Why Instructional Designers Should Learn to Challenge AI  🔗 **Source:** [Why Instructional Designers Should Learn to Challenge AI](https://www.commlabindia.com/blog/instructional-designers-learn-to-challenge-ai) > [!note]+ Document Notes > tags: [instructional-design, ai-collaboration, critical-thinking, prompt-engineering, professional-development, learning-development] > author: RK Prasad > source: CommLab India > reference: https://www.commlabindia.com/blog/instructional-designers-learn-to-challenge-ai > date created: {{date:YYYY-MM-DD}} {{time:HH:mm}} > > # 🏷️ Title: Why Every Instructional Designer Should Learn to Argue With AI > <!-- deep-read:sections start --> ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ## 📝 Summary - GenAI now drafts objectives, storyboards, scenarios, assessments, and narration fast enough that instructional designers risk mistaking fluency for quality. - Prasad argues the field needs a new professional habit: "arguing with AI" — interrogating its logic, testing assumptions, rejecting weak options, and demanding alternatives before accepting output into a course. - Key stakeholders: instructional designers (especially early-career), L&D leaders and managers, and indirectly the learners whose courses inherit whatever quality bar the designer enforces. - Risk: if agreement becomes the working standard, AI's oversimplifications and blind spots become the course's oversimplifications and blind spots, and no one catches it. - Bottom line: prompting gets output; pressuring tests it. The second skill, not the first, is what protects instructional judgment as AI usage scales. ## 📖 Main Content GenAI shifts the instructional designer's default working posture from blank-page author to reviewer of a already-generated draft. Prasad's core argument is that this shift is not neutral — reviewing a fluent AI answer is cognitively easier than reasoning through a design problem from first principles, so a designer can slip from "active architect" to "passive evaluator" without noticing. Once that happens, the AI's structure, assumptions, and simplifications quietly become the project's, and any weaknesses travel forward into the finished course unchallenged. His proposed countermeasure is a discipline he calls "arguing with AI": not emotional resistance, but a repeatable set of professional moves — ask for alternatives instead of accepting the first output, ask the AI to critique its own draft, defend your chosen option back to the AI and invite it to challenge your reasoning, use counterfactual questions ("what if the opposite were true?"), and treat unusually polished output with more suspicion, not less. The article frames "agreement" as a weak quality bar specifically because AI-generated weaknesses in instructional design tend to be subtle — a little too vague, too dense, too generic, too easy, too decorative — rather than obviously wrong. That subtlety is exactly what makes them slip past designers who are optimizing for speed. The piece also argues arguing with AI has a training effect independent of any single project: designers who practice it get measurably better at spotting vague objectives, false alignment, weak distractors, and oversimplified content — skills that transfer beyond AI-assisted work. This is framed as especially urgent for early-career designers, who may not yet have the instinctive critique reflex that experienced designers built up before GenAI existed, and as a leadership responsibility: managers should build critique into prompt guides and reward better review rather than faster output alone. ## 🗒️ Notes - Central reframe: prompting (getting output) vs. pressuring (testing output) — the article's most load-bearing distinction, and the one most directly actionable for a prompt-guide rewrite. - The "passive evaluator" drift is presented as a cognitive-posture risk, not a competence risk — designers don't get worse at instructional design, they just stop applying it as actively once a plausible draft exists. - Four question sets are given as ready-to-use challenge prompts, one per artifact type: sequencing/prior-knowledge assumptions, objective wording ("which verb is doing too much work"), assessment validity (recall vs. judgment, weakest distractor), and visual/interaction justification (does this solve a learning problem or just look active). - "Fluency is not proof of quality" is offered as the mindset shift underlying the whole piece — smoothness should raise scrutiny, not lower it. - Practical habit list (5 items) is concrete enough to lift directly into a team prompt-guide or onboarding checklist: alternatives-first, self-critique prompts, defend-your-choice prompts, counterfactual prompts, suspicion-of-smoothness. - Leadership framing narrows the audience gap: the same habits are pitched both as an individual skill and as something managers should model, template, and reward — meaning the article is implicitly two articles (an IC habit guide and a management directive) folded into one. - Explicitly positioned as part of a series; next installment is teased as "When AI Should Help, When It Should Challenge, and When It Should Stay Out of the Way" — useful cross-reference if more of the series gets processed later. - No data, study, or named example is cited anywhere in the piece — every claim is asserted from the author's practitioner reasoning, not evidenced. ## 📚 References - [Why Every Instructional Designer Should Learn to Argue With AI](https://www.commlabindia.com/blog/instructional-designers-learn-to-challenge-ai) — primary source for this note. - Related CommLab India posts referenced in the article's own "further reading" rail (not independently verified here): "The Real Risk of GenAI in Instructional Design Is Cognitive Dependence," "The Case for Nemesis AI Prompts in Instructional Design," "Instructional Design Model – What is it & Why Use it?" ## 🗣️ Paraphrase - GenAI can now produce a full first draft of almost any instructional artifact in seconds, and that speed creates a trap: designers start judging their work by whether the AI's output looks usable rather than by whether it's the strongest possible instructional choice. Prasad's fix isn't to distrust AI wholesale — it's to build a habit of pressure-testing everything AI hands back, the same way a good editor pressure-tests a first draft, before it becomes part of a real course. - The more interesting claim underneath the practical tips is developmental: doing this well doesn't just fix the current project, it trains the designer's own judgment. Someone who routinely makes AI defend its sequencing, its objectives, and its assessment items gets sharper at spotting those same weaknesses in any draft, AI-written or not — which is why Prasad thinks this matters most for people newer to the field, who haven't built that reflex yet. ## 📰 Newsletter Fluent doesn't mean finished. This week's read is a sharp reminder that AI-generated storyboards, objectives, and assessments can *sound* done while still carrying the same quiet weaknesses a rushed human draft would — vague objectives, thin distractors, decoration masquerading as interactivity. The fix isn't more prompting, it's pressure: ask for alternatives, make the AI critique itself, and get suspicious the moment something sounds *too* polished. A great habit to fold into onboarding for newer designers, and a good prompt-guide rewrite for teams leaning hard on AI drafts this quarter. ## ✅ Action Items #### DevRel - Education: build a short onboarding module for new instructional designers that drills the five "arguing with AI" habits (alternatives, self-critique, defend-your-choice, counterfactuals, suspicion-of-smoothness) using real course drafts as practice material. - Developer experience: if your team ships internal AI prompt guides, add a "challenge" step template next to the "generate" step template so designers see both by default. #### WordPress - Training: incorporate the article's four artifact-specific question sets (sequencing, objectives, assessments, visuals/interactions) into any AI-assisted course-design training curriculum. - Documentation: if your org's AI usage docs only cover prompting technique, add a companion section on critiquing/pressuring AI output before acceptance. ### → Things 3 - [ ] Draft a one-page "argue with AI" checklist (the 5 habits) for the team's prompt-guide doc #things - [ ] Pull the 4 artifact-specific question sets into a reusable review rubric for AI-drafted course content #things ## 🧪 Hypotheses - Designers who use structured AI-challenge prompts (alternatives, self-critique, counterfactuals) before finalizing objectives/assessments produce work that scores higher on independent expert review than designers who accept first-pass AI output. - Early-career designers trained explicitly in AI-challenging habits reach independent design competency faster than those trained only in prompting technique. - Courses built via a challenge-based AI workflow contain fewer "acceptable-looking weaknesses" (vague objectives, weak distractors, decorative-only interactions) on audit than courses built via single-pass AI drafting. - Teams whose prompt guides embed a mandatory critique step ship training with measurably better objective-to-assessment alignment than teams using ad hoc prompting. - The critical-review skill built by "arguing with AI" transfers to reviewing human-authored drafts, not just AI-authored ones. ### 📊 Methodology - Split-cohort study: two groups of designers build a course from identical SME material, one using prompt-only workflow, one using the challenge-based workflow; blind expert panel scores the outputs on objective quality, assessment validity, and interaction relevance. - Time-and-motion tracking to measure the added cycle time of "arguing