Pragmatic AI Solutions

I've just encountered one of the most important theoretical breakthrough in AI-assisted writing of 2025: Luciano Floridi's "distant writing" framework finally helps us appreciate AI writing as the sophisticated creative act it truly is. https://lnkd.in/eEhA4ftS For too long, we've failed to develop adequate theory for understanding how humans and LLMs collaborate in literary production. Floridi's framework brilliantly reconceptualizes the human as a "meta-author" - a narrative designer who retains creative control while leveraging AI's generative capabilities. What makes this framework revolutionary is how it transforms our understanding of narrative itself. Floridi introduces the concept of "isotropy of narrative space" where any direction in storytelling is equally workable if coherence is maintained - enabling entirely new literary forms previously impossible. The breakthrough lies in recognizing that distant writing represents an evolution of authorship, not its replacement. It positions the human as responsible designer rather than direct text producer - similar to how architects design buildings they never physically construct. The framework identifies seven distinct stages in the distant writing process that completely reshape creative production: Conception and Development - determining narrative vision Requirements Formulation - establishing constraints and parameters Prompt Engineering - crafting precise instructions WrAIting - LLM-based text generation Progressive Refinement - iterative improvement Validation and Verification - ensuring coherence Curation and Assembly - selecting and arranging content Perhaps most fascinating is how Floridi positions this shift within his "fourth revolution" - the Turing revolution that displaces humans from the center of the infosphere, reconfiguring our relationship with information production entirely. For educators, publishers, and writers navigating this new landscape, this framework isn't just theoretical - it provides practical insights into how literary production, pedagogy, and criticism must evolve to embrace the expanded creative possibilities of distant writing. The future belongs to those who understand that writing is becoming a design discipline - and Floridi's framework gives us the vocabulary we need to fully appreciate its complexity. #DistantWriting #AICreativity #MetaAuthorship #FourthRevolution #WritingTheory Amanda Bickerstaff Jessica L. Parker, Ed.D. Kimberly Pace Becker, Ph.D. Armand Ruci M.A, M.Ed Mike Kentz David H. Nigel P. Daly, PhD 戴 禮 Michael Woudenberg Michael Spencer Phillip Alcock Thom Markham, Ph.D.

This week at Educating AI, we highlight Nigel P. Daly, PhD 戴 禮 's fascinating guest article "From work to flow: How talking to AI Is changing the way we work, think, and feel." His research reveals surprising emotional and cognitive benefits of AI collaboration. https://lnkd.in/e9Bp3bxF Dr. Daly examines a recent Harvard Business School study—"The Cybernetic Teammate" (Dell'Acqua et al., 2025)—which found AI collaboration isn't just boosting productivity, but fundamentally changing how people feel about their work. Among 776 Procter & Gamble professionals: 15-18% performance improvements with generative AI 20% increase in positive emotions including confidence and engagement Individual AI users outperformed human-only teams AI-augmented teams saw the biggest gains in both performance and satisfaction Dr. Daly connects these findings to two powerful models: Maslow's Hierarchy: AI collaboration provides safety (judgment-free space), belonging (conversational exchanges), esteem (more polished work), and self-actualization (expanded creative exploration). Bloom's Taxonomy: AI scaffolds thinking across cognitive functions, acting as a curiosity driver that enhances learning while enabling non-experts to perform complex tasks. Most compelling is how AI helps users achieve what psychologist Mihaly Csikszentmihalyi called "flow states"—that optimal balance between challenge and skill. AI creates a "wider flow window" by: Adjusting the challenge/skill balance Providing immediate feedback Maintaining user control and direction Reducing both anxiety and boredom Despite these benefits, significant gaps exist: 47% of Gen Z employees use AI weekly, but half receive no training 91% of employees use AI at work, yet only 13% of organizations have implemented multiple use cases Dr. Daly offers three key recommendations: Train teams, not just individuals to develop collaborative AI workflows Teach metacognitive strategies to maintain critical thinking with AI Establish balance-promoting rituals including workspace organization, structured prompting routines, and analog practices As we navigate this AI-integrated future, the goal isn't just technical literacy but true "AI fluency"—moving from isolated skills to embodied practice that allows AI to become a co-agent in deeper learning and growth. Join the Conversation How has AI changed your emotional relationship with work? What practices help you maintain cognitive balance when using AI? Has AI helped you achieve flow states? Mike Kentz Amanda Bickerstaff Alfonso Mendoza Jr., M.Ed. Dr. Nisha Kanabar PT France Q. Hoang Vriti Saraf Jessica L. Parker, Ed.D.

