From ADDIE to Action Mapping

When discussing adult education, particularly the design of learning experiences—commonly referred to as instructional design—a wide range of well-known and widely used models inevitably comes into play. These models have long shaped how learning experiences are planned, structured, delivered, and evaluated, reflecting underlying assumptions about how adults learn and how instruction should be organized.

However, a critical question increasingly emerges: to what extent can we continue to rely on these models when both the learner and the tools available for learning have fundamentally changed? Adult learners today operate in environments saturated with information, supported by artificial intelligence, digital platforms, and on-demand resources that challenge traditional notions of instruction, attention, and knowledge acquisition.

Historically, some instructional design approaches have been process-oriented and sequential, emphasizing clearly defined stages that guide designers step by step from needs analysis to evaluation. Others have adopted a learner-centered or performance-oriented perspective, focusing more directly on what learners are able to do as a result of instruction rather than on the instructional process itself. In contemporary educational settings, these models continue to coexist, often applied in parallel without fully questioning the assumptions on which they were originally built.

In practice, the selection of an instructional design model is rarely neutral. Educators and instructional designers tend to gravitate toward frameworks that align with their professional strengths, prior experiences, pedagogical beliefs, or the tools and constraints of their working environments. Time pressure, institutional expectations, and technological infrastructure increasingly shape these decisions, sometimes more than learning theory itself.

This tension becomes particularly visible in adult education. Applying a comprehensive and highly structured model such as ADDIE, for example, can be difficult when designers lack meaningful access to learners’ needs, prior knowledge, or professional contexts before instruction begins. Adult learners often arrive with diverse experiences, evolving goals, and situational constraints that only become visible once learning is already underway in classrooms, virtual spaces, or workplace settings.

This article does not attempt to offer definitive answers, nor does it propose new models or reinterpret existing ones. Its purpose is more modest and, at the same time, necessary: to revisit and describe the most influential instructional design models in adult education, clarifying what they offer, where they fit, and where their limits may lie. By doing so, we can better understand what is inside the box of instructional design—because only by knowing what is inside the box can we meaningfully begin to think outside of it.

ADDIE: The Standard and Linear Model

Foundations of the Model

ADDIE is widely regarded as the most established and recognizable model in instructional design. It represents a systematic and structured approach to the development of training and educational programs and has served for decades as a foundational framework in both academic and professional learning contexts. The model is intentionally designed to follow a linear and sequential progression, in which each stage builds logically upon the previous one.

The name ADDIE is an acronym that refers to the five sequential phases that make up the instructional design process: Analysis, Design, Development, Implementation, and Evaluation. Together, these phases provide a comprehensive roadmap for planning, creating, delivering, and assessing learning experiences. Implicit in this structure is the assumption that instructional problems can be clearly defined in advance and that solutions can be designed in a relatively stable context.

The process begins with the analysis phase, where the instructional designer identifies learning needs, clarifies the problem to be solved, and examines the characteristics of the learners and the context in which learning will take place. This stage typically includes an exploration of learners’ prior knowledge, skills, motivations, and constraints, as well as organizational or institutional requirements.

In contemporary adult learning contexts, however, this phase raises an important tension: learners’ needs, tools, and expectations may evolve rapidly, particularly in environments shaped by artificial intelligence, digital platforms, and constant access to information. As a result, comprehensive upfront analysis may be difficult to complete—or may become outdated—by the time instruction is implemented.

Based on this analysis, the design phase focuses on defining clear learning objectives and determining how those objectives will be achieved. Decisions are made regarding content structure, instructional strategies, learning activities, assessment methods, and the overall learning pathway. The emphasis is on alignment: objectives, activities, and evaluations must all support one another.
Yet in a context where learners increasingly rely on AI-powered tools for information retrieval, explanation, and practice, the traditional role of predefined content and linear learning pathways becomes less stable. Designers must consider whether objectives should prioritize information acquisition or instead focus on judgment, decision-making, and performance in tool-rich environments.

The development phase translates design plans into concrete instructional materials. This may involve creating lesson plans, presentations, digital content, assessments, guides, and other learning resources. Historically, this phase required significant time and effort to produce fixed instructional artifacts. Today, however, the proliferation of rapid authoring tools, generative AI, and adaptive platforms challenges the notion of static materials, raising questions about what truly needs to be “developed” in advance and what can remain flexible or generated on demand.

