulation environments.

• AI Tool choice is critical. Some widely used AI tools actively undermine critical thinking—reflective selection is essential.

• Designing assessments with AI is systematic. Prompt engineering can be learned and applied through a five-phase process.

Let’s dive in 🚀

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Welcome to the December 2025 edition of the Learning Research Digest.

This month’s theme is clear: enhancing the quality of the learning process with AI. As generative AI becomes ubiquitous, the big question for most researchers isn’t whether or not to use it—it’s how we build and use it.

Across higher education, vocational training and medical education, the most recent research in the field shows a new preoccupation with exploring if and how AI can image learning, rather than the production of educational content.

🔥 Hot Off the Research Press This Month:

• Collaboration Vs Delegation: Postgraduate writers learn more when GenAI is a dialogic partner, not a ghostwriter.

• A study of AI users in Medical schools in China shows that they are wrestling with LLMs that are clinically smart but still pedagogically and ethically fragile.

• A two-year cross-disciplinary study shows students like ChatGPT—but still trust humans more when it comes to establishing nuance and complexity in their understanding.

• A study that shows that using GenAI in vocational education boosts relevance at first—but drifts into “metacognitive laziness” unless rigorous reflection is deliberately designed in.

• A review of DeepSeek, GPT and Gemini confirms that when AI has pedagogical smarts in-built, everyone wins (and when it doesn’t no one gains much).

Let’s dive in 🚀

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Welcome to the November 2025 edition of the Learning Research Digest. The theme of research published this month is clear: AI meets intentional design. We’re seeing practitioner wisdom from the front lines, lessons in co-creation and inclusion, and evidence tied to real-world examples.

🔥 Hot Off the Research Press This Month

• Researchers propose a blueprint for mapping GenAI across entire programmes—moving from “police AI use” to “plan for it.”

• A study of 49 secondary students reveals how motivational pedagogical agents influence choice-making and self-regulated learning in an adaptive algebra system—with surprising differences based on prior knowledge.

• A systematic review of 45 studies confirms that when AI fits the pedagogy (not just the platform), everyone wins—but universal access and ethics are non-negotiables.

• A digital ethnography shows what happens when design is “in the open,” with rapid prototyping, community critique and shared wins and failures.

• A practitioner’s perspective from the ground reveals what actually works in digital/blended design—co-design, accessibility, and reflective AI use.

Let’s dive in 🚀

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Welcome to the October 2025 edition of the Learning Research Digest. The theme of research published this month is clear: exploring if (and how) people + technology work well together to improve the quality and impact of learning experiences.

🔥 Hot Off the Research Press This Month

• Researchers find that updating Vygotsky’s ZPD methods for AI-era drive learner stretch and achievement.

• A team of engineers show that deep learning course recommender engines outperform standard methods (incl. Coursera’s) but only when significant effort is put into ensuring that metadata is clean and structured.

• Classroom trials indicate that augmenting the 5E-model with AI leads to higher quality instructional design work than a teacher working alone.

• A new editorial piece warns that too many AI tools skip pedagogy and real-classroom testing—leading to a potential crisis in learning quality.

• A review of 98 studies on instructional designer practices (1990–2023) points to endemic problems when it comes to problem framing/needs analysis and recommends the introduction of more heuristics, playbooks and specialised roles.

Let’s dive in 🚀

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Welcome to Volume 34 of the Learning Research Digest. It’s been another bumper month of research on instructional design, with continued focus on the impact of AI on the speed and quality of our work and our ethical responsibilities in an increasingly data-driven & AI-centred profession.

🔥 Hot Off the Research Press This Month

• Researchers show that using GenAI (specifically, GPT4) can boost the quality of novices’ micro-lesson design—but only for certain types of tasks.

• A research study shows how an AI assistant, LeDA, helped educators generate creative ideas for activities but failed to add value for more more practical tasks, like writing learning outcomes.

• An article which reimagines ADDIE for the AI age by introducing a new model and process, ADGIE, which allocates roles and responsibilities to both humans and machines.

• A systematic review that maps the current AI-ed risk landscape and warns that without intentional, human-centred design, “personalisation” can devolve into homogenised and stereotyped learning.

• A meta-analysis which shows that VR-based teacher training has huge potential to drive mastery, especially among novice teachers.

• A research project which shows huge potential for a new model of blended learning which combines human contact with AI tutoring to scale the support and feedback needed to drive learning outcomes.

Let’s dive in 🚀

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Welcome to the July 2025 edition of the Learning Research Digest.