📢 New guest post dropping Monday morning → Why working with AI feels better (and what it means for how we work) AI doesn’t just make us faster — it’s changing how we feel about work itself. In an extraordinary new guest post for Educating AI, Nigel P. Daly, PhD 戴 禮 dives deep into how AI acts as a "cognitive co-pilot," reshaping workflows and boosting emotional well-being. Drawing on a major 2025 Harvard Business School study, Daly shows how AI not only improves productivity but also sparks curiosity, confidence, and flow — and why that matters for everyone from junior employees to senior leaders. But this shift isn’t without risks. Nigel also tackles the harder questions: ⚡ Can AI make us too dependent? ⚡ How do we avoid letting it become the "smartest voice in the room"? ⚡ What kind of AI fluency training do teams need to truly thrive without losing human judgment? This is one of the most thoughtful, research-backed pieces we’ve published — and it’s arriving Monday morning (May 5) via Educating AI. → Subscribe now so you don’t miss it: https://lnkd.in/eY3d5HXb #AI #GenerativeAI #FutureOfWork #Leadership #LearningAndDevelopment #AIFluency #EmotionalIntelligence #CognitiveScience

I've spent the past quarter mapping the development of what I've begun calling "AI-adaptive agency" in K-12 classrooms, and I need to share something profound: we've been thinking about AI integration in education all wrong. The most transformative classrooms I've studied aren't simply teaching students to use AI responsibly - they're systematically developing students' capacity to design their own AI-enhanced learning ecosystems. This distinction is critical for understanding genuine educational transformation in the age of generative AI. Let me share a pattern I observed across multiple high-performing environments: In these spaces, the focus shifts from content mastery to learning architecture. Students don't just use AI to complete tasks; they actively construct frameworks for AI-assisted knowledge acquisition, evaluation, and application. One student I interviewed described it perfectly: "Before, I was just using AI to write essays faster. Now I'm designing AI dialogues that help me truly understand complex ideas and connect them to other knowledge." What's fascinating is how this capacity develops across three distinct dimensions: AI-contextual navigation - students learn to design effective AI interactions that extend their thinking Strategic co-creation - they develop personalized systems for AI-enhanced knowledge construction Reflective partnership - they continuously evaluate and refine how AI fits into their learning ecology Each dimension builds upon the others in a recursive cycle. As students develop these capabilities, something remarkable happens: AI becomes simultaneously more powerful and more properly contextualized. The false dichotomy between AI dependence and AI avoidance dissolves. This suggests we need to reimagine our concept of AI scaffolding. Perhaps effective AI integration isn't about restricting access, but about progressively transferring design authority to the learner in structured ways. The implications for educational AI policy are significant. Our most pressing challenge isn't controlling what AI students can access, but engineering environments where students develop the capacity to navigate AI-enhanced knowledge landscapes with increasing sophistication. These early discoveries challenge us to think beyond basic AI literacy toward something more fundamental: the systematic development of AI learning agency. #AILearningDesign #StudentAgency #EducationalAI #CognitivePartnership #FutureOfLearning Aco Momcilovic Mark Laurence Jason Gulya Thom Markham, Ph.D. Phillip Alcock Mike Kentz David H. Jessica Maddry, M.EdLT

What if everything we think we know about academic dishonesty is backwards? After reading "The Opposite of Cheating: Teaching for Integrity in the Age of AI" by Tricia Bertram Gallant and David A. Rettinger, I'm convinced we need to fundamentally rethink our approach to integrity in education. Rather than focusing solely on detection and punishment, the authors reframe academic integrity as a positive educational outcome that can be actively cultivated. Their framework is built on ten principles that challenge conventional thinking: 1. Cheating is a natural and normal human behavior. 2. Preventing and responding to cheating is an extension of one's role as an educator. 3. Knowledge is constructed, not received. 4. Students learn from mistakes, errors, and failures. 5. K-20 has moral obligation to graduate ethical citizens. 6. Students' decision to cheat is caused by internal and external factors. 7. Students do not enter higher ed with fixed moral mindsets or intellectual abilities. 8. Cheating by itself is not the problem to be fixed, but a symptom of a number of other problems: pedagogy, culture, mismatch in expectation. 9. Cheating behavior constantly evolves with changes in technology. 10. Strategies for reducing cheating and enhancing learning should be multi-purposed. What resonated most with me is their emphasis that knowledge is constructed rather than simply received, and that learning happens through mistakes and failures. The acknowledgment that cheating isn't the core problem but rather a symptom of issues in pedagogy, culture, or mismatched expectations feels especially relevant as AI tools reshape how we think about authorship and originality. For those wrestling with how to maintain academic standards while embracing technological change, this book offers practical strategies that simultaneously reduce cheating and enhance learning. What approaches have you found effective in promoting academic integrity in your teaching or learning environments? Alfonso Mendoza Jr., M.Ed. Amanda Bickerstaff Aman Kumar Mark Hammond Aco Momcilovic Marc Watkins Jason Gulya Lance Eaton, PhD Dr. Lance Cummings Mike Kentz Jessica L. Parker, Ed.D. Kimberly Pace Becker, Ph.D. David H. Vriti Saraf Jeanne Beatrix Law, PhD

