Artificial intelligence is rapidly reshaping the field of instructional design. In a 2025 survey by the Association for Talent Development, 80% of instructional designers reported using AI tools in their work, with many saying that AI has helped them work more efficiently and improve the quality of their designs. But the impact of AI goes beyond productivity. It is also changing what instructional designers do, where their expertise adds the most value, and which skills are becoming increasingly important. To understand this changing role, we need to look closely at what AI can do well, where human judgment remains essential, and how instructional designers can combine the strengths of both to create effective learning experiences.
What AI can do well
AI is especially well suited for work that is generative, iterative, variable, data-heavy, or repetitive. It can produce and revise content quickly, generate many alternatives with little additional effort, process large amounts of information, transform content from one form to another, and repeat well-defined tasks consistently. These affordances make AI particularly useful for the following ID tasks.
Generating Design Artifacts and Media. AI can significantly accelerate the development of instructional materials by helping designers draft design artifacts and create media. For example, AI can generate first drafts of learning objectives, storyboards, lesson outlines, assessment items, scenarios, scripts, job aids, and feedback, giving instructional designers a strong starting point that they can refine based on learner needs and design goals. AI can also support media production by creating or assisting with images, illustrations, voiceovers, animations, video scripts, captions, and other multimedia elements. This can reduce the time spent on routine production tasks. However, the instructional designer still plays the critical role of reviewing the output for accuracy, alignment, relevance, and overall instructional quality.
Ideation, Variation, and Rapid Prototyping. AI is especially useful for brainstorming, generating variations, and rapid prototyping. It can quickly suggest alternative activities, examples, scenarios, explanations, and assessment ideas; represent the same concept in multiple ways; and turn ideas into draft storyboards or prototypes. It is also very effective at producing many practice opportunities, such as case studies, role-plays, quiz questions, examples, non-examples, and feedback. This allows instructional designers to explore more options quickly and then select and refine the strongest ones.
Data Management, Processing, and Initial Analysis. AI can also help instructional designers manage and make sense of large amounts of data. It can organize learner feedback, clean and categorize information, summarize survey responses or interview notes, and compare results across multiple sources. It is particularly useful for initial analysis, where it can quickly scan large amounts of information to identify recurring themes, preliminary patterns, unusual responses, or areas that may deserve closer attention. This gives the instructional designer a useful first pass through the data and helps identify where deeper analysis may be needed, and can save considerable time, especially when working with large datasets or multiple sources of feedback. However, instructional designers still need to question and interpret the results cautiously and avoid treating AI-generated analyses as automatically accurate or conclusive.
Automating Routine and Administrative Tasks. AI can take on many repetitive and administrative tasks that consume instructional designers’ time, such as formatting content, summarizing meeting notes, organizing feedback, drafting routine emails, creating documentation, tagging or categorizing materials, checking consistency, and converting content into different formats. It can also help automate established workflows and routine project managing and tracking tasks, especially when the steps are already clearly defined and repeatable. By reducing this workload, AI gives instructional designers more time to focus on higher-value work such as learner analysis, design decisions, collaboration, evaluation, and improving the learning experience.
What shouldn’t AI be trusted to do?
AI is not naturally suited for work that depends heavily on deep local context, nuanced professional judgment, determining what really matters in a specific situation, human relationships, or ethical accountability. It can generate plausible recommendations, but it does not fully understand the people, history, culture, constraints, and consequences surrounding a design problem. These limitations mean that AI may support but not replace instructional designers in the following ID work.
Identifying the Real Problem in Context. AI can work only with the information that is available to it, but the real instructional or performance problem is often hidden in the local context. A request for “more training,” for example, may actually reflect unclear expectations, poor feedback, limited access to tools, weak incentives, low motivation, or a mismatch between what people are taught and what they are expected to do. Identifying the real problem requires talking with learners, subject-matter experts (SMEs), instructors, managers, and other stakeholders, observing how work is actually done, and understanding the environment in which learning and performance take place. This contextual investigation is difficult to automate because much of the most important information is tacit, informal, and never written down. AI can help organize and analyze what is collected, but the instructional designer still has to ask the right questions, interpret what people say, notice contradictions, and determine what problem is truly worth solving.
Judging Pedagogical Fit. AI can generate activities, assessments, and design recommendations that sound convincing, but it cannot be fully trusted to determine whether they are the right choices for a particular learning situation. Good design judgment requires understanding the learners, the instructional goals, the context, the constraints, and the tradeoffs among different approaches. An activity may look engaging but fail to support the objective, or an assessment may appear rigorous while measuring the wrong thing. AI can suggest options, but the instructional designer must decide whether those options are appropriate, aligned, and likely to improve learning.
