Instructional design models for the use of artificial intelligence tools and systems: Examples of other instructional design models and applications of artificial intelligence

3. Examples of other instructional design models and applications of artificial intelligence

In addition to the ADDIE model, other learning design models that support the effective integration of artificial intelligence (AI) tools and systems are increasingly used in educational practice. These models offer greater flexibility, faster iterative cycles, and better adaptation to users’ real needs, making them especially suitable for dynamic, data-driven educational environments.

Traditional instructional design models typically follow a linear, sequential process in which content is planned, developed, and evaluated in advance, often without the ability to make quick adjustments during course implementation. In contrast, AI-assisted approaches emphasise iterativity, adaptability, and learning analytics, enabling continuous improvement and personalisation of the educational experience.

It is important to note that artificial intelligence does not operate independently or replace the instructor or learning designer. Its role is to support professional decisions and pedagogical judgment, for example, through data analysis, content suggestions, or automation of routine tasks. Human expertise remains central to the process, ensuring the relevance, ethics, and quality of educational solutions.

These approaches shift the focus from assumption-based design to data-driven design and real-time feedback. In this context, the instructor and instructional designer act as reflective process guides who, with the support of artificial intelligence, monitor students’ needs and adapt content according to their progress.

Among the most commonly used models are:

The agile model, which emphasises iterative development and constant adaptation to user feedback
Kemp’s model, which views learning design as a complete system, considering context, goals, content, and student support
The Successive Approximation Model (SAM), which relies on rapid prototyping and continuous improvement of educational solutions
The User-Centered Design (UCD) model, which places the student, or user, at the center of planning and creating educational content.

Below, you will study the principles of each of these models in more detail, along with examples of their application in the context of artificial intelligence. This will provide insight into how different learning design approaches can support the development of more effective, personalised, and engaging educational experiences.

While these models are among the most well-known, it is important to note that many other approaches can be adapted to contemporary educational needs and the specific characteristics of individual educational environments.

Artificial intelligence does not replace instructors or instructional designers but helps them make more informed decisions.

The key to successful implementation of AI tools is balancing technological support with human expertise.

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