AI optimizes for speed. Education must optimize for struggle. In a world of instant answers, the deepest learning still happens in moments of cognitive friction. Students who wrestle with ideas build neural pathways that AI-only workflows can't replicate. The Toggle Method's foundation rests on these non-negotiable human phases: 1. Raw Cognitive Drafting (AI-Free) What's Protected: Initial hypothesis formation, original synthesis Implementation:Handwritten "messy thinking" journals Ungraded error-forging sessions ("Fail First" math problems) Analog source triangulation (books + print articles + interviews) 2. AI as Provocateur What's Enhanced: Perspective expansion, bias testing Implementation:"Devil's Advocate" prompts: "Challenge my thesis using 3 logical fallacies" Cognitive mirror exercises: Have AI generate variations of student work Counterfactual scenarios: "Rewrite this history essay assuming different outcomes" 3. Verification Sprints What's Developed: Discernment, intellectual ownership Implementation:"Source Autopsies:" Evaluate AI-generated citations for credibility Line-item veto exercises: Students must eliminate 30% of AI content with rationale Live defense panels: "Demonstrate this conclusion is authentically yours" Evidence-Based Impact: English students using verification sprints demonstrated 2.3x more substantive revisions to AI outputs than control groups (Harvard GSE observational study). When we embrace productive struggle alongside technological efficiency, we develop thinkers who can navigate both human and artificial intelligence landscapes with confidence. #CriticalThinking #EdTech #CognitiveScience Michael Spencer Michael Woudenberg Richard Andrew Aman Kumar Sam Bobo Nneka J. McGee, J.D., Ed.D. Christine Zanchi Mike Kentz David H.

🧠 "Smooth learning isn't effective learning. Brains need friction to grow." Remember learning to ride a bike? The wobbles, falls, and frustration were essential—not obstacles to overcome, but the very process that wired your brain for mastery. Neuroscience confirms what educators have long suspected: cognitive struggle isn't just valuable—it's necessary for building robust neural connections. AI's seductive efficiency threatens to eliminate the productive friction required for deep critical thinking – the cognitive resistance that forges neural pathways. When we outsource our thinking struggles, we deprive our brains of the necessary resistance that builds intellectual muscle. It's like installing an escalator on our mental staircase—convenient, but eliminating the very effort that strengthens us. Enter the Toggle Method: a deliberate alternation between AI-assisted and independent thinking that strategically introduces necessary cognitive struggle. This isn't about rejecting AI's benefits—it's about designing the optimal balance between frictionless assistance and essential mental effort. Why Toggle Works: Cognitive Mirroring: Contrasting human/AI outputs reveals blind spots in reasoning Skill Preservation: AI-free zones protect foundational analytical abilities Meta-Awareness: Students consciously recognize what they're offloading versus internalizing Classroom Application: 📝 Research Paper Toggle Friction Phase: Students manually source and annotate 5 primary references AI Phase: Leverage GPT-4 to generate 10 additional potential sources Integration: Students defend which 3 sources they'd incorporate and why (AI's or theirs) As one teacher told me, "The best moments in my classroom aren't when things are easy—they're when I see students struggling productively through a problem they once thought impossible." Tomorrow: The 3-part toggle framework every educator needs. #CognitiveResilience #ToggleTeaching #AIinEducation Pragmatic AI Solutions Mike Kentz Dr. Sabba Quidwai Mark Lawrence Marc Watkins Jason Gulya David H. Jessica L. Parker, Ed.D. Kimberly Pace Becker, Ph.D. Phillip Alcock Thom Markham, Ph.D.

