Instructional Design Document

Unit 1: Overview of Minicourse Idea

  • 1
    Title
    A.I. for Professionals in Learning Design
  • 2
    Overview
    Artificial intelligence tools are increasingly embedded in instructional design, content development, and professional decision making. However, widespread use has outpaced structured guidance. Many professionals rely on generative A.I. for efficiency without applying systematic credibility evaluation, instructional alignment checks, or ethical safeguards.
    This asynchronous, performance based minicourse prepares educators, instructional designers, and learning professionals to evaluate, refine, and ethically integrate A.I. generated content into instructional workflows. Learners engage in structured analysis, rubric based evaluation, ethical decision making, and artifact revision. The course emphasizes transfer, professional accountability, and documented human oversight in A.I. supported environments. The design reflects backward planning principles and iterative development cycles to ensure alignment, feasibility, and measurable performance growth.
  • 3
    Knowledge Gap
    A clear discrepancy exists between current and desired professional practice. While educators and designers frequently use generative A.I. tools for drafting lessons, assessments, and instructional materials, they often lack structured evaluation frameworks to assess reliability, bias, contextual alignment, and ethical implications (EDUCAUSE, 2023; Malamed, n.d.).
    The desired state includes:
     -Structured credibility evaluation,
     -Systematic detection of inaccuracies,
     -Ethical transparency and attribution practices, and
     -Documented human judgment within workflow integration.
    Without these safeguards, A.I. use may compromise instructional integrity, reinforce bias, or misalign with learner needs. This minicourse addresses the gap by developing measurable, transferable competencies in professional A.I. sensemaking.
This professional development minicourse prepares educators and instructional designers to integrate Artificial Intelligence (A.I.) into instructional design in ways that support content creation, personalized learning, accessibility, and data informed decision making. Research shows that educators are already using A.I. tools primarily for content generation tasks such as drafting lessons, assessments, and instructional materials, often without structured guidance on instructional alignment or ethical use (EDUCAUSE, 2023; Gibson, 2023). This minicourse addresses that reality by guiding participants to use A.I. intentionally, positioning it as a strategic support for sound instructional design rather than a shortcut for production (Kereselidze, 2023; Hobson, 2023).

Unit 2: Target Audience

The target audience for this minicourse includes K to 12 educators, higher education faculty, instructional designers, instructional facilitators, and academic support professionals who are interested in using artificial intelligence tools to support instructional content development in ethical, effective, and policy aligned ways. Learners may work in face to face, online, blended, or professional training environments. Participation may be voluntary for professional growth or required as part of institutional expectations related to instructional innovation, efficiency, or compliance.
Demographics
Background Knowledge and Prior Experience
Skills and Dispositions
Technology Proficiency
Motivations, Constraints, and Accessibility
Learners are adult professionals, generally between the ages of 25 and 60. Most hold at least a bachelor’s degree, with many possessing graduate level credentials in education, instructional design, or related disciplines. Learners are typically employed full time and balance instructional responsibilities, administrative tasks, family obligations, and ongoing professional development. Participants may be geographically dispersed and working within diverse institutional and cultural contexts.
Learners possess strong foundational knowledge of pedagogy, curriculum design, and assessment practices. Most have experience developing instructional materials or delivering instruction. However, prior experience with artificial intelligence varies widely. Some learners may have used AI informally for brainstorming or lesson planning, while others may have minimal exposure and express hesitation due to ethical concerns, uncertainty about accuracy, or lack of clarity regarding institutional policies.
Learners are reflective practitioners who value instructional quality, accuracy, and learner success. They are motivated by improving efficiency, reducing workload, enhancing engagement, and maintaining professional credibility. Many demonstrate strong problem solving and critical thinking skills but may also experience anxiety related to AI use due to concerns about bias, overreliance, or policy violations. Learners typically prefer practical, immediately applicable strategies over abstract or highly technical instruction.
Most learners are comfortable using learning management systems, productivity software, and common educational technologies. Proficiency with AI tools ranges from novice to intermediate. This variation requires scaffolded instruction, clear explanations, and optional advanced resources to support differentiated learning pathways.
Learners are motivated by professional relevance and real world application but often face constraints such as limited time, high workload demands, remote work environments, and accountability pressures. Instruction must be modular, flexible, and accessible. Inclusive design practices, such as captioned videos, readable formatting, alternative text, and multiple content formats, support equitable participation.
By incorporating additional detail about learners’ specific AI related goals, such as lesson planning efficiency, ethical decision making, assessment support, or differentiated instruction, this learner profile becomes highly actionable and directly informs instructional design decisions.