Once materials are complete, the implementation phase involves delivering the program to learners. This may take place in a classroom, online environment, workplace setting, or blended format. Instructors or facilitators put the instructional plan into action, often following predefined guidelines to ensure consistency and quality. In adult education, however, implementation frequently reveals gaps between planned instruction and actual learner behavior, attention levels, or technological use, especially when learners engage with content asynchronously or alongside other digital tools.

Finally, the evaluation phase assesses the effectiveness of the instructional program. Evaluation may be formative, occurring throughout the process to inform adjustments, or summative, taking place at the end to determine whether learning objectives were met. In fast-changing environments, this raises further questions about timing and relevance: by the time evaluation results are available, learner needs, tools, or performance expectations may already have shifted.

ADDIE remains particularly effective in projects that require thorough documentation, adherence to formal standards, and careful long-term planning. It is commonly applied in higher education, professional certification programs, government training initiatives, and organizational contexts where accountability and traceability are essential. Its primary strength lies in the clarity, structure, and control it provides over the instructional process. At the same time, this same linearity can limit its adaptability in contexts characterized by rapid technological change, reduced attention spans, and evolving learner practices.

Example in Adult Education

Consider a digital literacy course designed for older adults, with the goal of enabling participants to use email and complete basic online procedures independently. Using the ADDIE model, the process would begin with an analysis of participants’ existing technological skills, access to devices, and levels of confidence with digital tools. Based on this information, clear learning objectives would be established, such as the ability to send and receive emails with file attachments.

During the design and development phases, step-by-step guides, simple visual aids, and hands-on practice activities would be created to support learners. The course would then be implemented through in-person sessions that allow for guided practice and individual support. Finally, evaluation would focus on whether participants can successfully perform the targeted tasks on their own.

At the same time, even in this relatively stable context, the presence of rapidly evolving digital tools and AI-based assistants raises questions about how long specific procedural skills remain relevant, and whether instructional design should prioritize mastery of tools or the confidence and strategies needed to adapt to new ones as they emerge.

SAM: Successive Approximation Model and Agile Learning Design

An Agile Alternative to Linear Models

The Successive Approximation Model (SAM) emerged as a direct response to the limitations of traditional linear instructional design models such as ADDIE. While ADDIE emphasizes careful planning and sequential execution, SAM draws on principles from agile software development, prioritizing flexibility, collaboration, and rapid iteration. Implicit in SAM is the recognition that instructional problems are often ill-defined at the outset and that understanding improves through use rather than through extensive upfront analysis.

Rather than treating instructional design as a step-by-step process that must be completed in a fixed order, SAM frames learning design as an evolving and iterative practice. The model assumes that insight into learner needs, contextual constraints, and effective instructional strategies emerges progressively, particularly when designers receive early and frequent feedback from real users. This assumption aligns more closely with contemporary learning environments shaped by fast-changing technologies, AI-enabled tools, and shifting learner behaviors.

Core Structure and Process

At its core, SAM is organized around short iterative cycles that involve designing, prototyping, testing, and refining learning solutions. Instead of producing a fully developed course before implementation, instructional designers create early prototypes—often partial, imperfect, and intentionally provisional versions of learning experiences—that are quickly tested with learners or stakeholders.

Feedback gathered during these testing phases informs immediate revisions, and the cycle repeats until the instructional solution reaches an acceptable level of effectiveness. This rhythm reflects a broader shift in how learning products are developed in digital environments, where rapid experimentation and continuous improvement are often more realistic than long-term design stability.

SAM typically unfolds across two interconnected levels:

• Preparation Phase: Designers collaborate with stakeholders to clarify goals, identify performance gaps, and generate potential solutions. Unlike more traditional models, this phase favors lightweight analysis and shared understanding over exhaustive documentation.
• Iterative Design and Development Phase: Ideas are quickly translated into prototypes, tested in real or simulated contexts, revised, and expanded through multiple cycles, incorporating ongoing input from users and subject matter experts.

This structure allows learning solutions to remain responsive to change, whether driven by evolving organizational priorities, new digital tools, or emerging learner expectations. In environments where AI tools can rapidly alter workflows or knowledge requirements, this responsiveness becomes particularly relevant.

Strengths in Adult Learning Contexts

SAM is especially well suited to adult education contexts characterized by limited time, fragmented attention, and diverse learner profiles. Adult learners often arrive with varied experiences, skills, and expectations that are difficult to fully capture in advance. SAM’s iterative approach allows designers to observe actual learner behavior early on and adjust content, pacing, and instructional strategies accordingly.

