AI Tools in Instructional Design: Replacement or Revolution?
By: Justice Jones and Zaynah Danquah
Would you prefer to listen to this post? Click below.
Artificial intelligence (AI) is rapidly transforming the development and delivery of learning content. New AI-powered services like Lovable – an AI-driven web design/UX tool – and Synthesia – a platform for AI-generated video content – promise faster, cheaper content creation. This report analyzes whether such tools are likely to replace or significantly reduce the need for instructional designers (IDs). We examine their impact in corporate training, higher education, and the broader instructional design industry, including current use cases, adoption trends, limitations, and how IDs can adapt to remain relevant. Supporting examples, data, and expert insights are provided throughout.
AI in Corporate Training: Enhancing Efficiency, Not Eliminating Roles
Corporate learning and development (L&D) teams have been early adopters of AI tools to accelerate training content creation. Platforms like Synthesia are now used by thousands of companies to produce training videos at scale without studios or actors . For example, Synthesia converts written text or PowerPoint slides into narrated video lessons with lifelike virtual presenters in minutes. Organizations can easily generate product demos, safety tutorials, or compliance training videos in multiple languages, complete with branding and voiceovers . This saves significant time and cost, eliminating the need for camera crews or voice talent . In fact, Synthesia advertises that it’s “trusted by 50,000+ teams globally,” underscoring its widespread corporate adoption .
Use Cases in Corporate L&D: AI tools are being used to streamline many training development tasks:
Video Training at Scale: Text-to-video services like Synthesia allow L&D teams to turn instruction manuals or slide decks into video lessons with AI avatars. This is useful for onboarding, software tutorials, or policy updates, and ensures consistency in messaging. HR teams are using such tools to create employee training videos and internal explainers , while sales enablement teams produce quick product walkthroughs without waiting for studio production.
Rapid eLearning Development: AI design platforms can help build interactive eLearning modules or microsites quickly. Lovable, for instance, acts as a “personal full-stack engineer,” generating web-based applications or sites from a simple prompt . An instructional designer could describe a training portal or a quiz app, and Lovable’s AI will produce a functional web prototype in seconds. This makes it feasible to create custom learning experiences (like scenario simulations or practice tools) without needing a software developer. Notably, Lovable’s ease of use for non-technical users has driven explosive growth – it reportedly went from zero to $17 million in annual recurring revenue in just 3 months , reflecting strong demand for AI-generated design.
Multilingual and Personalized Content: AI can instantly translate or tailor content. A corporate ID can take an English training video and, using AI dubbing or avatars, deliver it in 10+ languages for a global workforce . AI analytics can also adjust difficulty or recommend resources to learners, hinting at future personalized learning paths.
Limitations in Replacing Human Designers (Corporate Context): Despite these advantages, current AI tools have clear limits and do not fully replace the instructional designer’s role. Companies find that human expertise is still essential to ensure training effectiveness:
Content Quality and Accuracy: AI video generators require a well-crafted script as input – they will articulate whatever text you provide, but they cannot judge the accuracy or pedagogical soundness of that content. An instructional designer must still decide what to teach and how to teach it. As one L&D professional noted, even with AI outputs, “it still takes an Instructional Designer to flesh out the important pieces, discern what is valid and what isn’t, confirm accuracy of content, and produce the final product for sign-off.” The analysis and design of learning objectives, assessments, and activities remain human-driven tasks that AI cannot reliably handle alone.
“Uncanny Valley” Effect in Videos: Learners can be distracted by AI-generated avatars if they appear unnatural. Early experiences with AI presenters have sometimes been negative – one practitioner found an AI narrator so “uncanny-valleyish” that learners focused on the weird talking head and ignored the content. Synthesia and similar tools are continually improving in realism, but subtle facial expressions and perfectly natural speech remain challenging for AI. This means for now, critical training (especially on soft skills or sensitive topics) may still benefit from a real human presenter to better connect with the audience.
