Capturing Elusive Technology: Designing a Course on AI for Learning and Development Practitioners

The rapid evolution of Artificial Intelligence (AI), including Generative AI, has introduced new opportunities and challenges for Learning and Development (L&D) practitioners. This article presents the design and development of an asynchronous online course aimed at equipping current and aspiring L&D professionals with essential AI-related skills for analysis, design, and development within the context of a master’s program. Tools covered in the course included Generative AI tools, such as large language models, and additional AI-powered tools that can contribute to L&D workflows. Course development followed a Learning Engineering approach that considered the unique characteristics of the learner population and was informed by data related to student needs as well as student feedback in course iterations. This design case covers the design decisions made throughout the design, development, and implementation processes of four iterations of this course. The course structure was guided by project-based and resource-based learning theories and practices and tailored to accommodate the rapid pace of AI development. A key challenge we faced as the design team was ensuring that the course equipped students with the knowledge and skills to evaluate and use a variety of AI tools within the short course duration. Closely following the evolution of AI technology was crucial to ensure content relevance. Our design experience also emphasized the need for constant improvement of our asynchronous course design in the face of emerging technologies. Our experience can inform strategies for designing courses on rapidly evolving technologies within rigid course development structures.

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Publication Details

Journal
International Journal of Designs for Learning
Published
2025-12-11
DOI
https://doi.org/10.14434/ijdl.v16i2.42150
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
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article

Capturing Elusive Technology: Designing a Course on AI for Learning and Development Practitioners

Rafael Leonardo da Silva
International Journal of Designs for Learning
Online Learning and Analytics
article

Capturing Elusive Technology: Designing a Course on AI for Learning and Development Practitioners

Rafael Leonardo da Silva
article en

Abstract

The rapid evolution of Artificial Intelligence (AI), including Generative AI, has introduced new opportunities and challenges for Learning and Development (L&D) practitioners. This article presents the design and development of an asynchronous online course aimed at equipping current and aspiring L&D professionals with essential AI-related skills for analysis, design, and development within the context of a master’s program. Tools covered in the course included Generative AI tools, such as large language models, and additional AI-powered tools that can contribute to L&D workflows. Course development followed a Learning Engineering approach that considered the unique characteristics of the learner population and was informed by data related to student needs as well as student feedback in course iterations. This design case covers the design decisions made throughout the design, development, and implementation processes of four iterations of this course. The course structure was guided by project-based and resource-based learning theories and practices and tailored to accommodate the rapid pace of AI development. A key challenge we faced as the design team was ensuring that the course equipped students with the knowledge and skills to evaluate and use a variety of AI tools within the short course duration. Closely following the evolution of AI technology was crucial to ensure content relevance. Our design experience also emphasized the need for constant improvement of our asynchronous course design in the face of emerging technologies. Our experience can inform strategies for designing courses on rapidly evolving technologies within rigid course development structures.

International Journal of Designs for LearningVol. 16(2)
Boise State University (US)
Openalex Percentile: Top 58%
Online Learning and Analytics
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