Unit 3: Course Type & Modality

Course Type

The selected course type for my minicourse is a how to course. This format is most appropriate because the identified learning gap involves learners needing explicit guidance to develop a specific skill rather than simply acquire information. A how to course allows for step by step instruction, modeling, guided practice, and performance based assessment, all of which align well with the Dick and Carey model’s emphasis on measurable outcomes and aligned assessments.
Course Modality
The selected modality for my minicourse is asynchronous online delivery. This modality best supports learner flexibility, accessibility, and self paced engagement. It allows learners to revisit content, practice skills, and complete assessments on their own schedules, which is particularly important for adult learners balancing multiple responsibilities. From an instructional design perspective, asynchronous delivery also allows for clear alignment between objectives, instructional materials, and assessments, which directly supports the Dick and Carey framework.

Unit 4: Course Learning Outcomes

The course learning outcomes for this minicourse are intentionally designed using the Understanding by Design (UbD) framework to emphasize transfer, application, and ethical decision making. In alignment with the identified learning gap, the outcomes focus on digital and A.I. supported sensemaking as a critical professional skill for adult learners. Rather than treating A.I. tools as the end goal, the outcomes position A.I. as a support for instructional design thinking, critical evaluation, and responsible professional practice.
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By the end of this course, learners will be able to...

  • 1
    Evaluate A.I. Information Credibility
    Apply structured criteria to determine reliability, bias, and relevance in professional and instructional contexts.
  • 2
    Analyze A.I. Outputs
    Identify limitations, hallucinations, and ethical risks when supporting learning design and decision making.
  • 3
    Apply A.I. Strategies
    Make informed, evidence based professional and instructional design decisions.
  • 4
    Embed Intentionally
    Incorporate A.I. tools into learning design workflows to support ideation, drafting, and refinement while maintaining human judgment and instructional integrity.
  • 5
    Demonstrate Ethical A.I. Use
    Demonstrate ethical and responsible A.I. use by explaining appropriate boundaries, attribution practices, and transparency in educational settings.
  • 6
    Transfer Skills
    Transfer digital and A.I. literacy skills to new professional contexts by independently selecting, evaluating, and justifying the use of emerging tools.

Unit 5: Rapid Instructional Design and Learning Activities

The learning activities are deliberately designed to align with the rapid approach while maintaining instructional rigor. Activities progress from structured evaluation and ethical analysis to synthesis, peer review, and transfer based application. This sequence supports iterative learning and allows feedback to inform both learner growth and instructional refinement. By embedding peer review with an accessibility and ethical focus, the course also leverages social learning to deepen understanding without extending development timelines unnecessarily (Boise State University, 2023).
 Overall, Rapid Instructional Design supports the goals of this minicourse by enabling adaptability, ethical responsiveness, and transferable professional judgment. When paired with clearly articulated outcomes and intentionally designed learning activities, the rapid approach strengthens rather than diminishes instructional quality.

Unit 6: Successive Approximation Model (SAM)

This minicourse is being developed primarily by one designer using limited resources. SAM 2 will be implemented in a scaled manner. The Preparation phase will use backward design principles to define outcomes first (Wiggins & McTighe, 2005). The Iterative Design phase will include two structured prototype cycles rather than extended multi round development. A small pilot group of adult learners will provide formative feedback in place of a full stakeholder team. The Iterative Development phase will progress through streamlined Alpha and Beta stages before final rollout, using free or trial based digital authoring tools.
This adaptation preserves the iterative strengths of SAM while maintaining feasibility within time, technological, and staffing constraints. The model is therefore blended with backward design for outcome clarity and scaled for solo implementation.