Lack of Creative Problem-Solving: AI automates production but doesn’t innovate training strategy. It can churn out a generic course outline or a template-based video, but designing a truly engaging learning experience often requires creative approaches (storytelling, humor, interactive elements) that come from human insight. As multimedia designer Stephen O’Hearn points out, AI can generate assets (outlines, graphics, voice-overs) in minutes, yet “none of these services can do the job independently, just yet” . The instructional designer’s creative input is needed to assemble those pieces into a coherent and impactful course.
Impact on the Instructional Designer’s Role (Corporate): Rather than outright replacing IDs, AI is reshaping their role and skills in corporate environments. Routine production work (like video editing, formatting slides, or coding eLearning interactions) can be offloaded to AI, freeing designers to focus on higher-level tasks. IDs are becoming “instructional producers” who utilize AI as part of their team. They delegate media creation to specialized AI tools and then direct and integrate the outputs into a learning product. In practice, an instructional designer might use:
ChatGPT for Research & Drafting: to gather information or generate a first draft of a course outline or quiz questions, which the ID then refines.
Design AI (e.g. Lovable) to prototype a training app interface or branching scenario quickly, which the ID then customizes for the audience.
Video AI (e.g. Synthesia) to produce a training video, while the ID ensures the script is instructionally sound and the visuals align with learning points.
By leveraging these tools, a single designer can produce more content in less time. Many corporate IDs report that AI “increases efficiency, speeds up video production, [and] generates creative ideas,” allowing them to deliver projects faster and even improve content personalization. In fact, nearly half of instructional design professionals already use AI on a daily basis, and ~30% use it weekly, according to a 2024 industry survey. This surge in AI adoption means corporate L&D teams may not need as many people for labor-intensive tasks, such as multimedia development; however, they still require designers to guide strategy, interface with stakeholders, and ensure the training aligns with business goals.
Importantly, corporate IDs who upskill in AI can become more valuable. Those fluent in using AI tools can handle larger workloads or tackle more complex projects, which may reduce the need to outsource video production or graphic design. Rather than eliminating the ID role, companies are seeing the role evolve: less time spent in PowerPoint or Adobe Premiere, more time in consultation with subject-matter experts (SMEs), analyzing learning needs, and fine-tuning AI-generated content. In other words, the human designer’s job is shifting toward being an orchestrator of AI outputs and a curator of learning experiences, ensuring the final product truly solves the performance problem at hand.
AI in Higher Education: Augmentation for Course Design and Delivery
In higher education, the adoption of AI tools like Lovable and Synthesia is also underway, albeit at a more gradual pace. Universities face pressure to expand online offerings and update digital course content regularly – an area where AI can help faculty and instructional design staff save time. A notable example is Maryville University, which integrated AI video generation to scale up its online course production. Maryville’s learning design team created 85+ video lectures for 11+ online courses in under 8 months, achieving roughly 35% time savings in video production . They did this by using Synthesia’s avatars to deliver lecture content, allowing busy professors to “present” material without being on camera. The team can script a lecture, choose an AI avatar (even one resembling the professor), and produce a video lesson far faster than scheduling studio recordings .
Use Cases in Higher Ed: Early use cases show AI assisting rather than replacing educators:
Lecture Videos and Micro-Lectures: Faculty or instructional designers can feed text (a lecture script or key points) into an AI like Synthesia to create short explainer videos. This is especially useful for online programs where faculty availability is limited – the AI avatar can deliver content at any time. Maryville’s team adopted this approach to update course materials quickly; when a textbook or regulation changed, they could generate a new video module within hours, rather than waiting weeks for a professor to film updates. Some professors are even creating avatars of themselves to maintain a personal presence – the avatar looks and sounds like the instructor, but presents perfectly on script every time. This approach can also help instructors who aren’t comfortable on camera produce more polished video lectures.
Multi-language Content & Accessibility: Universities with diverse student bodies see potential in AI translation and dubbing. An English lecture can be auto-translated and voiced over in languages such as Spanish, Mandarin, etc., thereby expanding accessibility for non-native speakers. Similarly, AI can generate captions and even sign-language avatars, improving accessibility compliance. These tasks would be labor-intensive manually but scale easily with AI.