This month, research is leaning into the lived reality of AI-powered instructional design. Across research journals, the enduring questions persist: how do we centre humanity in a world of automation? What does ethical, transparent, and impactful learning design look like in practice—not just in theory?

The latest research brings both practical answers and fresh provocations.

🔥 Hot Off the Research Press This Month:

• AI in Professional Learning: Recent studies show AI is transforming workplace learning by enabling deeply personalised and just-in-time training, supporting knowledge transfer, and reducing bias through data-driven assignments. Yet, the limits of current AI—especially in reading and supporting motivation and emotion—pose strategic questions about where humans remain essential in design and facilitation.

• Embedding Human Values, Not Just Technology: Instructional designers are moving from theory to practice by explicitly weaving care, empathy, inclusivity, and accessibility into all phases of course design. Research highlights how value-sensitive instructional choices now show up in concrete actions: diversified assessments, representative visuals, and intentional accessibility features.

• University-Level AI Integration: Promise and Pitfalls: Model-based, full-course AI integration can greatly amplify feedback and accelerate technical learning in higher education. However, it brings tradeoffs—such as student passivity, over-reliance on AI, and risks to creative or independent problem solving—surfacing the need for consistent human oversight and targeted supports for learner agency.

• Facilitation Matters in Hands-on Skill Workshops: Evidence from short-duration e-textile workshops underscores that intentionally structured guidance—not just open-ended making—drives both knowledge and skills acquisition among novices. Facilitators’ real-time support and scaffolding enable learners to overcome material challenges and increase their success, but creativity can plateau if workshops over-emphasise replication of exemplars.

• Grand Challenges and the Role of Instructional Design: Leaders in the field urge a strategic pivot toward tackling grand, societal problems (like literacy or digital equity) using coordinated, cross-sectoral approaches instead of simply focusing on the next shiny technology. The call: use instructional design’s power for systemic, measurable, global impact.

Let’s go 🚀

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Welcome to the June 2025 edition of the Learning Research Digest!

This month's research tackles the big questions: How do we design learning that sticks? What happens when AI meets human creativity? And how do we build truly collaborative STEM experiences that actually work?

🔥 Hot Off the Research Press This Month:

• A study that shows how AI-supported practice can reduce forgetting by 40%

• Researchers develop a new 25-task checklist transforms collaborative STEM design

• Systematic review of 64 children's AI tools reveals dangerous ethics gap

• Mixed reality both validates & refines classic cognitive load principles

Let's dive in 🚀

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Welcome to the May 2025 edition of the Learning Research Digest.

This month, the research spotlight shines brightly on the evolution of AI-assisted instructional design, collaborative learning models, and the ethics of automation in education. As large language models, multi-agent systems, and analytics tools become embedded in our workflows, researchers are asking: What remains uniquely human in learning design? How do we scaffold ethical, transparent, and learner-centred design — not just faster processes?

The research we feature this month invites us to reflect deeply on these questions — and offers practical, evidence-based answers from classrooms, labs, and design studios alike.

🔥 Hot Off the Research Press This Month:

• AI-powered chatbots improve design thinking when scaffolded using the 5E model.

• A six-stage model helps teachers collaboratively design interdisciplinary STEM learning.

• Human-centred analytics and AI tools need stronger pedagogical grounding and evaluation.

• A new framework uses Bloom’s Taxonomy to guide transparent, AI-assisted design.

• Instructional designers share how they’re using — and questioning — ChatGPT.

In This Month’s Edition You Will Find:

✨ The 5E chatbot method that transforms generic AI into your pedagogical partner (without the hallucinations)

🎯 The transparency framework top designers use to maintain control while leveraging AI's power

📊 The three designer archetypes—Pessimistic Evaluators, Optimistic Advocates, and Wary Thinkers (which one are you?)

🔧 A new 6-stage collaborative model that's revolutionising ADDIE

⚠️ The design gaps that make 70% of AI education tools fail in real classrooms (and how to avoid them!)

Let’s dive in 🚀

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Welcome to the April 2025 edition of the Learning Research Digest!

This month, the research spotlight shines on learning design in the age of AI, creativity in education, instructional redesign for real-world impact, and the urgent need for deeper, theory-driven practice.

🔥 Hot Off the Research Press This Month:

• Redesigning learning protocols using AI, concept mapping and multimodal reasoning.

• Rethinking construction safety training through learning theory and design.

• Moving beyond comparison: why instructional research must explain, not just compare.

• Problem posing as mathematical modelling in expert instructional design.

• Inquiry-based lab redesigns that boost engineering students' critical thinking.

• PBL and PjBL transforming motivation and outcomes in graphic engineering.

