Overview of the Dick and Carey Model

The Dick and Carey instructional design model is a systematic, learner centered approach that treats instruction as an interconnected system. The model emphasizes intentional alignment among instructional goals, learner analysis, performance objectives, assessments, instructional strategies, and evaluation, so that what learners are expected to do is directly supported by instruction and measured through aligned assessments (Kurt, 2016). This systems perspective is useful because it reduces the risk of designing engaging activities that do not actually build the intended skill.
The model is commonly described as a ten step process. First, the designer identifies instructional goals. Next, an instructional analysis is conducted to determine the subordinate skills required to reach the goal. Learner entry behaviors are then analyzed to understand learners’ readiness, prior knowledge, and likely barriers. The designer writes measurable performance objectives and develops criterion referenced assessments that directly measure those objectives. Instructional strategies are selected to support mastery, followed by developing or selecting instructional materials. Formative evaluation is conducted to identify what works and what needs revision before implementation. Finally, summative evaluation is used to determine overall effectiveness after instruction is delivered (Chaparro et al., 2023).
Implications for Instructional Design
Strengths and Limitations Applied to My Minicourse
A key implication of the Dick and Carey model is its front loaded emphasis on analysis and alignment. For instructional designers, this means the work begins with clearly identifying the performance problem, the learner context, and the measurable outcomes before selecting tools, activities, or media. This emphasis strengthens instructional validity because objectives, assessments, and learning tasks are built as a coherent set rather than separate components (Chaparro et al., 2023).
Another implication is the model’s iterative nature through formative evaluation and revision. Instruction is improved based on evidence, not assumptions. This matters in online learning, professional development, and training contexts where small design flaws can reduce learner persistence or lead to incorrect application of skills. The model supports a continuous improvement mindset that is essential for quality online learning design (Pappas, 2015).
How the model supports my minicourse...
The Dick and Carey model supports my minicourse because it requires me to define the performance gap precisely and design instruction that targets the exact skill learners need. By completing the instructional analysis, I can identify the subordinate skills required for credible AI evaluation, such as recognizing unsupported claims, differentiating opinion from evidence, and applying criteria consistently. This ensures instruction is not simply “tips about AI,” but a structured skill building process (Chaparro et al., 2023).
The model also strengthens alignment. Since the course skill is performance based, assessments should measure performance, not recall. Using Dick and Carey, I can align objectives to scenario based tasks where learners evaluate AI generated outputs and make revision decisions. The model’s emphasis on criterion referenced assessment supports this directly (Chaparro et al., 2023).
How the model may challenge my minicourse...
A limitation is that Dick and Carey can be resource intensive, especially for a small minicourse. Conducting deep analysis and multiple rounds of evaluation can feel heavy for a short course. To address this, I would streamline the analysis steps while preserving the essentials: clear objectives, aligned assessments, and a focused formative evaluation cycle. In other words, I will scale the model to the scope without losing the model’s alignment benefits.
Course Type
My minicourse is a how to course because learners are developing a specific applied skill, not just learning concepts. The goal is that learners can consistently evaluate and improve AI outputs for instructional use. A how to structure supports modeling, guided practice, independent practice, and performance checks, which is appropriate for a skill that requires decision making and revision rather than memorization.
Course Modality
My minicourse is asynchronous online to match adult learners’ schedules and to allow repeated practice and reflection. Learners can work through practice scenarios at their own pace, revisit examples, and refine their decision making. Asynchronous delivery also supports accessibility when learners have varying technology access and time constraints (University of Maryland Global Campus, n.d.).
Reflection & Peer Engagement
Overall, the Dick and Carey model provides a strong foundation for designing structured, outcome focused instruction. Compared to ADDIE, it offers greater precision in analysis and alignment, though at the cost of increased complexity. As I review peers’ applications of this model, I am particularly interested in how they adapt the model to different instructional contexts and whether their chosen course types and modalities align as clearly with their learning gaps. Examining these comparisons deepens my understanding of how flexible, yet disciplined, instructional design models can be when applied thoughtfully.

Target Audience Profile

  • Primary Audience

    Primary audience: Adult educators, trainers, instructional staff, and graduate level students who use AI tools (or plan to) for instructional tasks such as drafting learning objectives, designing activities, generating examples, creating discussion posts, or summarizing content.