with AI" against rework hours saved downstream (editing, SME re-review, learner complaints). - Longitudinal tracking of junior designers' independent review accuracy (measured against senior-designer judgments) at 3, 6, and 12 months, comparing those onboarded with explicit challenge training vs. without. - Content audit of shipped courses for the specific weakness markers named in the article (vague, dense, generic, disconnected, decorative) as a scored rubric, applied blind to AI-involvement status. ### ❓ Questions - What is the real time cost per design cycle of "arguing with AI," and does it net positive against rework avoided? - Can the challenge habit be taught directly to novices, or does it require prior instructional-design judgment to even recognize a weak AI answer — a chicken-and-egg problem the article doesn't address? - Does the technique's value depend on the AI actually being able to self-critique meaningfully, versus just performing contrition when asked "what's weak here?" ### 🔮 Future Research - Whether AI self-critique responses ("what would a skeptical reviewer challenge?") are reliably more rigorous than the AI's original output, or just differently-worded confidence. - Learner-outcome studies (not just designer-perceived quality) comparing courses built with vs. without a challenge-based AI workflow. - Whether the "argue with AI" habit degrades if practiced against increasingly agreeable/sycophantic model versions over time. ### 💡 Implications - Practice: AI prompt guides and course-review rubrics should embed a mandatory challenge/critique step, not just a generation step. - Education: onboarding curricula for new instructional designers should teach challenge habits explicitly rather than assuming they emerge from experience alone. - Policy: L&D quality assurance processes should audit for AI-deference patterns (unedited first-pass acceptance) as a specific risk category. - Security: the "pressure the AI" habit is structurally similar to adversarial prompting used in AI red-teaming — worth cross-referencing if security or eval teams are building similar rigor-testing prompts. ### 🧠 Learning Styles Assessment - Reading level estimate: approximately Flesch-Kincaid grade 8–9 (short, declarative sentences, minimal jargon, heavy use of one-line paragraphs for emphasis) — an approximation based on sentence and word structure, not a computed score. #### 👀 Visual learners - Present: none — pure text, no diagrams, screenshots, or labeled visuals. - Improve: a simple two-column "Prompting vs. Pressuring" visual, or a flowchart of the five habits, would reinforce the article's central distinction. #### 🔊 Aural learners - Present: none — no audio or narration version noted. - Improve: a 2–3 minute audio summary of the five habits and the four question sets would suit this content well, since it's inherently a checklist. #### 🗣️ Verbal learners - Present: strong — clear definitions ("arguing with AI" is explicitly defined), analogies (architect vs. evaluator), and concrete example questions per artifact type. - Improve: a short glossary distinguishing "prompting" from "pressuring" from "challenging" would tighten terms used semi-interchangeably. #### 🧩 Kinesthetic learners - Present: the four question sets and five habits function as a de facto checklist a reader could apply immediately. - Improve: turn the question sets into a fill-in worksheet designers can run against a real AI draft during onboarding. #### 🧠 Logical learners - Present: cause-effect reasoning (fluency → false confidence → unchallenged weakness) is explicit and well-structured. - Improve: a simple decision tree ("Is this output too smooth? → pressure-test → still holds? → accept") would formalize the article's implicit logic. #### 🧑‍🤝‍🧑 Social learners - Present: none — no discussion prompts, forums, or collaborative exercises. - Improve: pair the checklist with a peer-review exercise where two designers independently "argue with AI" on the same draft and compare findings. #### 🧘‍♀️ Solitary learners - Present: implicitly supports self-directed practice, since the five habits are individually applicable. - Improve: add self-check reflection prompts ("which of the five habits did I skip this week, and why?"). #### 🌿 Naturalistic learners - Present: none. - Improve: not a strong fit for this content; skip rather than force an ecosystem analogy. ### 📊 Accessibility Assessment - Assessment is limited to the extracted article text; no rendered page/markup was reviewed, so this section notes general gaps typical of long-form blog content rather than confirmed issues on this specific page. #### Screen reader semantics - Present: content is organized under a clear H1/section structure typical of blog posts (per the site's own "Table of Content" listing). - Improve: verify heading hierarchy in the live page matches the visual "Table of Content" list exactly (no