Unit 7: Sample Alignment

Grounded in the Understanding by Design framework as articulated by Wiggins and McTighe (2011), and reinforced through alignment logic discussed in rapid instructional design literature. These outcomes prioritize transfer, defensible judgment, and evidence-based application. The emphasis on performance based demonstration reflects Bloom’s higher order cognitive processes, particularly analysis, evaluation, and creation. By the end of this course, learners will be able to:
  • Evaluate the credibility, contextual reliability, and alignment of digital and A.I. generated information using defined evaluative dimensions including accuracy verification, bias detection, transparency, and contextual appropriateness in instructional and professional contexts. Bloom’s Level: Evaluate
  • Analyze outputs produced by generative and predictive A.I. systems to identify hallucinations, systemic bias patterns, instructional misalignment, and ethical risks affecting learning design integrity. Bloom’s Level: Analyze
  • Apply structured sensemaking processes, including comparative analysis, cross verification, and evidence synthesis, to justify instructional and professional decisions in complex information environments. Bloom’s Level: Apply and Evaluate
  • Integrate A.I. tools strategically within a documented instructional design workflow, demonstrating alignment to stated learning outcomes, transparent documentation of tool use, and preservation of human evaluative authority. Bloom’s Level: Create and Evaluate
  • Develop and defend ethically grounded A.I. supported instructional artifacts, incorporating attribution practices, disclosure statements, and articulated design rationales aligned with institutional and professional standards. Bloom’s Level: Create
  • Transfer digital and A.I. literacy competencies to novel professional challenges by independently selecting, evaluating, implementing, and defending emerging tools within authentic design constraints. Bloom’s Level: Evaluate and Create
Sample alignment

Assessments

  • 1
    Pre-Test
    The module begins with a diagnostic pre test delivered through the Canvas Quiz Tool. This ten item multiple choice assessment uses short excerpts from A.I. generated instructional content. Learners identify credibility weaknesses, hallucinations, bias indicators, and ethical red flags. The producible item is the completed auto graded quiz stored in the LMS gradebook. This assessment aligns to CLOs 1, 2, and 5 by measuring baseline analytic and ethical recognition skills prior to structured instruction. The purpose is diagnostic and supports measurable pre to post growth comparison.
  • 2
    Workflow Mapping Artifact
    Learners complete a structured defect analysis checklist using a provided Google Docs template. They must identify a minimum of three inaccuracies, two bias indicators, two hallucinations, and one contextual misalignment within a flawed A.I. generated lesson. Each entry must include quoted evidence and a brief explanation. The producible artifact is the completed checklist submitted as a PDF. The instructor evaluates the submission using a rubric measuring analytic accuracy, evidence use, and completeness. This assessment directly measures Module Objective 1 and aligns to CLO 2.
  • 3
    Rubric Based Credibility Evaluation
    Learners apply a five category evaluation rubric to the same flawed lesson and submit a 400 to 600 word evaluation report that includes scored criteria and written justification for each rating. The rubric addresses accuracy, instructional alignment, accessibility, contextual relevance, and transparency. The producible artifact is the structured evaluation report submitted through Canvas. This assessment measures Module Objective 2 and aligns to CLO 1, requiring learners to apply structured evaluation standards rather than rely on intuition.
  • 4
    A.I. Generated Instructional Artifact Creation and Presentation
    To ensure direct assessment of intentional A.I. integration, learners must create a new micro instructional artifact using an approved A.I. tool such as ChatGPT, Canva Magic Studio, Curipod, Lumen5, or Quizzes A.I. The artifact must address a clearly defined learning objective in their professional context.
    The producible submission includes:
     1. The original A.I. prompt used
     2. The initial A.I. output
     3. The revised final artifact
     A 3 to 5 minute recorded screencast presentation explaining:
     - Why the tool was selected
     - What elements were modified or rejected.
     - How alignment to learning objectives was ensured.
     - What human oversight decisions were made.
    The screencast may be recorded using Zoom, Loom, or Canvas Studio and submitted as a video link.
    The instructor evaluates the artifact and presentation using a rubric measuring intentional tool selection, alignment to learning outcomes, quality of revision, ethical transparency, and clarity of professional reasoning.
    This assessment directly measures CLO 4 and reinforces CLOs 1 and 5. It provides observable evidence that learners can intentionally embed A.I. tools into instructional workflows while maintaining human judgment and ethical accountability.
  • 5
    Ethical Decision Memo
    Following artifact creation, learners submit a professional decision memo evaluating whether their A.I. generated artifact meets ethical and instructional standards for use. The memo includes a recommendation, three identified risks, mitigation strategies, and a transparency statement. The producible artifact is a 600 to 800 word memo submitted as PDF. This assessment measures Module Objective 3 and aligns to CLO 5, requiring higher order evaluative reasoning tied to their own created work.
  • 6
    Summative Revised Artifact and Change Log
    Learners revise their A.I. generated artifact based on peer and instructor feedback and submit:
     -The final revised artifact
     -A structured change log table documenting at least five revisions
     -A 300 word professional reflection describing oversight decisions and alignment adjustments
    The instructor evaluates using a comprehensive rubric measuring instructional alignment, ethical integrity, transparency, and quality of revision. This assessment measures Module Objective 4 and aligns to CLOs 1, 4, and 5. It represents authentic Create level performance.
  • 7
    Post-Test
    The module concludes with a ten item multiple choice post test delivered through Canvas. Questions use a new flawed A.I. instructional excerpt and measure credibility recognition, hallucination detection, and ethical risk identification. The producible item is the auto graded quiz submission. Pre and post test results are compared to measure growth in analytic and ethical recognition skills. This assessment aligns to CLOs 1, 2, and 5.