  • Background and prior knowledge

    Most learners have baseline digital literacy and have experimented with AI tools at least a few times, but they often lack a reliable method for evaluating AI output quality. Some learners may over trust AI because the language sounds confident, while others may avoid AI entirely due to uncertainty about accuracy and academic integrity.

  • Motivation

    Learners are motivated by efficiency and professional credibility. They want AI to save time while still producing accurate, ethical, high quality work. Many are also motivated by academic integrity expectations and workplace accountability, especially when instructional materials impact learners, compliance, or assessment outcomes.

  • Technical Access & Constraints

    Learners typically have access to a laptop or desktop with internet, though access may vary. Some may rely on mobile devices, so the course materials must be mobile friendly, readable, and low bandwidth when possible. Learners may also have limited time blocks, making self paced participation essential.

Target Audience Profile

  • My minicourse teaches one primary skill: Skill: Learners will be able to apply a structured evaluation process to determine whether an A.I. generated response is credible and usable for a specific instructional task, then revise the output into an accurate, context appropriate product.
  • In practical terms, learners will develop the ability to:
  • Verify claims in AI outputs using a checklist of credibility indicators, and when necessary, cross checking with reputable sources..
  • Identify common AI issues (hallucinated facts, missing context, bias, oversimplification).
  • Revise AI output into a final artifact that meets a defined instructional requirement (audience, constraints, learning objectives, and tone). This skill is intentionally specific so that performance objectives and assessments can measure real proficiency rather than general awareness.

Application to my Minicourse

Using AI Responsibly in Instructional Design In this area you can list out the key services and product groups that your business offers.
Learners will develop the ability to:
  • Apply a provided decision framework to classify AI generated instructional content as usable as is, usable with revision, or not usable, based on alignment with stated learning objectives and instructional constraints.
  • Select and justify specific revision actions for AI generated content, such as narrowing scope, adding required context, removing misleading statements, or restructuring content to support learning progression.
  • Revise AI generated drafts into a finalized instructional artifact, such as a lesson activity, discussion prompt, or learning objective, that explicitly aligns with a stated learner audience, instructional constraints, measurable learning objectives, and an appropriate academic or professional tone.

Performance Objectives (Dick and Carey Aligned)

  • Objective One
    Classification Decision
    Given an AI generated instructional response and a set of stated learning objectives and instructional constraints, learners will classify the AI output as usable as is, usable with revision, or not usable, selecting the appropriate category with 100% accuracy across three instructional scenarios.
  • Objective Two
    Revision Planning
    Given an AI generated instructional draft and an identified classification decision, learners will select and justify at least two appropriate revision actions, such as narrowing scope, adding contextual detail, removing misleading statements, or reorganizing content, with justifications aligned to the stated learning objectives and learner characteristics.
  • Objective Three
    Instructional Artifact Revision
    Given an AI generated draft and a defined instructional context, learners will revise the draft into a finalized instructional artifact, such as a lesson activity, discussion prompt, or learning objective, that fully aligns with the specified learner audience, instructional constraints, measurable learning objectives, and required academic or professional tone, as measured by a criterion referenced rubric.
References
Chaparro, R., Reaves, M., Jagger, C. B., & Bunch, J. C. (2023). Instructional design using the Dick and Carey systems approach (Publication No. AEC632). University of Florida IFAS Extension. https://edis.ifas.ufl.edu/publication/WC294
Kurt, S. (2016). Dick and Carey instructional design model. Educational Technology. https://people-shift.com/articles/dick-carey-instructional-design-model/
Pappas, C. (2015, November 24). 9 steps to apply the Dick and Carey model in eLearning. eLearning Industry. https://elearningindustry.com/9-steps-to-apply-the-dick-and-carey-model-in-elearning
University of Maryland Global Campus. (n.d.). Identifying a course type and modality. https://leocontent.umgc.edu/content/umuc/tgs/ldtc/ldtc605/2262/unit-3-/identifying-a-course-type-and-modality.html?ou=1378426

Dr. J. Ryner, Ed.D.

PHONE: 954-404-4499 Email: J.Ryner@aol.com
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