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 A.I. 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).    

From an instructional design perspective, A.I. serves as an augmentative tool that enhances, rather than replaces, human expertise. Instructional designers remain responsible for conducting analysis, identifying learning gaps, selecting appropriate strategies, and ensuring Universal Design for Learning principles are upheld. A.I. compares favorably to Ritavism Theory because both emphasize learning as a human centered, relational, and network based process. While Ritavism prioritizes meaning making through lived experience and social interaction, A.I. strengthens these learning networks by supporting personalization, accessibility, and responsiveness without supplanting human judgment or instructional intent (Hobson, 2023).

Additional Resources

Gibson, R. (2023). 10 ways artificial intelligence is transforming instructional design. EDUCAUSE Review.

Kereselidze, M. (2023). The role of artificial intelligence in instructional design. eLearning Industry.

Minicourse Overview: Professional Development Focus
             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).

Minicourse Design Questions Embedded in the Course
          To directly address this learning gap, the minicourse is structured around the following guiding questions:
•	What instructional problem or performance issue is A.I. being used to solve, and is training the appropriate solution?
•	How can A.I. generated content be evaluated for alignment with learning objectives, accessibility standards, and learner needs?
•	In what ways can A.I. support personalization and adaptation without increasing cognitive load or reinforcing bias?
•	How can learning analytics and A.I. driven data be used to identify learner progress and instructional effectiveness?
These questions ensure that A.I. use is grounded in instructional analysis, performance improvement, and ethical decision making rather than efficiency alone (Malamed, n.d.; Peck, 2023).

UDL Reflection
            Universal Design for Learning (UDL) provides a critical framework for ensuring that this minicourse creates a flexible and inclusive learning environment that accommodates diverse learner needs and preferences (CAST, 2018). Multiple means of representation are incorporated through text-based explanations, visual examples, short videos, and A.I. supported features such as captioning, transcription, and simplified language options (Power, 2023). Multiple means of engagement are supported through self-paced learning, real world design scenarios, and reflective activities that allow learners to apply A.I. tools within their own instructional contexts (CAST, 2018). Finally, multiple means of action and expression are embedded by allowing participants to demonstrate learning through varied outputs, including written reflections, instructional design prototypes, or A.I. assisted learning artifacts, supporting learner autonomy and accessibility throughout the course (Hobson, 2023). Integrating UDL principles ensures that A.I. enhances access and equity rather than creating new barriers.


References

CAST. (2018). Universal Design for Learning guidelines version 2.2. 
http://udlguidelines.cast.org

EDUCAUSE. (2023). QuickPoll results: Generative A.I. in teaching and learning. 
https://www.educause.edu

Gibson, R. (2023, August 14). 10 ways artificial intelligence is transforming instructional 
design. EDUCAUSE Review. https://er.educause.edu/articles/2023/8/10-ways-artificial-intelligence-is-transforming-instructional-design

Hobson, L. (2023, July 21). Why A.I. can’t replace instructional designers [Video]. YouTube. 
https://www.youtube.com/watch?v=q6A1XQ2UgMg

Kereselidze, M. (2023, August 17). The role of artificial intelligence in instructional design. 
eLearning Industry. https://elearningindustry.com/role-of-artificial-intelligence-in-instructional-design

Malamed, C. (n.d.). Types of analysis for eLearning. The eLearning Coach. 
https://theelearningcoach.com/elearning_design/analysis-for-elearning/

Peck, D. (2023, May 5). 4 types of analysis for instructional design. 
https://www.devlinpeck.com/content/analysis-instructional-design

Power, R. (2023). Accessibility in online learning. In R. Power (Ed.), Everyday instructional 
design: A practical resource for educators and instructional designers (Chap. 17). Pressbooks. https://pressbooks.pub/everydayid/chapter/accessibility-in-online-learning/

Instructional Design Topic

Section 1: AI Powered Content Creation

Section 2: Personalized and Adaptive Learning

Section 3: Accessibility and Universal Design for Learning

Section 4: Data Informed Instructional Design

Dr. J. Ryner, Ed.D.