Instructional Design (ID) is the systematic practice of designing, developing, and delivering instructional products and experiences. While the discipline has been formally recognized since the mid-20th century, its visibility and strategic importance have exploded in the 21st century, driven by the digital transformation of education, the rise of corporate Learning & Development (L&D), and the ubiquity of e-learning.

Paradoxically, this increased visibility has birthed a sprawling ecosystem of misconceptions and myths that plague the profession. These myths are not benign; they actively hinder effective learning design, waste organizational resources, devalue the expertise of Instructional Designers (IDs), and ultimately lead to poor learning outcomes.

This article dismantles the most enduring and damaging instructional design myths to provide a clear roadmap for achieving genuine, measurable learning success.

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I. The Role & Scope Myths: Misconceptions About What an ID Actually Does

These myths arise primarily from a lack of understanding of the ID’s strategic and scientific function, often confusing the role with mere content creation or graphic design.

Myth 1.0: “Instructional Designers are just graphic designers who specialize in presentations.”

Origin and Persistence:

This fallacy stems from the most visible output of the ID process: a visually appealing e-learning module or presentation deck. Stakeholders often equate the final polished aesthetic with the entire professional process, overlooking the upstream cognitive, analytical, and architectural work. When IDs spend significant time working in tools like PowerPoint or Articulate Storyline, the visual result is the most accessible feature for laypersons to critique.

The Reality: The ID as Learning Architect

The Instructional Designer’s core function is architectural and analytical, not aesthetic. The process of ID, formalized by models like ADDIE (Analysis, Design, Development, Implementation, Evaluation), is a rigorous application of systems thinking:

• Analysis: IDs spend the majority of their upfront time on Needs Assessment, Task Analysis, and Learner Analysis. They determine why training is needed, who the learners are (prior knowledge, motivation), and what specific tasks must be performed, a process that requires zero graphic design skills.
• Design: This involves selecting appropriate learning theories (e.g., constructivism vs. behaviorism), writing measurable learning objectives (based on Bloom’s Taxonomy), and creating a blueprint or storyboard that structures content for cognitive load management.
• Visuals as Support: When visuals are created, their purpose is cognitive support, not decoration. They must adhere to principles of visual literacy to direct attention, reduce cognitive load, and illustrate abstract concepts, requiring knowledge of perception and cognition, not just aesthetics.

Negative Impact:

This myth leads to under-scoping the ID role, resulting in projects that look beautiful but fail to achieve objectives because the foundational analysis and design phases were rushed or skipped entirely.

Myth 2.0: “If you know the content, you can design the course.”

Origin and Persistence:

This is the “Subject Matter Expert (SME) Fallacy.” It assumes that deep knowledge of a topic automatically confers the ability to teach it. This is often propagated by senior leaders or experts who believe their organizational position makes them qualified to structure learning.

The Reality: The Curse of Knowledge

SMEs are essential, but they suffer from the “Curse of Knowledge,” a cognitive bias where they struggle to recall what it’s like to not know something. They often fail to segment content logically for novices, overlook foundational steps, and use specialized jargon without definition.

• Instructional Design is an Interdisciplinary Science: It is the application of psychology, pedagogy, and communication theory to content. An ID’s expertise lies in asking how content should be segmented, sequenced, assessed, and applied.
• The ID’s Unique Skill: Task Analysis: The ID takes the SME’s raw knowledge and performs Task Analysis, breaking down complex processes into discrete, observable steps, and then mapping those steps to measurable objectives and assessments—a skill rarely held by the SME.

Negative Impact:

SME-led design often results in “information dumps” (or “Shovelware”), characterized by excessive detail, poor sequencing, and assessments that test recall rather than application. This leads to frustrated learners and a low return on training investment.

Myth 3.0: “An ID is only needed for massive, complicated projects.”

Origin and Persistence:

This myth views ID as an overhead cost to be deployed only when stakes are high. It assumes that smaller, simpler projects (like a brief compliance module or a quick process change tutorial) can be handled by managers or developers alone.

The Reality: The Value of Systematic Thinking

The principles of effective instruction—clarity of objective, management of cognitive load, and effective practice—are universal, regardless of the project size.

• Microlearning Requires Macro-Design: Effective microlearning (short, focused lessons) requires even more precise design. An ID ensures that the short module hits one specific objective, that the content is contextualized, and that it includes a mechanism for immediate practice and feedback. A simple topic can be confusing without the systematic approach of an ID.
• Focus on Performance: An ID’s fundamental question is, “What performance change do we need?” Applying the ADDIE or Successive Approximation Model (SAM) framework, even in a scaled-down form, ensures that resources are not wasted on unnecessary training and that the solution is the right one (e.g., maybe the problem is a lack of tools, not a lack of knowledge).