Guaranteeing Trustworthy Output. AI can produce answers that sound clear, confident, and well written even when the information is incomplete, outdated, or incorrect. It may invent facts, misrepresent sources, or overlook important details. For this reason, instructional designers should not treat AI output as automatically trustworthy. Content, references, and recommendations still need to be checked against reliable sources and reviewed by SMEs when appropriate. The same caution applies to data processed or analyzed by AI. Designers need not only to assess whether the patterns AI identifies are meaningful, but also to look for what it may have overlooked, excluded, or failed to recognize.
Taking Accountability or Responsibility. AI can recommend actions, generate content, and support decision-making, but it cannot be held accountable for the consequences of a design. It does not take responsibility when information is wrong, when an assessment is unfair, or when a design fails to meet organizational or ethical standards. The instructional designer must remain responsible for reviewing AI output, making final decisions, documenting the rationale for those decisions, and ensuring that the design is accurate, appropriate, and aligned with learner and organizational needs.
What skills do instructional designers need now?
As AI takes on more drafting, production, analysis, and routine work, the instructional designer’s role shifts toward the skills that require stronger human expertise. The most important capabilities are no longer just about creating instructional materials, but about understanding how people learn, making sound design judgments, working effectively with others, evaluating AI-supported work, and knowing where human oversight is essential. In this environment, several skills become especially important.
Learning Science as the Foundation. As AI makes it easier to generate lessons, activities, assessments, feedback, and media, instructional designers need an even stronger foundation in learning science to judge whether those outputs will actually support learning. AI can produce something that looks polished and plausible without understanding whether it matches how people learn, whether the cognitive load is appropriate, whether practice is meaningful, or whether feedback will help learners improve. A deeper knowledge of learning science helps instructional designers move beyond asking, “Can AI create this?” to asking, “Will this actually help learners learn, remember, transfer, and perform?” In the AI age, learning science becomes the basis for directing, evaluating, refining, and sometimes rejecting AI-generated design ideas.
AI Literacy as the Essential Competence. AI literacy is now a core competency for instructional designers because AI is becoming part of both the ID process and the learning experience itself. A good instructional designer needs to understand, at a conceptual level at least, how AI works, what generative AI can and cannot do, and where its limitations and risks lie. They also need to know how to effectively communicate with AI, critically evaluate its output, create tailored AI tools, and design human-AI workflows that keep appropriate human judgment in the loop. Just as importantly, instructional designers must understand issues related to bias, ethics, privacy, copyright, and responsible use. Without this foundation, it is easy to overtrust AI, use it inappropriately, or design learning experiences that are technically impressive but not necessarily accurate, fair, or pedagogically sound.
Data-Informed Decision Making. Data-informed decision making becomes even more important in the age of AI because instructional designers now have access to more learner data, faster analysis, and a growing number of AI-generated recommendations. The challenge is no longer simply gathering information. It is deciding which data are meaningful, how much confidence to place in the findings, and what those findings actually mean for design. Instructional designers therefore need to do more than organize and analyze data. They must interpret evidence through the lens of learning science, contextual knowledge, and professional judgment, while also questioning the assumptions and limitations behind the results. The goal is not to follow the data blindly, but to use evidence thoughtfully to guide decisions, test ideas, and continuously improve the learning experience.
Communication and Collaboration as the Key Human Skill. AI can generate content quickly, but it cannot build shared understanding among people. Instructional designers still need to work closely with learners, SMEs, instructors, managers, and stakeholders to uncover needs, clarify goals, gather context, resolve differences, and make design decisions. They also need to explain how and why AI is being used, communicate risks and limitations, and help others interpret results and evidence. As AI speeds up production, the instructional designer’s value increasingly comes from connecting people, asking the right questions, translating between different perspectives, and building trust around the design process.
Other Core ID Skills as a Necessity. A pilot can use autopilot, but still needs to know how to fly. It is important for instructional designers to retain traditional core ID skills even when AI can perform parts of them, because once a skill is fully offloaded, the designer can lose the ability to judge the quality of the AI’s work. If instructional designers repeatedly let AI do the difficult cognitive work, they may gradually lose diagnostic ability, design intuition, ability to notice poor alignment, ability to challenge SMEs, ability to explain design decisions, and ability to work when AI is wrong or unavailable. This means instructional designers need to intentionally keep doing certain tasks themselves even when AI can do them, especially while their design expertise is still developing.
Conclusion
In sum, AI is increasingly becoming a design partner and co-worker in the ID process. It can generate alternatives, accelerate production, and help designers explore possibilities that might otherwise take much longer to do. The instructional designer, however, remains the director of the design process: the person who defines the problem, understands the context, decides what matters, exercises professional judgment, and takes responsibility for the final design. The most valuable instructional designer in the AI era will probably not be the person who can get AI to do the most work. It will be the person who knows what can safely be offloaded to AI, where human judgment must remain, and how to make the most of both.
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