Unit 8: Learning Theory Selection and Design Rationale for the Minicourse

  • Why a Blended Theory Approach Fits This Minicourse

    This minicourse is performance focused. Learners are not only absorbing information about A.I. generated content risks, they are practicing how to evaluate A.I. outputs, justify decisions, and revise content into an improved instructional product. Because the goal is applied competence, the learning activities must support cognitive processing, authentic practice, reflection, and increasing independence. For that reason, a blended approach using Cognitive Load Theory (CLT), Constructivism and Social Constructivism, and Adult Learning Theory (Andragogy) is the most effective combination to drive the design of learning activities and assessments in this minicourse (Merrill, 2002, Sweller, 1988, Vygotsky, 1978, Knowles et al., 2015).
    This blended approach also fits the way the module is intentionally structured to move learners from guided evaluation to independent performance through sequencing, chunking, and alignment, which are consistent with strong instructional design practice (Instructional Design Team, n.d., Malamed, n.d., Pappas, 2016).

  • Reference

    Hartley, B., & Cha, A. (n.d.). Alignment. The Online Course Mapping Guide, University of San Diego Teaching and Learning Commons.
    Instructional Design Team. (n.d.). Instructional design: Content sequencing in instructional design. Texas Tech University.
    Knowles, M. S., Holton, E. F., & Swanson, R. A. (2015). The adult learner (8th ed.). Routledge.
    Kurt, S. (2020, November 11). How can we align learning objectives, instructional strategies, and assessments? Educational Technology.
    Malamed, C. (n.d.). Chunking information for instructional design. The eLearning Coach.
    Merrill, M. D. (2002). A pebble in the pond model for instructional design. Performance Improvement, 41(7), 39–44. Pappas, C. (2016, August 28). 6 eLearning content chunking strategies to apply in instructional design. eLearning Industry.
    Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
    Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

  • Theory 1: Cognitive Load Theory

    Definition. Cognitive Load Theory explains that working memory capacity is limited, and that instruction should reduce unnecessary processing while supporting the mental work required to learn complex skills (Sweller, 1988). In an online learning environment, this is especially important because learners can experience overload when content is dense, unstructured, or filled with distracting information.
    How CLT drives the design of learning activities. In this minicourse, learners are asked to evaluate A.I. generated instructional content for credibility, bias, hallucinations, and ethical risk. This task involves multiple interacting elements, including content accuracy, context relevance, alignment to objectives, and justification quality. CLT supports the decision to: - Sequence skills from simple to complex, so learners first practice identifying errors before they must defend evaluative judgments and revise content (Instructional Design Team, n.d., Sweller, 1988). - Chunk information into manageable steps, using structured templates and short, focused practice activities, rather than long, open ended tasks that increase extraneous load (Malamed, n.d., Pappas, 2016). - Use consistent tools and formats, such as a repeated rubric structure and standardized checklists, which lowers cognitive friction and lets learners focus on the evaluation skill rather than navigation or formatting.
    Operational implementation in Module 2. CLT directly supports the module design decisions you are using: - The module begins with a multiple choice pre test to establish baseline recognition patterns with minimal cognitive demand. - Learners then complete a structured analysis checklist that reduces overload by narrowing attention to specific defect categories. - Only after structured practice do learners complete the evaluative memo and revised artifact, which are higher demand tasks.

