Instructional design models for the use of artificial intelligence tools and systems

3.1. Agile Instructional Design Model

The agile learning approach originates from software development practices and is based on rapid iterations, constant feedback, and a high level of adaptability. In education, this approach allows content and teaching methods to be continuously improved based on student feedback and learning analytics results.

How artificial intelligence supports the agile model in education:

• Rapid creation and updating of educational materials – Artificial intelligence enables teachers and instructional designers to develop and adapt content quickly. Tools such as ChatGPT can generate texts and questions, Synthesia can create video lessons, and Canva can design visuals.

• Tracking student engagement and understanding – AI-powered analytics allow real-time monitoring of progress and engagement levels. Based on this data, teachers can immediately adjust their approach and content to meet student needs.

• Conducting A/B testing – AI systems can automatically test different versions of the same educational material (such as lessons, quizzes, or videos) to determine which approaches produce better learning outcomes.

Example: 

Instructors regularly adjust their teaching approaches to complex topics. After each lesson, an AI-powered system analyses data on student engagement and performance and suggests ways for teachers to design and adapt the next lesson. In this way, content is continuously improved, and students receive personalised and motivating educational materials aligned with their needs and learning pace.

Advantages and disadvantages of applying the agile model with artificial intelligence:

Advantages: 

• Enables rapid adaptation of content and teaching methods based on student feedback and learning analytics

• Encourages creativity and experimentation in instructional design, as AI tools facilitate testing of different approaches and formats

• Supports continuous improvement of teaching quality through data-driven decisions

• Reduces the burden on teachers for routine tasks (such as creating materials, quizzes, and checking comprehension)

• Contributes to the creation of dynamic, interactive, and personalised educational experiences

Disadvantages:

• Requires constant pedagogical reflection and supervision, as over-reliance on AI can reduce the human aspect and pedagogical warmth of teaching

• Implementation can be challenging for teachers who lack digital competencies or experience with agile methods

• Frequent updates and iterations can lead to content fragmentation if clearly defined learning objectives are not maintained

• Raises questions of ethical responsibility and privacy protection regarding data used in learning analytics

• Requires additional time and team coordination, especially in more complex teaching environments

Reflective questions:

• How could an agile approach be applied in your subject with the support of AI?
• Which activities or phases of teaching could be improved with rapid iterations and adjustments based on learning analytics?

You can read more about the agile model and the application of AI:

Agile Instructional Design Framework

ELM Learning: “Agile Instructional Design: Key to Flexible, Effective Learning.”

Olaide et al. (2024): Artificial Intelligence with Agile Teaching Strategies for Personalized Learning

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