Course Website and UX Design: Tools like Lovable (and similar AI prototyping platforms) allow for the quick creation of course websites or interactive study tools. For example, an instructional designer in an online program might prompt Lovable to “build a minimalistic course homepage with a week-by-week module layout, a discussion forum section, and an embedded video player,” and get a functional web page instantly. This reduces dependence on campus IT for custom learning tools. It’s essentially no-code development for education: a tech-savvy ID or even a graduate assistant could spin up a new practice quiz app or a virtual lab interface using AI. This can spur innovation in teaching methods by lowering the barrier to trying new digital learning activities.
Limitations and Academic Concerns: Although these tools assist in course development, they are unlikely to replace human instructors or instructional designers in academia anytime soon. There are several reasons for this:
Maintaining Academic Quality: University curricula require rigorous accuracy and alignment with learning outcomes to ensure academic excellence. AI-generated content (whether text or video) can contain errors or pedagogical gaps if not carefully reviewed. Faculty and IDs must vet any AI-produced lecture or quiz for correctness and academic rigor. As learning scientist Dr. Philippa Hardman notes, AI can generate course materials quickly, but human oversight is crucial to ensure that those materials meet high teaching standards and truly facilitate learning. In practice, Maryville’s team still went through “many rounds of iterations (video content, pronunciation, etc.)” when first using an AI video tool, adjusting and refining the output to meet their quality bar. This highlights that AI output often needs iterative human editing – it’s not a simple one-click solution for polished educational content.
Student Engagement and Trust: Learning is fundamentally a social, interactive process, and students may be less engaged by AI-delivered instruction if it lacks authenticity or the ability to respond to questions. There is a question of student acceptance: would learners feel shortchanged if, for example, an entire course’s lectures were AI-generated with no real professor involved? A provocative vision by one ed-tech commentator imagines “developing a whole course without a faculty member,” using GPT-generated content and avatar instructors . While technically possible, most higher-ed experts doubt that fully automated teaching could replace the mentorship and expertise a professor provides. Human instructors guide discussion, provide nuanced feedback, and adapt to student needs in ways current AI cannot. Thus, wholly AI-driven courses are likely to face resistance from both faculty (many of whom view teaching as their domain) and students who value human connection.
Ethical and Integrity Issues: In academia, the use of AI must be transparent and conducted in an ethical manner. Universities are cautious about dishonesty or “deepfake professors.” If an avatar stands in for a lecturer, students should be aware that the instructor should still be accessible for interaction. Moreover, over-reliance on AI content generation could raise concerns about academic integrity (e.g., is the course content original, or just auto-generated text from GPT?). Instructional designers in higher ed will play a key role in setting guidelines for AI use, ensuring it supplements rather than deceives or dilutes the learning experience.
Evolving Role of Instructional Designers in Higher Ed: Instructional designers working in colleges and universities are increasingly becoming consultants and facilitators for AI integration. Rather than manually building every course component, they might train faculty to use these AI tools effectively, curate the AI-generated materials, and focus on the overall learning architecture. In essence, their role shifts more toward academic project managers and quality consultants:
They help professors identify where AI can save time (e.g., converting a syllabus into a video overview) and where a personal touch is non-negotiable (e.g., facilitating discussion forums or mentoring student research).
They establish best practices for AI-generated content, such as checking that an AI-produced lecture adheres to the syllabus and contains no factual errors or bias.
They may also tackle new responsibilities like AI ethics training, ensuring that both faculty and students understand the proper use of generative AI (for instance, to prevent misuse like having AI do all of a student’s assignments).
In higher education, AI tools augment the instructional designer’s toolkit but do not obviate the need for their expertise. As one medium-term forecast, we might see fewer routine media production tasks for IDs (since AI can handle those), but more demand for IDs as integrators, trainers, and quality assurance experts for instructional technology. The number of ID positions in academia might not shrink, because the push for more online and hybrid learning continues to grow, but the competencies required will evolve. IDs will be valued for their ability to blend AI efficiency with human pedagogical wisdom, ensuring that technology serves learning rather than just automating it.