Moreover, in a landscape where adults increasingly use AI systems, search engines, and digital assistants to support their learning, SAM accommodates the reality that learning rarely follows a predefined path. By testing learning activities in real conditions, designers can better assess how learners engage with content alongside external tools, rather than assuming a closed instructional environment.

Example in Adult Education

Consider the development of a short online training program for employees who must quickly learn to use a newly implemented workplace software system. Using SAM, an instructional designer might begin by creating a minimal prototype focused on the most critical tasks, rather than designing the entire course upfront.

This prototype would be tested with a small group of employees, who would provide feedback on clarity, usability, pacing, and relevance. Based on this feedback, the designer would revise explanations, adjust practice opportunities, and gradually expand the training to include additional tasks. Through successive iterations, the learning experience evolves into a focused and efficient intervention that reflects how employees actually use the software—often in combination with help features, AI assistants, or informal peer support.

Limitations and Considerations

Despite its advantages, SAM is not without limitations. Its reduced emphasis on formal documentation can pose challenges in highly regulated or compliance-driven environments, where traceability and standardized processes are required. The model also assumes sustained stakeholder and learner involvement, which may not always be feasible.

Additionally, in environments with severe attention constraints, iterative cycles risk expanding in scope or prioritizing speed over coherence if not carefully managed. As with any instructional design approach, SAM requires experienced judgment to balance flexibility with focus.

Bloom’s Taxonomy: Structuring Levels of Learning

Conceptual Foundations

Bloom’s Taxonomy, originally developed by Benjamin Bloom and his colleagues in 1956 and later revised in 2001, is not an instructional design model in the strict sense, but rather a classification system for learning objectives. It has become one of the most influential and enduring frameworks in education due to its capacity to describe varying levels of cognitive complexity involved in learning.

The taxonomy provides a shared language for educators and instructional designers to articulate what learners are expected to achieve. Instead of treating learning as a single, uniform outcome, Bloom’s framework assumes that learning progresses from basic knowledge acquisition to increasingly complex forms of thinking and creation. Implicit in this structure is a view of cognition that predates many of the technological conditions shaping learning today, particularly the widespread availability of AI-supported tools that can assist with recall, explanation, and even content generation.

The Cognitive Levels

In its revised version, Bloom’s Taxonomy organizes cognitive learning into six levels:

• Remember: retrieving relevant knowledge from memory.
• Understand: constructing meaning through interpretation, explanation, or summarization.
• Apply: using learned information in concrete or novel situations.
• Analyze: breaking information into parts and examining relationships or structures.
• Evaluate: making judgments based on criteria and standards.
• Create: generating new ideas, products, or ways of organizing information.

These levels are often represented hierarchically, suggesting progression from lower- to higher-order thinking. In practice, however, learning rarely follows a linear path. Learners move fluidly between levels depending on context, task demands, and available tools. In contemporary learning environments, this fluidity is further amplified by technologies that can automate or support certain cognitive processes, particularly at the lower levels of the taxonomy.

Role in Instructional Design

Bloom’s Taxonomy remains widely used across educational settings because of its clarity, flexibility, and intuitive structure. In adult education, it plays a key role in aligning learning objectives, instructional activities, and assessment strategies. By explicitly identifying the intended cognitive level of each objective, instructional designers can ensure that learning experiences are intentionally designed rather than implicitly assumed.

At the same time, the presence of AI-powered systems raises important questions about how Bloom’s levels are interpreted. When learners can easily outsource remembering, summarizing, or even applying information to external tools, the distinction between cognitive effort and cognitive outcome becomes less clear. This does not render Bloom’s framework obsolete, but it does complicate assumptions about what it means for a learner to “know” or “understand” something in practice.

The taxonomy also continues to inform assessment design. Lower-level objectives may be assessed through recognition or recall tasks, while higher-level objectives require analysis, judgment, and creation. However, in environments with limited attention and abundant external support tools, designers must consider whether traditional assessment formats accurately reflect learners’ cognitive engagement or merely their ability to use available resources.

Application in Adult Learning Contexts

Adult learners typically bring extensive prior knowledge, professional experience, and situational awareness to learning environments. Bloom’s Taxonomy can help designers capitalize on this experience by intentionally targeting higher cognitive levels associated with reflection, problem-solving, and decision-making. Rather than allocating excessive time to recall or comprehension, adult education programs often emphasize application, analysis, and evaluation in authentic contexts.

