It is now easier than ever to produce learning materials with generative AI. But as content generation expands, so does the risk of creating learning that is abundant yet not meaningful.
Organizations face increasingly complex learning needs: different roles, expertise levels, ways of working, and moments when people need to apply new knowledge. Learners also expect experiences that are relevant, intuitive, and connected to their work. The challenge is not simply how to create more learning content, but what people need to learn, why they need it, and what experience will help them apply it effectively.
That makes instructional design more important, not less. As AI accelerates learning production, the discipline becomes responsible for the thinking behind those assets: defining the learning problem, understanding the learner, establishing the right objectives, and creating experiences that lead to meaningful behavior and performance.
AI can expand what can be produced. Instructional designers determine what is worth producing.
Where Their Value Resides Today
The importance of the instructional designer and instructional design programs is now recognized early in content production and throughout the learning experience.
The IDs developed today combine a number of areas of expertise which cannot be reliably supplied by AI alone: an understanding of the learners and their situation, knowledge of the way in which people learn, the ability to convert business needs into learning objectives, and the judgement needed to decide whether an experience is in fact working.
They are also taking on a more strategic role, assisting organisations in telling the difference between situations that need formal learning and those that are better handled by performance support, practice, coaching, or learning that takes place as part of everyday work. They determine where technology can improve the experience and where human interaction is still necessary.
In this situation, instructional designers are not merely employing AI to work more quickly; rather, they are determining how AI should be used to produce better learning experiences. The knowledge and expertise they have provide the structure, standards, and appropriate context that turn ever more powerful generative capabilities into learning that is relevant, purposeful, and linked to real-world results.
For many years, Instructional Designers (IDs) had to deal with a production bottleneck. To produce a solid learning curriculum, it was necessary to spend weeks or months interviewing Subject Matter Experts (SMEs), developing storyboards for the modules, formatting the slide decks, and preparing quiz questions. Nowadays, Generative AI has completely changed that workflow, reducing the time taken previously to produce it, from months to just a few days.
A rapid degree of automation has led to a usual question: will AI take the place of instructional designers?
Research in the industry shows that AI is not replacing instructional designers; instead, it is raising the role up the value chain. As repetitive production tasks shift to AI, instructional designers are moving away from being called ‘content creators’ and are becoming Learning Orchestrators and Experience Architects.
The End of the Content Bottleneck
The job of generating raw content is becoming more and more automated. Today, generative AI tools and instructional design tools are thoroughly integrated into the process of learning design, automatically writing up case studies, preparing learning objectives, summarising complicated texts, and even creating interactive multimedia.
Efficiency should not be confused with efficacy. Although the raw output of AI is thorough, it does not possess organizational context, emotional intelligence, or a deep understanding of psychological friction. It may be able to produce a syllabus for leadership training at once, but it cannot inherently grasp a company’s particular culture or the specific anxieties that a newly promoted manager experiences. This level of nuance has to be provided by humans.
Curating the Human-Machine Path
The contemporary instructional designer works at the important point where human expertise and machine-produced output meet, acting as the essential human-in-the-loop.
- Validation and refinement: Rather than composing each word from scratch, the IDs now produce preliminary drafts using AI and then carefully refine these drafts to ensure they are in line with adult learning theories and cognitive science.
- Mitigating Bias and Ensuring Ethics: AI adoption has significantly outpaced governance. Research highlights that AI systems can inadvertently perpetuate biases when trained on flawed data. Modern IDs must possess “AI literacy” to act as ethical gatekeepers, ensuring content is accessible, equitable, and free of algorithmic bias.
- The management of the SME Partnership involves the ID taking charge of the process by which Subject Matter Expertise is scaled. If an SME offers deep contextual knowledge, then the ID arranges for that knowledge to be scaled into individualised and adaptive learning pathways.
Rewiring the Frameworks: From ADDIE to ADGIE
As the role changes, so too do the basic structures of the profession. The traditional, linear ADDIE model (Analyze, Design, Develop, Implement, Evaluate) is changing in order to deal with the speed and agility characteristic of the AI era.
The pioneers in the industry are moving towards frameworks that incorporate AI, a version of which is known as ADDIE+ or ADGIE (Analysis, Design, Generation, Individualization, Evaluation). In these updated models, collaboration between humans and AI is made explicit.
- AI acts as a co-designer and quickly produces personas, pathways, videos, and quizzes.
- Development is carried out in two parts. While the AI focuses on the quick creation of assets, humans invest their time in validation, instructional coherence, and strategic alignment.
- Individualization: By using real-time learning data, the AI modifies both the complexity and the format of the material (for example, giving a struggling learner a video rather than text) in order to produce highly adaptive experiences.
Architecting the Learning Ecosystem
Only when instructional designers are not hindered by the need to format slides can they focus on what matters most: the entire learning journey. In the role of Experience Architects, they create integrated, data-driven learning ecosystems instead of developing separate, individual courses. They decide whether a learner should be given an immersive AI role-play situation for high-stakes practice and whether they only need a short learning intervention as they carry out their daily tasks.
Instructional design has changed from producing passive content to active, intelligent planning. Learning professionals now achieve more engaging, accessible, and strategic experiences by treating AI as a collaborative partner.
Learn how Globant’s Education AI Studio is leveraging innovative technologies to reimagine learning experiences at scale.