General Industry Trends and Future Outlook for Instructional Design
Across corporate and educational sectors, the instructional design industry is experiencing a transformation rather than an extinction. The consensus from many experts is that AI will change how instructional designers work, but not eliminate the profession. In fact, a large 2024 survey of 400+ instructional design professionals found that 85.4% of IDs are not worried about losing their jobs to AI in the next three years . Instead, a strong majority (79%) are excited about AI and see it as a tool that can enhance their work or even boost their career prospects . This optimism is tied to the understanding that while AI can automate production, it still lacks the uniquely human skills that effective instructional design requires.
Current Adoption and Attitudes: Rapid adoption of AI tools is already underway among instructional designers:
The most popular AI tools in use include ChatGPT (used by ~91% of IDs) for tasks like brainstorming and writing assistance, as well as tools for AI audio narration (~36%), image generation (~22%), and video generation (~16%), according to industry research. Designers primarily use these tools to save time (cited by 50% of IDs) and to generate creative ideas (29%), with a smaller portion even stating that AI helps them create higher-quality output. In other words, many IDs are already leveraging AI to work faster and augment their creativity, rather than to replace their own thinking.
Content authoring software is also beginning to integrate AI features. For example, major eLearning tools like Articulate and Adobe are rolling out AI aids that can automatically draft quiz questions, summarize content, or transform text into a video at the click of a button . These embedded AI features will make it commonplace for IDs to have an AI “assistant” within their standard workflow. Being adept at using those assistants will be key; as one LinkedIn commentator noted, “it’s only a matter of time before companies like Adobe or Articulate incorporate these services into their offerings,” and IDs will be expected to use them for efficiency.
Evolving Skills and Roles: As repetitive tasks diminish, instructional designers are refocusing on high-value skills that AI cannot easily replicate. Several experts have coined new role descriptors to capture this shift. Stephen O’Hearn describes the emerging role as “Instructional Producer,” where the designer’s job is to direct AI tools, orchestrating the content creation process much like a film producer, rather than hand-crafting every element. The ID becomes the vision-holder, ensuring the learning experience is coherent and effective, while delegating portions of development (writing drafts, creating graphics, programming interactions) to AI collaborators. This requires skills in prompt engineering (i.e., knowing how to ask AI for the desired output), critical evaluation of AI outputs, and the ability to integrate various AI-generated pieces into a polished learning product.
Crucially, instructional designers will need to double down on skills that involve human judgment and empathy. These include:
Needs Analysis & Performance Consulting: Determining the real problem to solve and the requirements of the audience. AI can assist by analyzing data, but deciding training strategy (or even deciding if training is the solution) requires consulting with stakeholders and understanding human factors – the strengths of a human ID .
Creative Instructional Strategy: Innovating engaging learning activities, stories, and examples. AI can supply raw ideas, but selecting the right approach for a given audience (and doing so within organizational constraints and culture) is a designer’s art.
Quality Assurance & Ethics: Verifying the accuracy of content, ensuring representation and tone are appropriate, and using AI ethically (e.g., avoiding biased content). The human ID must oversee these aspects, as AI has no built-in ethical compass or domain expertise beyond its training data.
Facilitation and Coaching: Many instructional designers also serve as facilitators or create instructor-led training. The interpersonal skills to lead a workshop, adapt on the fly to learner questions, or coach a subject-matter expert in teaching – these remain uniquely human capabilities that AI avatars or chatbots cannot fully emulate.
Limitations Preventing Full Replacement: Despite rapid advances, current AI has inherent limitations that make a complete replacement of instructional designers unlikely in the near term:
AI lacks the contextual understanding and critical thinking necessary for effective decision-making. It might generate a plausible lesson on a topic, but it doesn’t truly understand the learners’ context or the nuance of the subject matter. It might miss subtle prerequisites or misconceptions that a skilled educator would catch. As one ID professional put it, “AI tools cannot and should not replace analysis, research, or design” – they might create outputs that look decent, but an experienced designer knows when something is instructionally unsound or off-target. In short, AI can draft content, but it takes a human to determine if that content actually teaches effectively.