skipped levels), and confirm descriptive alt text on any social-share icons. #### Keyboard navigation - Present: unknown from text extraction alone. - Improve: confirm skip-links and visible focus states on the site's nav/submenu structure, which appeared complex (multiple nested "Show submenu" sections). #### Contrast and readability - Present: unknown from text extraction alone. - Improve: verify body text meets 4.5:1 contrast; short-paragraph style already aids readability. #### Text resizing and reflow - Present: unknown from text extraction alone. - Improve: confirm the article reflows cleanly at 200% zoom given the heavy sidebar/nav structure noted in the raw page content. #### Media and captions - Present: not applicable — no audio/video embedded in this article. - Improve: n/a. #### Forms and interactions - Present: a newsletter subscribe CTA is present ("Subscribe Now"). - Improve: confirm the subscribe form has proper labels and accessible error handling. #### Standards reference - General WCAG 2.2 AA gaps to check on the live page: heading hierarchy consistency, focus visibility across the multi-level nav, and form labeling on the subscribe CTA. ### 🌍 DEIB Assessment #### Cultural breadth - Present: none — no regional or multilingual framing; written from a single, unspecified organizational vantage point. - Improve: examples from varied L&D contexts (different industries, regions, company sizes) would broaden applicability. #### Inclusive language - Present: plain, jargon-light language throughout; terms like "SME," "distractor," and "objective" are used but are standard instructional-design vocabulary. - Improve: a one-line glossary for readers newer to the field (e.g., what counts as a "distractor"). #### Neurodiversity support - Present: strong — short paragraphs, frequent headers, consistent one-idea-per-paragraph structure. - Improve: the five habits and four question sets could be pulled into visually distinct callout boxes rather than left in running prose. #### Representation - Present: no named individuals, case studies, or quoted practitioners other than the author. - Improve: quoting or citing a range of practitioners (not just the author's assertions) would strengthen credibility and representation. #### Psychological safety - Present: respectful, non-alarmist framing — frames AI dependence as a risk to manage, not a moral failing. - Improve: none needed; tone is already measured. ### ✅ Logical Validity #### Strong reasoning - Analogical reasoning: "Every major tool changes not only what professionals can do, but also the habits they need in order to use the tool well" — a reasonable, well-established analogy to prior tool transitions in the field, used to justify why a new habit (not just a new skill) is needed. - Causal reasoning: the "architect → passive evaluator" shift is logically coherent — reviewing a plausible draft requires less generative effort than authoring from scratch, so if review is shallow, the AI's structure persists unchallenged. This is a sound mechanism, clearly stated. #### Fallacies - Unsupported generalization: claims like "it is quickly becoming part of the craft" and "the future danger is..." are asserted with confidence but backed by no data, survey, or cited example anywhere in the piece — the entire argument rests on the author's practitioner intuition. Fix: cite even one concrete before/after example or practitioner account. - Implied false dilemma: the piece frames designers as either "only prompting" or "arguing with AI," without acknowledging a middle ground of partial, inconsistent challenge — which is likely how most practitioners actually operate. Fix: acknowledge a spectrum rather than a binary. #### Tone of voice - Direct, didactic, and repetitive-for-emphasis — frequent one-line paragraphs ("Not emotionally. Not theatrically. Not for the sake of showing resistance.") function rhetorically like a spoken cadence, prioritizing conviction over nuance. ### 🛡️ DARVO Analysis - Not applicable — this is a straightforward advocacy/opinion piece about professional practice, not a response to an accusation. No denial, attack, or victim-reversal rhetoric is present; the article does not defend the author or any party against a claim of wrongdoing. ## 🎯 Conclusion The article's real contribution is a clean vocabulary distinction — prompting versus pressuring — applied to a genuine and underexamined risk: that AI fluency lets weak instructional choices pass as acceptable ones. The five practical habits and four artifact-specific question sets are immediately usable as a team checklist or onboarding module. The weakest point is evidentiary: every claim is asserted from practitioner reasoning with no cited data, study, or example, so treat the hypotheses above as worth testing, not as established fact. Practical next step: turn the five habits into a one-page rubric and trial it against one AI-drafted course before rolling it into a full prompt guide. ## 🔄 Refactor Appendix — Why Every Instructional Designer Should Learn to Argue With AI ### 📚 Literature Atoms #### Pressuring AI beats prompting for output quality **Context & use**: - From the article's core reframe, stated directly in the "AI Should Not Just Be Prompted" section. - Matters because it's the single most transferable line from the piece — a rubric or prompt guide can be built around this one distinction alone. **Tags**: #xref #aphorisms <!