Negative Impact:

This myth leads to a proliferation of ineffective, ad-hoc, and inconsistent training materials across the organization, creating knowledge gaps and increasing the eventual cost of fixing the poorly designed training later.

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II. The Learning Theory Myths: Misinterpretations of Cognitive Principles

These myths are often rooted in misunderstood or misapplied psychological theories, resulting in design practices that actively work against the brain’s natural learning processes.

Myth 4.0: “People have different learning styles (visual, auditory, kinesthetic) and training must cater to them.”

Origin and Persistence:

The VAK/VARK learning styles theory gained widespread popularity because it is intuitively appealing—everyone feels they learn better one way or another. This myth is pervasive in education and L&D and often driven by commercial assessments.

The Reality: The Neurological Flaw

Extensive, peer-reviewed research (including a comprehensive 2009 analysis by Pashler, et al.) has definitively debunked the VAK/VARK hypothesis.

• No Empirical Evidence: There is no evidence that tailoring content delivery to a learner’s preferred style improves learning outcomes. The brain does not process information purely by modality; complex information is processed semantically.
• The Content Dictates the Modality: The most effective modality is determined by the content itself, not the learner’s preference. Learning to tie a knot (a kinesthetic skill) requires demonstration and practice, not just reading text. Learning how to identify a chemical compound (a visual/conceptual task) requires diagrams, not just listening to an audio lecture.
• Focus on Cognitive Principles: Effective design should focus on genuine cognitive principles, such as Multimedia Principle (using words and pictures together) and the Coherence Principle (excluding extraneous material), which optimize processing for all learners.

Negative Impact:

This myth wastes significant development time and resources creating unnecessary duplicate content (e.g., a text version, an audio version, and a video version of the same information), adding complexity without improving effectiveness.

Myth 5.0: “Attention spans are now shorter than a goldfish’s.”

Origin and Persistence:

This highly sensationalized myth, often attributed to a 2015 Microsoft study, claims the average human attention span has dropped to 8 seconds. It is propagated to justify the creation of extremely short, fragmented content.

The Reality: The Myth of Sustained Attention

The myth fundamentally confuses sustained attention (focusing on a single, monotonous task) with selective attention (the ability to focus on relevant information).

• Engagement, Not Duration: Humans can focus intensely for hours—on video games, complex novels, or solving a critical business problem—when the content is relevant, challenging, and varied. The issue is not the duration of a lesson but its engagement and design quality.
• Cognitive Load Management: The brain tires from overload, not duration. Effective e-learning breaks down content into manageable cognitive chunks and forces active processing (practice, reflection, discussion) every 5-10 minutes. A 60-minute course is not too long if it features high-quality instruction, variety, and spaced practice.
• Microlearning’s Purpose: Microlearning is valuable because it provides contextual, just-in-time support at the moment of need, not because the learner cannot focus for longer. Its value is in its accessibility, not its brevity.

Negative Impact:

This myth leads to the oversimplification and fragmentation of complex topics, resulting in content that is too shallow for real competence. It leads to the development of numerous, tiny, disconnected training nuggets that fail to build deep, contextualized understanding.

Myth 6.0: “All you need is the 70:20:10 model.”

Origin and Persistence:

The 70:20:10 model, suggesting that 70% of learning happens through on-the-job experience, 20% through social/coaching, and 10% through formal training, is one of the most widely cited yet least substantiated models in L&D. It originated from research by the Center for Creative Leadership (CCL).

The Reality: The Misapplication of a Descriptive Model

The original CCL research was descriptive—it simply observed how successful executives learned—it was never intended to be a prescriptive instructional design model for how all learning should be structured.

• Formal Training is the Foundation: Formal training (the 10%) is essential because it provides the cognitive schema (the mental framework, or the “how-to”) necessary for the experiential learning (the 70%) to be effective. Without the 10%, the 70% becomes expensive trial-and-error.
• Context is Key: The optimal ratio is entirely dependent on the subject matter. Learning to code a website might be 70:20:10. Learning legal compliance or safety procedures must be closer to 100% formal training to mitigate legal and physical risk.
• The ID’s Role: IDs create the scaffolding for the 70% and 20% by designing effective job aids, mentorship programs, and structured experiential activities that turn random experience into deliberate practice.