Last week, a colleague asked: "How can I assess student writing when I don't know if they wrote it themselves?" My response: "What if they defined the assessment criteria themselves?" This semester, I've experimented with student-defined outcomes for major projects. Rather than providing a standard rubric, I've asked students to develop their own success criteria within broad learning goals. The results have transformed not just assessment, but the entire student relationship with AI tools. Maya*, the student developing a denim brand market study, created assessment categories that included "market insight originality," "data visualization effectiveness," and "authentic brand voice development." These self-defined criteria became guiding principles – and completely changed her approach to using AI. "I catch myself asking better questions now," she told me. "Instead of 'help me write this section,' I'm asking 'does this analysis seem original compared to standard market reports?'" This highlights the "assessment ownership effect" – when students help create the criteria for quality, they develop internal standards that guide both their work and their AI interactions. I've documented four key benefits of this co-created assessment approach: Metacognitive Development: Students must reflect on what constitutes quality Intrinsic Motivation: Self-defined standards create stronger investment Selective AI Usage: Students use AI more thoughtfully to meet specific quality dimensions Authentic Evaluation: Discussions shift from "did you do this yourself?" to "does this meet our standards?" When students merely follow teacher-defined rubrics, AI can become a tool for compliance. When they define quality themselves, AI becomes a thought partner in achieving standards they genuinely value. Implementing this approach means starting with broader learning outcomes and then guiding students to define specific success indicators. It requires trust that students, when given responsibility, will often exceed our expectations. What assignment might you reimagine by inviting students to co-create the assessment criteria? *Name changed #AssessmentInnovation #StudentAgency #AILiteracy #AuthenticLearning Pragmatic AI Solutions Alfonso Mendoza Jr., M.Ed. Polina Sapunova Sabrina Ramonov 🍄Thomas Hummel France Q. Hoang Pat Yongpradit Aman Kumar Mike Kentz Phillip Alcock

In my AI Theory & Composition class, we’ve just launched a real-world, authentic research and writing project—at the request of our admin team—exploring how students and teachers are currently using AI in academic contexts. But we’re going deeper. Our students quickly identified a need to examine the impact of AI on critical thinking, diving into concepts like cognitive offloading, automation of thought, and what it means to think with or through AI. To support their inquiry, I built a custom-trained chatbot that creates a more immersive research experience. It’s trained on a diverse and thought-provoking set of sources, including: 📘 The Efficiency-Accountability Tradeoff in AI Integration – Nicolas Spatola 📘 Lazy Brain Syndrome – Terry Underwood 📘 The Impact of Generative AI on Critical Thinking – Lee, Sarkar, Tankelevitch, et al. (Microsoft Research) 📘 To Think or Not to Think: The Impact of AI on Critical-Thinking Skills – Christine Anne Royce & Valerie Bennett 📘 Enhancing Learners’ Critical Thinking Skills with AI-Assisted Technology – Vinod Aithal & Jasmin Silver Chatbot link here: https://lnkd.in/eWc7AnS9 The students are already loving the experience of designing their workflows, asking big questions, and confronting AI’s dual role as both a tool and a potential shortcut. 📌 If you’re researching similar questions—or supporting student inquiry into AI—feel free to reach out or try out the chatbot. Happy to share. Let’s keep pushing the conversation forward. #AIinEducation #CriticalThinking #StudentResearch #CognitiveOffloading #AutomationOfThought #AIandLearning #GenerativeAI #ChatbotAsResearchTool #EduInnovation Amanda Bickerstaff Mike Kentz Mark Laurence Aco Momcilovic David H.Sam Bobo Brenda Trimble, M.Ed. Jason Gulya Richard Andrew

This semester, I've been conducting a quiet experiment: deliberately transferring the authority to define learning outcomes to my students. The results have been revelatory. While many educators see AI as a threat to academic integrity, I've witnessed something more significant: when students define their own learning pathways, their relationship with AI transforms from potential shortcut to powerful collaborator. Take Maya*, one of my most engaged students. She's researching luxury consumption in fashion markets and developing a market study for a sustainable denim brand she's conceptualizing. Most importantly, she crafted her own project outcomes and assessment criteria. What's fascinating is how this transfer of authority changed her relationship with AI: Before: AI was primarily a way to generate content that matched teacher expectations After: AI became a thought partner helping her explore possibilities she defined I observed her use BoodleBox to explore market positioning strategies, critically evaluate each response, and synthesize her own approach that differed from any AI suggestion. The AI didn't replace her thinking – it amplified it by expanding the possibility space. This pattern has repeated across my classrooms: when students own the definition of quality, their use of AI shifts from outsourcing to augmentation. They develop what I call "outcome ownership" – the ability to define meaningful endpoints and assess their own progress. For educators concerned about AI's impact, I suggest this counterintuitive approach: transfer more authority to students, not less. Let them define project outcomes within meaningful guardrails. The resulting ownership transforms AI from threat to asset. Perhaps the most powerful question isn't "How do we prevent AI misuse?" but "How might AI help us create space for authentic student agency?" What small experiments in authority transfer might you try in your classroom? *Name changed for privacy #StudentAgency #AILiteracy #AuthenticLearning #EducationalInnovation Mike Kentz Vriti Saraf Amanda Bickerstaff Dr. Lance Cummings Armand Ruci M.A, M.Ed Alfonso Mendoza Jr., M.Ed. Aman Kumar Scott Sommers, PhD Nigel P. Daly, PhD 戴 禮 Phillip Alcock Jessica Maddry, M.EdLT