At the same time, adult learners frequently operate under time pressure and cognitive overload. In such conditions, the assumption that learners must always progress systematically through Bloom’s lower levels may not hold. Designers may need to accept that certain foundational knowledge is accessed just-in-time through digital tools, while instructional time is devoted to sense-making, judgment, and adaptation.

Example in Adult Education

Consider a basic accounting course designed for adult entrepreneurs. Early learning activities may focus on remembering key financial terms and understanding the structure of simple financial statements. As the course progresses, learners apply these concepts to their own business transactions, analyze financial data to identify trends or problems, evaluate decisions related to budgeting or pricing, and ultimately create a financial plan tailored to their specific business context.

In practice, learners may rely on accounting software, templates, or AI-assisted tools to support lower-level cognitive tasks, while instructional design focuses on interpreting results, making informed decisions, and understanding consequences—areas where human judgment remains central.

Strengths and Limitations

One of the primary strengths of Bloom’s Taxonomy is its universality and conceptual clarity. It provides a structured way to think about cognitive demand and learning depth, making it especially useful for curriculum planning and instructional alignment. However, it does not prescribe instructional methods, nor does it account for motivational, emotional, or social dimensions of learning. It also assumes a relatively stable relationship between cognitive processes and learner performance—an assumption increasingly challenged by technology-mediated learning environments.

For these reasons, Bloom’s Taxonomy is most effective when used as a descriptive and organizational tool, integrated thoughtfully within broader instructional design frameworks, rather than as a prescriptive model applied in isolation.

GaGagné’s Nine Events of Instruction

A Cognitive Framework for Effective Lessons

Gagné’s Nine Events of Instruction, developed by Robert Gagné, are grounded in cognitive psychology and propose that effective learning occurs when external instructional events are deliberately aligned with internal mental processes. Unlike models that structure entire programs or curricula, Gagné’s framework is particularly suited for the design of individual lessons, modules, or tightly bounded learning sequences.

Gagné’s central contribution lies in identifying specific instructional actions that support attention, encoding, practice, feedback, and retention. The model assumes that learning is not incidental, but rather the result of carefully sequenced instructional experiences that guide learners through a complete cognitive cycle from initial engagement to transfer.

This assumption reflects a view of learning environments as relatively controlled spaces, where instructional designers can manage the learner’s focus, sequence of activities, and exposure to information—an assumption that merits closer examination in contemporary, technology-saturated contexts.

The Nine Instructional Events

The model outlines nine instructional events, each aligned with a corresponding cognitive function:

1. Gain attention – capturing learners’ interest through questions, problems, stories, or unexpected stimuli.
2. Inform learners of objectives – clearly communicating what learners will be able to do as a result of instruction.
3. Stimulate recall of prior knowledge – activating existing schemas that support new learning.
4. Present the content – introducing new information in a structured and meaningful way.
5. Provide learning guidance – offering examples, analogies, cues, or strategies to facilitate understanding.
6. Elicit performance – giving learners opportunities to practice or apply what they have learned.
7. Provide feedback – delivering timely and specific information about learner performance.
8. Assess performance – measuring whether learning objectives have been achieved.
9. Enhance retention and transfer – supporting long-term memory and application in new contexts.

As many instructors have observed, this sequence closely resembles a well-designed persuasion or sales cycle, guiding learners from attention to action. In contemporary environments, however, learners’ attention is increasingly fragmented across multiple stimuli, platforms, and tools—including AI-driven systems that compete with or supplement instructional guidance.

Relevance for Adult Education

Gagné’s model remains particularly effective in adult education contexts where time is limited and learning must be efficient, purposeful, and clearly structured. Adult learners often value transparency regarding objectives, logical sequencing, and immediate opportunities to apply new knowledge. The nine events offer a reliable scaffold for designing learning experiences that meet these expectations.

At the same time, adult learners rarely engage with instruction in isolation. They often interact with content while simultaneously consulting digital resources, peer networks, or AI-based assistants. This raises questions about how much control instructional designers realistically have over attention, sequencing, and guidance, especially beyond the initial moments of a lesson.

Nevertheless, the model’s emphasis on practice and feedback remains highly relevant. These elements address aspects of learning that technology cannot fully automate, particularly when skill development, judgment, and safe performance are required.

Example in Adult Learning

Consider a workplace training session on safety procedures. The instructor may begin by showing a short video depicting a real accident to gain attention. Learning objectives are clearly stated, followed by a discussion that activates participants’ prior experiences with workplace safety. New procedures are then presented using demonstrations and visual aids, supported by checklists and guidance.