Negative Impact:

This myth is often used by executives to justify slashing formal training budgets, arguing that “experience” will cover the gap. This results in untrained employees wasting resources due to lack of knowledge and a failure to standardize organizational best practices.

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III. The Technology & Tools Myths: Oversimplification of E-Learning and Automation

These myths confuse the technology used to deliver learning with the design process itself, leading to the selection of tools before identifying the learning need.

Myth 7.0: “If the training is gamified, it will be engaging and effective.”

Origin and Persistence:

The explosion of mobile apps and the success of video games have led to the belief that adding game mechanics (points, badges, leaderboards) automatically makes corporate training successful. This is often propagated by software vendors who sell “gamified” LMS systems.

The Reality: Mechanics vs. Motivation

Effective gamification requires integrating game design principles that influence internal motivation and directly support the learning objective, not just surface-level mechanics.

The Gamification Fallacy	Simply adding a leaderboard to a dull, text-heavy compliance course only motivates learners to click quickly to accumulate points. It encourages compliance behavior (finishing fast) over learning behavior (mastering the content).
The Focus: Game-Based Learning	The ID’s focus should be on Game-Based Learning (GBL), which embeds learning directly into the game structure. For example, a virtual reality safety simulation where the learner must correctly identify hazards to proceed. The challenge of the game is the practice of the skill.
Intrinsic Motivation	True engagement comes from meeting the learner’s need for Autonomy, Mastery, and Purpose (Self-Determination Theory). A well-designed lesson, even without points, is more engaging than a poorly designed one with badges.

Negative Impact:

This myth leads to the development of “chocolate-covered broccoli”—making boring training superficially palatable—wasting money on unnecessary game mechanics that fail to address the core problem of poor content design.

Myth 8.0: “We need an app for that.”

Origin and Persistence:

This is the “Shiny New Toy” Syndrome. Stakeholders see a successful consumer app and assume that a custom-built mobile app is the best, or only, way to deliver training.

The Reality: The Technology Must Serve the Pedagogy

The ID’s role is to select the most appropriate and cost-effective delivery mechanism based on the learning objective and learner context, not the newest technology.

• The Challenge of App Development: Custom apps are expensive to build, require continuous maintenance (iOS and Android updates), and face the hurdle of learner adoption (getting users to download and use yet another application).
• Mobile vs. App: The most effective “mobile learning” is often responsive web design that works on any device (laptop, tablet, phone) and is housed within an existing, mandatory system (like an LMS or internal portal). This eliminates the friction of downloading a new app.
• Simple Solutions First: The most effective solution may be a well-designed job aid (PDF), a simple checklist, or a text message notification, not a complex, custom-coded mobile application.

Negative Impact:

Wasting budget on custom app development that is over-engineered for the learning task, resulting in an unused tool that fails to integrate with the learner’s actual workflow.

Myth 9.0: “AI will replace the Instructional Designer.“

Origin and Persistence:

The rise of large language models (LLMs) and Generative AI (GenAI) has created panic that AI will automate content generation, rendering human IDs obsolete. This is fueled by AI’s ability to instantly create outlines, write quiz questions, and generate draft text.

The Reality: AI as a Productivity Partner

AI is a powerful tool that automates the Development (D) phase of ADDIE, but it cannot replace the essential Analysis (A) and Design (D) phases—the core of the ID profession.

• AI Lacks Empathy and Context: AI cannot conduct a genuine Learner Analysis, understand a company’s specific cultural nuances, interpret vague stakeholder requests, or apply complex ethical considerations. It cannot ask, “Is training really the solution?”
• The ID as the AI Editor: The ID of the future uses AI to generate content drafts faster. The ID’s value shifts from content creator to content curator, editor, and strategic architect—applying their expertise in cognitive load and objective mapping to refine, integrate, and evaluate the AI-generated output.
• AI Cannot Design Assessments: While AI can generate quiz questions, it struggles to design high-level scenarios and performance-based assessments that truly measure application and synthesis, which is the ID’s most critical value-add.

Negative Impact:

Organizations rush to use AI to generate “training” without human oversight, leading to the rapid proliferation of superficial, non-contextual, and potentially inaccurate training content that lacks the strategic design necessary for performance impact.

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IV. The Content & Delivery Myths: Misbeliefs about Acquisition and Retention

These myths perpetuate ineffective pedagogical practices by relying on tradition or intuition rather than evidence-based strategies for knowledge transfer and long-term retention.