  • Theory 2: Constructivism and Social Constructivism

    Definition. Constructivism emphasizes that learners build knowledge by actively interpreting information and integrating it with prior understanding. Social constructivism extends this idea by emphasizing learning through dialogue, feedback, and meaning making within a community (Vygotsky, 1978).
    How constructivism drives the design of learning activities. Evaluating A.I. generated instruction is not a single right answer skill. It requires interpretation, professional judgment, and justification using criteria. Constructivism supports instructional choices that require learners to:
     - Work with realistic artifacts that contain imperfect content and incomplete transparency. - Apply evaluation criteria, explain why an issue matters, and decide what to do next. - Compare their judgment to standards, exemplars, and peer perspectives.
    How social constructivism fits an online, asynchronous course. In asynchronous courses, discussion boards and structured peer review can function as a learning engine, not just participation. Learners refine their reasoning when they see how others interpret the same flawed content and when they must defend their own evaluation decisions. In this minicourse, peer dialogue is a practical way to strengthen: - Bias detection awareness - Ethical risk reasoning - Calibration of rubric scoring consistency - Operational implementation in Module 2. Social constructivism is embedded through: - A discussion forum where learners post one identified issue, explain why it matters, and propose a revision. - A peer feedback protocol where learners comment using the same rubric language, which increases reliability and shared expectations. - Instructor feedback that focuses on justification quality and alignment to objectives, reinforcing a shared standard of evidence.
    This approach aligns with the course expectation that activities and assessments should support learning progression and reduce learner confusion by clarifying purpose and evaluation criteria (Hartley & Cha, n.d., Kurt, 2020).

  • Theory 3: Adult Learning Theory

    Definition. Adult Learning Theory, also called andragogy, emphasizes that adult learners are goal oriented, practical, internally motivated, and benefit from autonomy, relevance, and problem centered learning (Knowles et al., 2015).
    How adult learning theory drives the design of learning activities. Many learners in this minicourse will be educators, trainers, or professionals who want immediately usable evaluation skills. Adult learning theory supports design choices such as:
     - Clear performance relevance, learners evaluate content they could realistically encounter at work. - Choice within constraints, learners follow required criteria but can propose revisions that match their context. - Reflection on professional practice, learners explain how they would apply ethical oversight in their real environment.
    Operational implementation in Module 2. Andragogy is visible in: - The Ethical Decision Memo, which mirrors a real workplace deliverable and requires a recommendation, risks, and mitigation steps. - The Summative Revised Artifact and Change Log, which resembles a professional quality assurance workflow and documents human oversight decisions. - The inclusion of transparency and attribution decisions, supporting accountability practices adults may need in real instructional settings.

  • How the Theories Work Together in One Aligned System

    This blended approach is intentionally aligned to the assessment system you are building:
     - Cognitive Load Theory shapes structure, chunking, and sequencing to make complex evaluation skills learnable in an online format (Sweller, 1988, Malamed, n.d.).
     - Constructivism and Social Constructivism shape the use of realistic artifacts, justification, peer dialogue, and feedback cycles that develop professional reasoning (Vygotsky, 1978).
     - Adult Learning Theory ensures the work is relevant, problem centered, and autonomy supportive, which improves motivation and transfer (Knowles et al., 2015).
    Together, these theories justify why the learning activities and assessments progress from recognition, to structured analysis, to evaluative judgment, to authentic revision and documentation, which is consistent with strong course alignment principles (Hartley & Cha, n.d., Kurt, 2020).
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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.

Dr. J. Ryner, Ed.D.

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