Learners participate in a simulated safety drill, receive immediate feedback on their actions, and are assessed on their performance. The session concludes with a discussion on how the procedures apply to different work scenarios, reinforcing retention and transfer.

In practice, learners may later rely on digital reminders, automated alerts, or AI-supported systems to support compliance. Instruction, therefore, plays a critical role not in replacing these tools, but in helping learners understand why procedures matter and how to respond when automated guidance is insufficient or fails.

Contemporary Use and Limitations

Although Gagné’s model was originally developed in the 1960s, it remains widely used in face-to-face instruction, e-learning modules, microlearning experiences, and corporate training environments. Its structured nature helps ensure that lessons are complete and cognitively coherent, particularly in contexts where consistency and reliability are essential.

However, when applied mechanically, the model can feel rigid and may underemphasize learner autonomy, exploration, and self-direction. It also assumes a level of instructional control that may be difficult to sustain in environments characterized by constant interruptions, multitasking, and external cognitive support tools.

For these reasons, Gagné’s Nine Events are most effective when used as a design lens rather than a strict script, often in combination with more flexible or learner-centered approaches that acknowledge the realities of modern adult learning.

Merrill’s First Principles of Instruction

A Task-Centered View of Learning

M. David Merrill proposed the First Principles of Instruction as a synthesis of research on effective learning, distilling common patterns observed across successful instructional approaches. Rather than offering a rigid sequence of steps, the model identifies a small set of core principles that, when applied together, increase the likelihood that learning will be meaningful and transferable. Merrill’s framework is explicitly learner-centered and task-oriented, emphasizing purposeful engagement with real problems over exhaustive content coverage.

At the core of this model lies the assumption that learning is most effective when it is anchored in real-world tasks. Knowledge, in this view, is not an end in itself but a means to perform actions, solve problems, and make decisions in authentic contexts. This assumption aligns closely with contemporary learning environments, where information is widely accessible and increasingly supported by digital and AI-driven tools.

At the same time, the rapid evolution of work practices raises important questions about task stability. In many professional contexts, tasks themselves are changing due to automation, software updates, and AI-supported workflows. This challenges instructional designers to consider not only how well tasks reflect current practice, but also how resilient learning experiences are when tasks evolve or partially disappear.

The Five Core Principles

Merrill identified five interrelated principles that define effective instruction:

• Problem-centered learning – Learning is promoted when learners engage in real or realistic tasks that mirror how knowledge is used outside the learning environment.
• Activation – Learning improves when prior knowledge or experience is activated and connected to new information.
• Demonstration – Learning is facilitated when new knowledge or skills are shown in action rather than merely described.
• Application – Learning is strengthened when learners actively apply new skills and receive guidance and feedback.
• Integration – Learning is consolidated when learners reflect on, articulate, and integrate new knowledge into their own personal or professional contexts.

These principles are intended to function as a coherent system, not as isolated techniques. Merrill argues that instruction is most effective when all five principles are present and aligned within a learning experience. In contemporary contexts, this alignment becomes particularly important as learners increasingly rely on external tools to support parts of the learning process.

Alignment with Adult Learning Theory

Merrill’s principles align closely with adult learning theory, particularly andragogy and experiential learning. Adult learners are typically goal-oriented, motivated by relevance, and sensitive to perceived usefulness. They want to understand not only what they are learning, but why it matters and how it applies to their immediate context.

By centering instruction on authentic tasks, Merrill’s model acknowledges adults’ existing expertise and frames learning as skill refinement and problem-solving rather than passive content consumption. The emphasis on activation and integration recognizes that adult learning is cumulative and situated, building on prior experience rather than starting from scratch.

However, in environments where AI systems increasingly assist with task execution—suggesting actions, generating outputs, or automating decisions—the boundary between human performance and tool-supported performance becomes less distinct. This raises questions about how instructional designers define “application” and “mastery” when learners perform tasks in collaboration with intelligent systems.

Example in Adult Education

Consider a professional training program for healthcare assistants learning a new patient documentation system. Instead of beginning with abstract explanations of system features, the course is organized around a realistic task: completing a patient record accurately and efficiently. Learners first reflect on their existing documentation practices (activation), observe demonstrations of the new system in use (demonstration), practice entering data with guided feedback (application), and discuss how the system fits into their daily workflow and improves patient care (integration).

In practice, learners may later rely on built-in prompts, templates, or AI-supported documentation tools. Instruction therefore plays a critical role in helping learners understand not only how to complete the task, but how to interpret, verify, and intervene when automated support is incomplete or inappropriate.