-- atomicity: 1 idea ✓ --> #### Fluency is not proof of instructional quality **Context & use**: - Stated explicitly under "Treat Smoothness With Suspicion." - Directly counters the default heuristic ("if it looks good, it's fine") the article opens by naming as dangerous. **Tags**: #xref #aphorisms <!-- atomicity: 1 idea ✓ --> #### Shallow AI review lets its weaknesses become the course's weaknesses **Context & use**: - From the "architect to passive evaluator" argument in "Why This Matters More Than It Seems." - Mechanism-level claim explaining *why* the habit matters, not just *that* it matters — useful as the causal backbone if this gets split into a permanent note. **Tags**: #xref <!-- atomicity: 1 idea ✓ --> ### 🧠 Permanent Candidates #### The prompting/pressuring distinction as a review-rubric anchor **Claim** (Courtney's voice): When I build AI-assisted workflows, I should design two distinct prompt stages — one that generates and one that explicitly pressures the generated output — rather than treating a single prompt-and-accept pass as sufficient. **Evidence pointer**: [[Literature note#Pressuring AI beats prompting for output quality]] **Scope & conditions**: Holds for any AI-drafted deliverable meant for external use (courses, docs, code review); less critical for pure throwaway exploration. **Links**: - [[Nemesis AI prompts]] — because extends - [[AI cognitive dependence]] — because contrasts on failure mode (dependence vs. deference) <!-- atomicity: 1 idea ✓ --> ### ✏️ Fleeting Queue - [F1] Check whether "Nemesis AI Prompts" (referenced in this article's related-posts rail) covers the same "arguing with AI" ground in more tactical detail — worth a deep-read pass. #xref ### → Things 3 - [ ] Build a one-page "prompt then pressure" rubric template for AI-drafted deliverables — context #things <!-- deep-read:sections end --> ## Highlights - The prompting/pressuring distinction as a review-rubric anchor - Note: [[Nemesis AI prompts]] — because extends · - [[Nemesis AI prompts]] — because extends · #atom/zettel/z1 - Check whether "Nemesis AI Prompts" (referenced in this article's related-posts rail) covers the same "arguing with AI" ground in more tactical detail — worth a deep-read pass. #xref - Note: ✏️ Fleeting capture #atom/fleeting/f1 - the piece frames designers as either "only prompting" or "arguing with AI," without acknowledging a middle ground of partial, inconsistent challenge — which is likely how most practitioners actually operate. Fix: acknowledge a spectrum rather than a binary. - Note: 🔍 Fallacy: False Dilemma #fallacy/false-dilemma ## New Highlights added 2026-07-21 at 12:46 PM - Instead of working from a blank page, the designer is now often working from a generated suggestion. That sounds like a clear improvement. In many ways, it is. But it also changes the cognitive posture of the designer. The danger is that the designer moves from active architect to passive evaluator. ([View Highlight](https://read.readwise.io/read/01ky2p4xw6j98s3vjw2h1qdpy9)) - Note: The shift being warned against: AI moves the designer from active architect to passive evaluator. - Tags: atom/literature/h3 - Because reviewing a fluent answer is not the same as thinking through the problem from first principles. If the review is shallow, then the AI’s structure quietly becomes the designer’s structure. Its assumptions become the project’s assumptions. Its simplifications become the course’s simplifications. Its weaknesses travel forward unless someone actively stops them. ([View Highlight](https://read.readwise.io/read/01ky2p4rgw2j0ykvqhzgzms6sg)) - Note: Mechanism: shallow review lets the AI's weaknesses/assumptions propagate into the course itself. - Tags: atom/literature/h2 - Most instructional designers are learning how to prompt AI. Fewer are learning how to pressure it. ([View Highlight](https://read.readwise.io/read/01ky2p4ca6pyh0q4gqwgehnmcr)) - Note: The core reframe: prompting vs. pressuring AI — the single most transferable line of the piece. - Tags: atom/literature/h1 - The more polished the output sounds, the more carefully it may need to be examined. Fluency is not proof of quality. ([View Highlight](https://read.readwise.io/read/01ky2p4gzk1kkz6mm20ee11dfd)) - Note: "Fluency is not proof of quality" — counters the default heuristic that polished-sounding output is trustworthy. - Tags: atom/literature/h1