Instructional Design Models
Unit 1: Instructional Design Topic: Artificial Intelligence
Artificial intelligence, A.I., is rapidly transforming instructional design by expanding how learning experiences are created, personalized, evaluated, and improved. A.I. powered tools support content creation, adaptive learning pathways, accessibility aligned design, and data informed decision making, allowing instructional designers to respond more effectively to diverse learner needs (Gibson, 2023; Kereselidze, 2023). Key learnings from this unit emphasize that while AI increases efficiency and personalization, it must be implemented intentionally, with attention to ethics, accessibility, learner privacy, and instructional alignment to avoid reinforcing bias or excluding learners (Hobson, 2023).
Hobson, L. (2023, July 21). Why AI can’t replace instructional designers [Video]. YouTube. https://www.youtube.com/watch?v=q6A1XQ2UgMg
A.I. Powered Content Creation
Examples of A.I. tools used for instructional content creation, including text, interactive media, and video based learning.
Personalized and Adaptive Learning
Illustration of A.I. supporting personalized learning experiences tailored to individual learner needs.
Accessibility and Universal Design for Learning
A.I. supported accessibility features that promote inclusive learning design.
Data Informed Instructional Design
Representation of A.I. driven learning analytics informing instructional design decisions.
Unit 2: ADDIE
The ADDIE instructional design model is a systematic framework used to guide the creation of effective learning experiences. It consists of five interconnected phases, Analysis, Design, Development, Implementation, and Evaluation, that work together to ensure instruction is purposeful, learner centered, and outcome driven.
Analysis
This phase focuses on identifying the learning or performance gap, understanding the learners, and clarifying instructional goals.
Design
Instructional designers create a detailed blueprint that outlines learning objectives, instructional strategies, assessments, and content structure.
Development
Involves producing the instructional materials, including content, multimedia, and learning activities, while ensuring quality, accessibility, and alignment with objectives.
Implementation/Evaluation
The instruction is delivered to learners, facilitators provide support, and learner progress is monitored. The evaluation phase assesses the effectiveness of the instruction through formative and summative methods and provides data that informs revisions and continuous improvement.
Unit 3: Dick and Carey Instructional Design Model
The Dick and Carey instructional design model is a systematic, learner centered framework that emphasizes alignment among instructional goals, learning objectives, assessments, instructional strategies, and evaluation. Developed by Walter Dick and Lou Carey in the late 1970s, the model approaches instruction as an interconnected system rather than a linear checklist. Each component informs the others, ensuring that instructional decisions are intentional and data driven (Kurt, 2016).
Implications for Instructional Design
Wide range of activities from hiking to diving, tailored to thrill-seekers of all levels.
Course Type
The selected course type for my minicourse is a how to course.
Course Modality
The selected modality for my minicourse is asynchronous online delivery.
Peer Engagement
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.
Unit 4: Understanding by Design (UbD)
Understanding by Design (UbD) is a backward design framework developed by Grant Wiggins and Jay McTighe that prioritizes learning outcomes and evidence of understanding before instructional activities are planned (Wiggins & McTighe, 2011). Rather than beginning with content coverage or preferred teaching strategies, UbD begins by clarifying what learners should understand and be able to transfer to new contexts.
Stage 1, Identify Desired Results
Instructional designers define enduring understandings and essential questions that reflect the most important ideas learners should retain over time (Bowen, 2017).
Stage 2, Determine Acceptable Evidence
Designers identify how learners will demonstrate understanding through authentic assessments and performance tasks aligned to the desired results (Poston, n.d.).
Stage 3, Plan Learning Experiences
Designing instructional activities, resources, and supports that intentionally prepare learners to succeed on those assessments and achieve the identified outcomes (Poston, 2016).