Myth 10.0: “More content is always better.”

Origin and Persistence:

This fallacy is driven by the SME and stakeholder desire for completeness. They fear leaving out any detail, leading to the belief that the learner needs to know everything about a topic to be competent.

The Reality: The Cognitive Load Crisis

The human brain has finite working memory capacity, governed by Cognitive Load Theory (Sweller). Presenting too much information, especially irrelevant information, actively hinders learning.

• Extraneous Load: Adding unnecessary graphics, irrelevant anecdotes, or excessive text creates extraneous cognitive load, which distracts the learner and consumes precious working memory resources, leaving less capacity for meaningful learning.
• The Focus on Performance: The ID’s job is to ruthlessly filter content to include only what is necessary to achieve the measurable performance objective. Content must be segmented into manageable chunks, and any content that is “nice to know” but not “need to know” must be moved to supplemental job aids or resources.
• Primacy and Recency Effects: Overly long lessons dilute the Primacy and Recency Effects, making the information presented in the middle less likely to be recalled.

Negative Impact:

Learners experience information overload and mental shutdown, leading to low knowledge retention, low course completion rates, and the misconception that the training was too “difficult” when it was simply too dense.

Myth 11.0: “Telling them once is enough.”

Origin and Persistence:

This is the belief that a single exposure to a concept—whether through a video, lecture, or text block—is sufficient for long-term retention and skill mastery. This myth is efficient for the trainer but disastrous for the learner.

The Reality: The Power of Spacing and Retrieval

Decades of cognitive science research demonstrate that a single exposure leads to poor retention. Effective learning requires repeated exposure and active effort.

• The Forgetting Curve (Ebbinghaus): Research shows that knowledge retention drops precipitously after initial exposure. To combat this, IDs must employ two high-leverage techniques:

1	Spaced Practice	Distributing practice and review over increasingly long intervals (e.g., review 1 day, 1 week, 1 month later) dramatically improves long-term retention.
2	Retrieval Practice (The Testing Effect)	Making the learner actively recall information (e.g., through low-stakes quizzes, flashcards, or application exercises) strengthens memory traces more effectively than passive review.

• ID Application: The ID must design not just the training event, but the full learning journey, including follow-up resources, automated check-ins, and scheduled review prompts delivered weeks after the initial session.

Negative Impact:

Training events are treated as “one-and-done” transactions. Knowledge is temporarily recalled for a final quiz and then quickly forgotten, leading to a failure to transfer learned skills to the job.

Myth 12.0: “All learning must be discovery-based and self-directed.”

Origin and Persistence:

This myth, often associated with radical constructivism, promotes the idea that learners should “discover” complex knowledge entirely on their own, often using open-ended, minimally guided instruction. This is seen as fostering creativity and critical thinking.

The Reality: The Expertise Reversal Effect

While discovery is valuable for high-level experts, it is highly ineffective and often detrimental for novices, a phenomenon explained by the Expertise Reversal Effect.

• Cognitive Load for Novices: For beginners, unguided discovery creates immense extraneous cognitive load as they waste time navigating the system and figuring out what they need to learn, instead of focusing on the content itself.
• The Need for Scaffolding: Effective constructivist design requires scaffolding—providing structured support that is gradually removed as the learner gains competence. This is often achieved through Guided Practice (working through a problem with feedback) before transitioning to Independent Practice.
• The ID’s Balance: The ID uses Merrill’s First Principles of Instruction, which emphasizes a balance: content must be demonstrated first (show), then applied (practice), and finally integrated (create). Pure discovery violates the first principle and is ineffective for acquiring core skills.

Negative Impact:

Novice learners become frustrated, form incorrect mental models, and often give up. This results in high attrition rates and a failure to meet basic competency requirements.

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V. The Business & Measurement Myths: Devaluing ID’s Strategic Impact

These myths prevent ID from being viewed as a strategic business partner, instead relegating it to a tactical, cost-center function focused solely on output volume rather than performance impact.

Myth 13.0: “The end-of-course survey (Level 1) is enough to measure training success.”

Origin and Persistence:

Kirkpatrick’s Level 1 (Reaction) surveys (e.g., “Did you like the course?” “Was the instructor engaging?”) are the easiest and fastest measurement to gather. Due to time constraints and lack of access to performance data, many organizations stop here, equating positive feedback with effective training.