Strengths and Practical Implications

One of the greatest strengths of Merrill’s model is its explicit focus on performance and transfer. Learning is designed from the outset to prepare learners for action beyond the instructional setting, making the model particularly valuable in vocational education, professional development, and competency-based training.

However, effective application of Merrill’s principles requires careful task selection and a nuanced understanding of real-world contexts. When tasks are poorly defined, outdated, or overly narrow, the learning experience risks becoming misaligned with actual practice. As with other instructional design frameworks, Merrill’s model is most effective when used as a guiding lens, informed by ongoing observation of how work, tools, and learner roles continue to evolve.

Action Mapping (Cathy Moore): A Performance-Focused Approach

Shifting the Focus from Content to Results

Action Mapping, developed by Cathy Moore, represents a significant shift in instructional design by questioning one of the field’s most persistent assumptions: that performance problems are best addressed through the delivery of more content. Instead, Action Mapping reframes instructional design as a process centered on measurable performance outcomes and observable behavior change.

This approach is closely associated with human performance improvement, workplace learning, and business-driven training strategies. Its influence is especially strong in organizational contexts where the value of learning initiatives is judged not by completion rates or content coverage, but by their impact on concrete results. In environments increasingly shaped by automation and AI-supported workflows, this focus on outcomes rather than instruction itself becomes particularly salient.

Core Logic of the Model

Action Mapping begins not with learning objectives or content outlines, but with a clear definition of the desired organizational goal. The model poses a foundational question: What do people need to do differently in their work in order to achieve this goal?

From this starting point, the design process unfolds through four key decisions:

• Identify the business goal – a specific, observable, and measurable outcome the organization seeks to achieve.
• Define the critical actions – the behaviors learners must perform to reach that outcome.
• Design realistic practice activities – decision-making tasks and scenarios that allow learners to practice those actions in context.
• Provide only the necessary information – content is included only when it directly supports performance and informed decision-making.

This logic deliberately reverses traditional instructional design sequences. Learning is driven by actions and decisions, not by content accumulation. Information functions as a support mechanism rather than the focal point of instruction.

Relevance in Adult and Workplace Learning

Action Mapping aligns closely with adult learning contexts where immediacy, relevance, and efficiency are paramount. Adult learners, particularly in professional settings, often have limited tolerance for long, information-heavy courses that feel disconnected from their daily responsibilities. By emphasizing realistic practice and decision-making, Action Mapping reflects how adults typically learn on the job.

The model also resonates strongly with contemporary learning environments in which AI systems increasingly support task execution—suggesting responses, automating steps, or providing just-in-time guidance. In such contexts, the value of instruction shifts from transmitting information to helping learners recognize situations, make judgments, and intervene effectively when automated systems are insufficient or inappropriate.

Importantly, Action Mapping explicitly acknowledges that not all performance problems are learning problems. Some stem from poorly designed processes, unclear expectations, inadequate tools, or misaligned incentives. By encouraging designers to question whether training is the appropriate intervention, the model promotes a more disciplined and ethical use of instructional solutions.

Example in Adult Education

Consider an organization aiming to improve customer satisfaction scores. Rather than developing a traditional course on “customer service principles,” an instructional designer using Action Mapping would begin by identifying specific behaviors that directly influence customer experience, such as responding empathetically to complaints or resolving issues efficiently.

The resulting learning experience would center on realistic customer interaction scenarios in which learners make decisions, experience consequences, and receive feedback. Brief information resources—such as communication frameworks, policy reminders, or examples—would be introduced only when they support these actions. Effectiveness would be evaluated not through tests, but through observable changes in workplace behavior and customer feedback metrics.

In practice, learners may also rely on CRM systems, scripted prompts, or AI-powered recommendation tools. Instruction, therefore, focuses on developing the human judgment and situational awareness required to use these tools appropriately rather than replacing them.

Strengths and Limitations

The primary strength of Action Mapping lies in its direct alignment with performance outcomes. By eliminating unnecessary content and prioritizing practice, the model increases the likelihood that learning interventions will produce measurable impact.

However, Action Mapping also presents challenges. It requires strong collaboration with stakeholders to define meaningful goals and observable behaviors—conditions that are not always present. The model may also feel uncomfortable in educational cultures that equate learning with content mastery rather than performance. Additionally, its focus on measurable outcomes raises questions about how to account for learning goals related to reflection, ethical reasoning, or long-term development, which may not be immediately observable.