Unit 5: Rapid Instructional Design & Learning Activities
Rapid Instructional Design is best understood as an instructional approach rather than a prescriptive model, emphasizing speed, iteration, and responsiveness to learner needs. As Pastore (2018) explains, rapid design functions more like a toolkit than a linear sequence, allowing instructional designers to adapt processes based on context rather than following rigid phases. This flexibility is particularly relevant for learning environments shaped by rapidly evolving digital and A.I. technologies, where instructional relevance can diminish quickly if content is not continuously refined.
supports timely, authentic learning
Rapid design is most effective when complexity is managed through intentional constraints rather than eliminated entirely.
introduces important limitations
Without careful design, rapid development can result in shallow assessment or surface level engagement, a concern raised by Pappas (2014)
Expert implications for learner engagement and differentiation
By incorporating varied activity types, including scenario analysis, peer review, reflective writing, and applied design, the minicourse supports diverse learning preferences while maintaining shared learning outcomes
Provides a Strong Foundation for a Minicourse
Allows instructional designers to remain agile without sacrificing instructional rigor or depth of learning.
Unit 6: Successive Approximation Model (SAM)
The Successive Approximation Model, SAM, is an agile instructional design framework developed by Michael Allen that emphasizes collaboration, rapid prototyping, and iterative refinement. Unlike strictly linear models, SAM operates through repeated cycles of design, feedback, and revision, allowing instructional products to evolve in response to learner and stakeholder input. The version emphasized in this module, SAM 2, is particularly appropriate for larger or more complex projects because it includes three structured yet flexible phases, Preparation, Iterative Design, and Iterative Development, culminating in rollout.
feedback driven developmental process
It foregrounds collaboration and prototype testing, allowing instructional materials to evolve before full implementation (Herrholtz, 2020). This structure aligns well with complex learning environments where content, tools, or learner needs are dynamic.
resource intensive
Frequent review cycles require time, coordination, and potentially additional technological tools. For a single designer working with limited free trial software, managing multiple prototype cycles may extend development time.
iterative nature
SAM aligns well with a course on digital literacy and A.I. sensemaking. The content domain evolves quickly, making responsiveness essential. Early prototyping supports authentic scenario design, while continuous evaluation ensures alignment with adult learners’ professional contexts.
feedback driven refinement
- The implications for practice include:
- -Increased collaboration and stakeholder engagement
- -Greater emphasis on formative evaluation
- -Reduced risk of late stage redesign
- -Stronger alignment with evolving learner needs
Unit 7: Course Learning Objectives (CLOs)
In my minicourse, the CLOs define the terminal competencies learners must demonstrate by the end of the course. For example:
- Learners will evaluate the credibility of digital and A.I. generated information using structured evaluative criteria to determine reliability, bias, and contextual relevance in instructional settings.
- Learners will embed A.I. tools intentionally into learning design workflows while maintaining documented human judgment and ethical transparency.
These outcomes represent performance expectations that require higher-order thinking and transfer.
Unit 8: Course Overview
References
Allen, M. W. (2012). Leaving ADDIE for SAM: An agile model for developing the best learning experiences. ASTD Press.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction (8th ed.). Pearson.
Knowles, M. S. (1984). The adult learner: A neglected species (3rd ed.). Gulf Publishing.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Morrison, G. R., Ross, S. M., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction (8th ed.). Wiley.
OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com
Reiser, R. A., & Dempsey, J. V. (2018). Trends and issues in instructional design and technology (4th ed.). Pearson.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
University of Maryland Global Campus. (n.d.). LDTC 605 course materials. University of Maryland Global Campus.
Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). ASCD.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Dick, W., Carey, L., & Carey, J. O. (2015). The systematic design of instruction (8th ed.). Pearson.
Knowles, M. S. (1984). The adult learner: A neglected species (3rd ed.). Gulf Publishing.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Morrison, G. R., Ross, S. M., Morrison, J. R., & Kalman, H. K. (2019). Designing effective instruction (8th ed.). Wiley.
OpenAI. (2025). ChatGPT [Large language model]. https://chat.openai.com
Reiser, R. A., & Dempsey, J. V. (2018). Trends and issues in instructional design and technology (4th ed.). Pearson.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
University of Maryland Global Campus. (n.d.). LDTC 605 course materials. University of Maryland Global Campus.
Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd ed.). ASCD.