The Reality: Measuring Impact, Not Preference

Liking a course has little correlation with a change in behavior or business outcome. Effective measurement requires moving up the Kirkpatrick-Phillips Model:

• Level 2 (Learning): Did the learner acquire the knowledge/skill? (Measured by tests, simulations).
• Level 3 (Behavior): Did the learner apply the skill on the job? (Measured by supervisor observation, performance reviews, job aids usage). This is the true measure of training effectiveness.
• Level 4 (Results): Did the change in behavior lead to the desired business outcome? (Measured by KPIs: reduced errors, increased sales, faster time-to-market).
• The ID’s Role: The ID’s contract should involve setting a Level 3 or Level 4 target during the Analysis phase, ensuring the course is designed specifically to achieve that business goal, not just to pass a test.

Negative Impact:

Training remains a cost-center activity that fails to demonstrate its return on investment (ROI). Managers continue to invest in popular, well-liked training that has zero impact on organizational performance metrics.

Myth 14.0: “Our problem is a lack of training.”

Origin and Persistence:

This is the “Training Intervention Fallacy.” When a performance gap is identified (e.g., low sales, high error rates, poor customer service), management’s default, instantaneous solution is to demand training, without deeper root cause analysis.

The Reality: Performance Consulting

The ID’s initial role is not as a training developer but as a Performance Consultant, applying models like Mager and Pipe’s Performance Analysis or the Human Performance Technology (HPT) model. These models establish that performance problems are rarely only knowledge gaps.

• Is it a Knowledge Gap? The ID must first ask, “Could the employee perform the task if their life depended on it?” If yes, the problem is not training.
• Root Cause Analysis: The ID investigates other potential root causes:
  • Incentives: Are they rewarded for doing the task correctly?
  • Environment: Do they have the necessary tools, time, and resources?
  • Feedback: Do they know what “good” performance looks like?
• The ID’s Solution: The ID often recommends non-training solutions (e.g., redesigning the process, improving job aids, clarifying expectations) which are often faster and more effective than a course.

Negative Impact:

Resources are wasted developing training to solve non-training problems. The original performance gap remains unsolved, leading to further frustration and increased costs.

Myth 15.0: “ID is a ‘nice-to-have’ skill set.”

Origin and Persistence:

This myth views ID expertise as expendable or easily replaced by subject matter expertise or by the automatic features of e-learning software.

The Reality: ID is a Strategic Business Function

Instructional Design is the systematic application of science to human behavior and performance. In the modern knowledge economy, ID is a strategic necessity.

• Risk Mitigation: In high-stakes environments (e.g., safety, compliance, high-cost manufacturing), effective ID is a risk mitigation tool. Poorly designed training can lead to regulatory fines, injuries, or critical operational failures.
• Efficiency and Scalability: Professional IDs design training that is scalable, reusable, and efficient to maintain. They create templates, content architecture, and standards that save the organization millions in long-term development costs.
• The ID’s Core Competency: The ability to translate complex, messy organizational needs into structured, measurable, and repeatable learning experiences is the defining skill of the knowledge economy—it is the difference between organizational chaos and predictable performance.

Negative Impact:

Organizations fail to invest in recruiting and retaining skilled IDs, resulting in an internal L&D function that operates reactively, tactically, and ultimately fails to deliver the strategic performance improvements the business requires.

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Conclusion: Reclaiming the Value of Instructional Design

The proliferation of these 15 core myths demonstrates a clear gap between the science of learning and the practice of training. As organizations continue to invest heavily in L&D and technology, the need for the Instructional Designer as a strategic partner has never been more critical.

To achieve genuine learning outcomes by 2026 and beyond, the L&D field must collectively commit to:

1	Leading with Analysis	Never beginning a project without a rigorous Needs and Learner Analysis to ensure training is the correct solution.
2	Adopting Cognitive Science	Designing all content based on evidence-based principles like Cognitive Load Theory, Spaced Practice, and Retrieval Practice, rejecting neuromyths like Learning Styles.
3	Measuring Performance, Not Preference	Moving beyond Level 1 surveys to establish Level 3 and Level 4 metrics that demonstrate the ROI of learning.
4	Embracing Technology as a Support	Utilizing AI and advanced tools to automate development and delivery, thereby freeing up the ID to focus on the high-value strategic work of analysis and design.

By replacing intuition and assumption with the systematic rigor of Instructional Design, organizations can finally realize the full potential of their people and their training investments. The future of the discipline is not in the hands of the tool—it is in the hands of the designer who knows how to apply science to the human mind.