• Vygotsky’s creativity theory applied to 21st-century technology-enhanced learning.

• Human-centered AI: the ethical future of learning design and technology.

Let’s dive in 🚀

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Welcome to the March 2025 edition of the Learning Research Digest — and what a month it’s been!

Across disciplines and continents, this month’s research shares a clear theme: learning design as both science and craft. From value-sensitive course creation to AI-simulated learners and VR-powered teacher training, these studies explore what it means to design learning experiences that are ethical, evidence-informed, and future-ready.

🔥 Hot Off the Research Press This Month:

• Designing Simulation for Better Patient Outcomes – What instructional design features in anaesthesia training actually lead to behaviour change and clinical impact?

• VR-Based Teacher Training – What design elements make immersive environments effective for professional learning?

• Values in Instructional Design Practice – How do designers’ personal ethics shape what (and how) they build?

• MOOC Satisfaction Decoded – What really matters to learners using platforms like Coursera?

• What IDs Actually Do (and What They Don’t) – A closer look at how higher ed designers navigate real-world constraints.

• AI-Powered Simulated Learners – Can AI models predict the outcomes of instructional design decisions?

• AI Assessment in Language Learning – What kinds of feedback, tools, and timeframes drive learning in K–12?

• Designing for Motivational Regulation – How can learning environments actively support students’ ability to stay engaged?

Let’s dive in 🚀

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Welcome to the February 2025 edition of the Learning Science Digest!

It’s been a busy month for education — let’s jump right in:

🔥 Hot Off the Research Press This Month:

• AI-Infused Learning Design – A structured approach to AI-enhanced assignments using a three-step model for AI integration.

• Mathematical Dance and Creativity in STEAM – Using AI-powered motion capture to translate dance movements into mathematical models.

• AI-Generated Instructional Videos – How adaptive AI-powered video learning enhances problem-solving and knowledge retention.

• Immersive Language Learning with XR & AI – A new framework for integrating AI-driven conversational agents with Extended Reality (XR) for task-based language learning.

• Decision-Making in Learning Design – A scoping review on how instructional designers navigate complex instructional choices and make data-driven decisions.

• Interactive E-Books and Engagement – Examining the impact of interactive digital books on student motivation, comprehension, and cognitive engagement.

• Elevating Practitioner Voices in Instructional Design – A new initiative to amplify instructional designers’ contributions to research and innovation.

Let’s go! 🚀

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Happy New Year, and welcome to the January 2025 edition of the Learning Science Digest.

This month, our featured research highlights the increasingly dynamic interplay between artificial intelligence, instructional design and inclusive education.

🔥 Hot Off the Research Press This Month:

• ChatGPT as a Learning Partner: Explore how ChatGPT, used as a teachable agent, is revolutionising programming education by enhancing student engagement, self-regulated learning, and understanding of complex concepts.

• Social Justice in Medical Education: Discover Brazil’s transformative "Pedagogy of Connection," an innovative framework integrating healthcare, equity, and education to prepare socially responsive medical professionals.

• Career Growth in Instructional Design: Delve into the career journeys of instructional designers, examining how Action Regulation Theory provides a roadmap for achieving professional goals and navigating challenges.

• Simulation in Emergency Medicine: See how Gagné’s instructional design principles are improving learning outcomes in obstetric emergency simulations, from enhanced knowledge retention to structured feedback loops.

Let’s go!

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Welcome to the November 2024 edition of the Learning Science Digest. This month we've seen a bumper amount of research produced specifically on the topic of AI. This research reflects growing experimentation with AI and presents compelling evidence for both the transformative potential and essential human elements in AI-enhanced education.

🔥 Hot Off the Research Press This Month

Enhancing AI-Augmented Classrooms: A comprehensive analysis of how teachers can effectively integrate Intelligent Tutoring Systems while maintaining their crucial instructional role.

An Exploratory Study of Elementary School Teachers' AI Competencies: Insights into essential AI skills and behaviors for K-12 educators based on real classroom experiences.

Investigating the Effect of Role-Play Activity With GenAI Agent: A quasi-experimental study examining AI-powered speaking practice in EFL contexts.

Enhancing Instructional Design with AI: A comparative analysis of 5E model-informed AI chatbot scaffolding versus traditional teaching approaches.

Preparing Educators to Teach and Create With Generative AI: Introduction of the SAIL framework for developing teacher AI literacy through hands-on experience.

Redefining Teaching in the Era of Generative AI: A 360-degree examination of how GenAI is transforming traditional educational approaches.

Let's go! 🚀

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