For decades, we’ve known what “good teaching” looks like: timely formative feedback, targeted reteaching, scaffolded guided practice, and classrooms designed around Universal Design for Learning (UDL)—with multiple means of engagement, representation, and expression. We’ve also known why these practices are hard to sustain: they require time, diagnostic acuity, and continuous adaptation that few faculty can deliver at scale without heroic effort. Artificial intelligence changes this calculus. Done right, AI is not about replacing instructors; it’s about operationalizing best practice with consistency, speed, and equity we’ve never been able to reach system wide. Done poorly, it automates malpractice, accelerating credentialism without learning.
Why AI Is Different This Time
Most edtech platforms scale content delivery; AI, on the other hand, scales cognition. Modern models can interpret student artifacts, detect misconceptions, generate just-in-time scaffolds, and orchestrate adaptive practice aligned to explicit learning outcomes. This is not generic personalization; it’s the capacity to diagnose, adapt, and reteach in the moment, across modalities and languages, making high-impact pedagogy feasible in high-enrollment courses and heterogeneous cohorts.
- Reteaching at the moment of need: When a student stalls on rational expressions or fails to transfer a concept from physics to engineering, AI can surface a focused mini‑lesson, offer a worked example with fading, and check for durable understanding before progressing.
- Scaffolding with intent: Beyond hints, AI can structure problem decomposition, prompt metacognitive reflection, and vary representation (text, diagram, simulation) in response to learner signals.
- UDL by default: AI can produce alternate formats (captioning, transcripts, summaries), linguistic and cultural bridges, and choices for demonstration of mastery, without requiring an instructor to create five versions of every piece of content or assessment.
These are not theoretical niceties; they are the levers that close gaps, reduce DFW rates, and expand belonging.
The Amplification Effect: Good Faculty Get Better; Bad Faculty Get Worse
AI is a force multiplier, not a moral compass. In the hands of reflective practitioners, AI accelerates evidence-based teaching:
- Design acceleration: Drafting diverse examples, generating rubric-aligned feedback, and simulating misconceptions to test an assignment before it goes live.
- Feedback quality: Transforming the “slow drip” of comments into a timely, criterion-referenced dialogue with students, without losing instructor voice or standards.
- Data-informed iteration: Turning clickstream and artifact analysis into actionable course redesign between terms, not once a decade.
Conversely, AI can entrench weak practice:
- Template tyranny: Auto-generated slides and quizzes that look polished but misalign with outcomes (especially based on Bloom’s Taxonomy levels) or propagate misconceptions.
- Over‑delegation: Offloading judgment, grade curving, accommodation decisions, or academic integrity reviews to opaque models, eroding fairness and trust.
- Passive pedagogy: Using AI as a content vending machine rather than a catalyst for higher-order thinking exacerbates disengagement.
The same holds for students. Self-regulated learners use AI as a cognitive exoskeleton, testing understanding, seeking re-explanations, and stress-testing arguments. Avoidant learners use it as an answer factory, bypassing productive struggle and sabotaging long-term retention. AI doesn’t fix motivation; it magnifies it.
The No‑Learning Equilibrium: When Bad Meets Bad
The darkest outcome is a local equilibrium where uninspired teaching meets transactional student behavior. In that setting, AI automates a truce: instructors generate assignments AI can easily grade; students submit AI-written responses tuned to pass the rubric. The LMS records activity, the SIS posts credits, and payroll runs on time. Everyone gets what they wanted except society: no real learning takes place. This is the “automated paycheck and credential” scenario: efficient, inexpensive, and corrosive.
Escaping that trap requires redesigning incentives, assessments, and transparency:
- Assess process, not just product: Incorporate oral exams, whiteboard think-alouds, version histories, and artifact portfolios that reveal reasoning pathways.
- Make misuse harder and learning easier: Open‑book, real‑world tasks with novel data; frequent, low‑stakes checks, and reflective prompts tied to course outcomes.
- Visible AI: Require students to declare when and how AI was used, attach prompts/outputs, and justify edits, turning the tool into a teachable moment.
What Institutions Should Do Now
- Adopt a pedagogy-first AI blueprint. Start with intended learning outcomes and cognitive demands (e.g., analyze, design, critique). Map where AI can add formative checkpoints, scaffolds, and reteaching loops. Then choose the tools, not the reverse.
- Stand up a Learning Engineering function. Pair faculty with learning designers, data analysts, and AI specialists to translate pedagogy into workflows and guardrails. Treat large, multi‑section courses as engineered systems with continuous improvement cycles.
- Redesign assessments for an AI-present world. Move upstream to authentic tasks, public audiences, domain‑novel data, and oral defense. Build rubrics that reward reasoning, evidence, and reflection more than surface form.
- Establish transparent AI use policies. Course‑level syllabi should specify allowed/encouraged uses, required disclosures, and accountability. Institutional policies should cover privacy, model selection, bias monitoring, and data retention.
- Invest in faculty development that respects time. Embed micro credentials into the core practices (AI-supported feedback, UDL with AI, prompt-to-pedagogy patterns) and amortize effort via shared banks of prompts, exemplars, and vetted scaffolds.
- Instrument outcomes, not just clicks. Define observable indicators of learning (concept inventories, transfer tasks, performance on novel items), and use AI to analyze patterns, then close the loop by iterating design.
- Equity by design. Mandate multi-modal support, language accessibility, and proactive nudges. Use AI to identify friction points for underserved students and trigger just-in-time human outreach.
A Practical Heuristic
- Copilot, not autopilot: AI proposes; faculty disposes.
- Explainability over efficiency: If we can’t explain why a scaffold or grade emerged, it doesn’t belong in high stakes use.
- Transparency is a teaching strategy: Normalize the visibility of AI in the learning process.
- Fewer, better tools: Depth of integration beats tool sprawl.
The Bottom Line
AI gives higher education the rare chance to industrialize care; to bring the best of small‑class pedagogy to the scale realities of modern institutions. But tools won’t save us from weak goals, misaligned incentives, or opaque practice. If we lead with pedagogy and design for integrity, AI will make good faculty great and good students formidable. If we don’t, we’ll get the worst version of scale: fast, cheap, and empty.
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Originally posted by Matthew Winn on LinkedIn. Be sure to follow him there to catch all his great industry insights.