For these reasons, Action Mapping is most effective in environments that value outcomes, accountability, and continuous improvement, and when it is applied with an awareness of both its strengths and its boundaries.ent.

The 5E Model: Inquiry-Based Learning and Knowledge Construction

Constructivist Foundations

The 5E Model, originally developed by the Biological Sciences Curriculum Study (BSCS), is grounded in constructivist learning theory, which holds that learners actively construct knowledge through exploration, reflection, and meaning-making rather than through passive reception of information. Although initially designed for science education, the model has been widely adopted across disciplines, including adult education, professional training, and lifelong learning.

At its core, the 5E Model frames learning as a process of inquiry. Learners are encouraged to ask questions, test ideas, reflect on outcomes, and refine their understanding through a sequence of structured yet flexible instructional phases. This view of learning assumes that understanding emerges gradually through engagement with problems rather than through direct transmission.

In contemporary learning environments, however, inquiry increasingly takes place in technology-rich spaces, where learners have immediate access to search engines, simulations, AI-generated explanations, and automated feedback. This raises questions about how inquiry is shaped when exploration is mediated—or partially replaced—by external cognitive tools.

The Five Instructional Phases

The model consists of five interconnected phases that guide learners through a complete inquiry cycle:

• Engage – This phase seeks to capture learners’ interest and stimulate curiosity through questions, problems, or scenarios that connect with prior knowledge. In modern contexts, gaining attention can be particularly challenging, as learners’ focus is often fragmented across multiple digital stimuli.
• Explore – Learners investigate concepts through activities, discussions, or problem-solving tasks, with minimal direct instruction. Traditionally, this phase emphasizes discovery through interaction with materials or peers. Today, exploration may also involve consulting online resources or AI systems, shifting the instructor’s role from facilitator of discovery to curator of meaningful exploration.
• Explain – Learners articulate their understanding, and instructors introduce formal concepts and terminology. This phase serves to consolidate insights and address misconceptions. When learners arrive with AI-generated explanations or prior informal learning, the challenge lies in integrating these inputs into coherent conceptual frameworks.
• Elaborate – Learners extend and apply their understanding to new situations, deepening comprehension and promoting transfer. In adult learning, this phase is particularly significant, as it connects inquiry to real-world decision-making and contextual adaptation.
• Evaluate – Learners and instructors assess understanding and progress. Evaluation often includes reflection, self-assessment, and demonstration of learning. In environments where learners can rely on external tools to support performance, evaluation must distinguish between conceptual understanding and tool-assisted output.

Although often presented sequentially, these phases are not strictly linear. Learners may revisit earlier stages as new questions arise, reinforcing the iterative nature of learning.

Value in Adult Education

The 5E Model aligns well with adult learning principles by promoting active engagement, autonomy, and relevance. Adult learners frequently benefit from opportunities to explore and reflect before receiving formal explanations, particularly when learning builds on prior experience.

The model’s emphasis on inquiry and reflection supports the development of critical thinking and problem-solving skills, which remain essential even in technology-supported environments. However, adult learners also face constraints related to time, cognitive load, and attention. Extended exploration phases may be difficult to sustain in contexts where learning must be efficient or immediately applicable.

Example in Adult Learning Contexts

Consider a financial literacy workshop focused on managing personal debt. The session might begin by engaging learners with a realistic scenario involving credit card balances and interest rates. Participants explore repayment strategies using sample data, discussions, and digital calculators. During the explanation phase, key financial concepts and terminology are introduced to clarify patterns observed during exploration. Learners then elaborate by applying these concepts to their own financial situations, and the session concludes with evaluation through reflection and practical planning.

In practice, learners may also use online tools or AI-powered financial assistants. Instruction, therefore, focuses not on replacing these tools, but on helping learners interpret outputs, recognize limitations, and make informed decisions.

Strengths and Considerations

One of the main strengths of the 5E Model is its capacity to foster deep conceptual understanding and learner engagement. It supports learning that is exploratory, reflective, and meaning-driven. However, the model requires careful facilitation and sufficient time—resources that are often scarce in adult education and workplace learning contexts.

In highly time-constrained, compliance-driven, or performance-focused environments, the 5E Model may need to be adapted or combined with more structured approaches. Its effectiveness depends not only on the model itself, but on thoughtful alignment with learners’ contexts, available tools, and realistic constraints.

Instructional Design in an Era of AI, Speed, and Fragmented Attention

The contemporary learning landscape is defined by profound changes in how people access information, process knowledge, and allocate attention. The widespread availability of artificial intelligence, ubiquitous digital tools, and on-demand information has transformed learning from a scarcity-based activity into an environment of constant cognitive stimulation. At the same time, adult learners face increasing constraints on time, attention, and mental bandwidth, often balancing learning with professional and personal responsibilities.

In this context, traditional instructional design models that prioritize exhaustive content coverage and rigid sequencing are increasingly challenged. Models such as ADDIE, while still valuable for large-scale, regulated, or high-stakes training, may struggle to respond to rapidly evolving needs and the realities of modern learners. When information is readily accessible through AI-driven tools, the instructional designer’s role shifts from delivering content to designing meaningful experiences that promote decision-making, judgment, and performance.

Agile and performance-oriented approaches such as SAM and Action Mapping are particularly well aligned with these conditions. Their emphasis on iteration, rapid feedback, and real-world application allows learning experiences to remain relevant and responsive. Rather than assuming stable requirements and predictable learner needs, these models embrace uncertainty and change as part of the design process. This is critical in an era where tools, workflows, and knowledge domains evolve faster than traditional curricula can be updated.

Similarly, frameworks like Merrill’s First Principles of Instruction gain renewed relevance. By centering learning on authentic tasks and immediate application, Merrill’s model acknowledges that adults no longer need instruction primarily to acquire information, but to apply, integrate, and contextualize knowledge. Artificial intelligence can support recall and explanation, but it cannot replace the human capacity for judgment, ethical reasoning, and contextual decision-making—precisely the skills these task-centered approaches cultivate.

Bloom’s Taxonomy and Gagné’s Events of Instruction continue to provide valuable cognitive and instructional scaffolding, but their use must be intentional and selective. In environments of limited attention, not all learning objectives warrant progression to higher cognitive levels, nor do all lessons require all nine instructional events. Strategic design now involves deciding what not to teach, and which cognitive demands truly matter for performance.

The 5E Model, with its emphasis on inquiry and exploration, remains powerful for fostering deep understanding, but it requires time and cognitive space—resources that are increasingly scarce. As such, it may be most effective when applied in targeted learning moments rather than as a universal framework.

Ultimately, no single instructional design model is sufficient on its own for the realities of modern adult learning. The most effective approaches today are hybrid, selective, and intentional, combining the structure of traditional models with the agility, focus, and performance orientation demanded by AI-driven environments. Instructional design in the present era is less about controlling the learning process and more about orchestrating meaningful interactions, where technology supports learning, but human experience, reflection, and action remain at the center.

In an age of infinite information and limited attention, the true value of instructional design lies not in what learners know, but in what they are able to do, decide, and adapt.

The ideas discussed in this article are informed by recent research in instructional design, adult learning, and artificial intelligence. The following references offer deeper academic and practical perspectives for readers who wish to explore these topics further.

Some References

Choi, Y., Kim, J., Lee, S., & Moon, H. (2024). Utilizing generative artificial intelligence for instructional design: Strengths, weaknesses, opportunities, and threats. Educational Technology Research and Development. https://doi.org/10.1007/s11423-024-10392-6

Evanick, J. (2025). From blueprinting to action mapping: Bridging instructional design practices across higher education and corporate learning. Learning Technologies Conference. https://www.researchgate.net/publication/398829293

Leiker, J., Finnigan, T., Gyllen, J., & Cukurova, M. (2023). Prototyping the use of large language models for adult learning content creation at scale. arXiv. https://arxiv.org/abs/2306.01815

Li, F., Fang, A., Lee, S., Botelho, A., & McNamara, D. (2025). ARCHED: A human-centered framework for transparent, responsible, and collaborative AI-assisted instructional design. arXiv. https://arxiv.org/abs/2503.08931

MacLellan, C. J. (2025). Model human learners: Computational models to guide instructional design. arXiv. https://arxiv.org/abs/2502.02456

Xu, Y., Zhang, L., & Li, H. (2024). Integration of artificial intelligence into instructional design: A scoping review. Computers & Education: Artificial Intelligence, 5, 100162. https://doi.org/10.1016/j.caeai.2024.100162

Wang, Z., Xiao, F., Hou, Y., & Stamper, J. (2025). Enabling multi-agent systems as learning designers: Applying learning sciences to AI instructional design. arXiv. https://arxiv.org/abs/2508.16659

Zhang, Y., & Park, Y. (2024). Redesigning instructional design with an AI-incorporated ADDIE model for 21st century education. Journal of Educational Computing Research, 108(1), 3–29. https://doi.org/10.1177/07356331231174289