Many AI tools address the “how” of content (format, media) but not the “why.” They excel at producing materials (videos, slides, text) but struggle with identifying goals or learning gaps. An instructional designer starts by asking, “What should learners be able to do after this training?” and “What’s the best way to get them there?” – strategic questions far upstream of content generation. AI does not inherently handle these design decisions. Even the most advanced generative systems work from prompts that a human provides; thus, the vision and pedagogy come from the designer. This aligns with findings that interest in AI among IDs is especially high for analysis-phase tasks (like needs analysis and learner data analytics) , where AI is seen as a tool to enhance the designer’s own analysis rather than replace it.
Future Outlook – Collaboration Between AI and IDs: Looking ahead, the career of instructional designers is expected to evolve in tandem with AI, not vanish. As AI capabilities grow, some sub-tasks of instructional design may become fully automated – for instance, it’s conceivable that AI could eventually generate a competent first draft of an entire eLearning course, complete with narrated slides and quiz questions. In fact, experiments have already shown that with the right prompts, GPT-4o can spit out a course outline and content, which can then be turned into videos by tools like Synthesia. However, these automatically generated courses would be generic and impersonal without a designer’s touch.
The likely scenario is a “centaur” model (human + AI partnership), where instructional designers leverage AI to be dramatically more productive and to create learning experiences that were previously too labor-intensive to build.
For example, adaptive learning programs that tailor content in real-time or rich scenario-based simulations could be developed more easily with AI support. This means that IDs who are fluent in AI will have more time to innovate and focus on learner experience, rather than spending time on routine work. One survey found that 44% of instructional designers believe AI will help increase their income, presumably by enabling them to deliver more value or take on more projects, compared to only 17% who fear a negative impact. In essence, those who incorporate AI into their skill set are positioning themselves for the next generation of instructional design roles.
To stay relevant, instructional designers are advised to adapt and upskill rather than resist the change. You must “future-proof your career by using AI tools wherever you can” – not to do all the work for you, but to augment your capabilities. This may involve training in data literacy, learning experience design, and proficiency with AI tools. Indeed, new specializations are emerging, such as Learning Experience Designers (LXD) who focus on the holistic learner journey (often leveraging AI-driven personalization), or AI Learning Consultants who help organizations implement AI in L&D strategies. These roles still fundamentally require instructional design expertise, wrapped in new technical skills.
Expert Opinions Summarized: Multiple thought leaders in the field conclude that AI will not outright replace instructional designers, but it will redefine excellence in the field:
A Medium article by learning designer Damla Surek asserts that AI will “neither replace instructional designers” nor make them obsolete; rather, IDs may evolve into roles akin to project managers for learning, overseeing AI contributions. Specialists will always be needed to ensure training aligns with human cognitive needs and organizational context.
Similarly, our research emphasizes that “AI can’t reliably produce high-quality learning experiences alone – your involvement is completely needed.” He foresees IDs spending less time on hands-on production (like writing or media development) and more time on strategic activities like interviewing stakeholders, validating content, and leveraging AI outputs to craft better solutions .
On LinkedIn, practitioners have discussed that the advent of AI is finally pushing IDs to “quit [our] content fetish” and concentrate on the “neglected bits like job task analysis, skills validation, [and] formative assessments.” In other words, the value of an instructional designer will lie in those deeper design tasks that ensure training actually improves performance, something AI on its own cannot guarantee.
Conclusion
AI-powered tools like Lovable and Synthesia are game-changers in how training content is developed, but they are unlikely to render human instructional designers obsolete. In corporate training, these tools are boosting efficiency, allowing for the faster creation of videos and digital learning experiences, while freeing designers to focus on analysis, creative design, and aligning learning with business objectives. In higher education, AI helps scale online learning and alleviate the burden on overburdened faculty, yet the expertise of educators and instructional design staff remains critical to maintaining academic quality and fostering meaningful student engagement.
The overall trend in the instructional design industry is one of augmentation and evolution: AI handles the heavy lifting of content production and provides data-driven insights, whereas human designers provide direction, insight, and quality control. The need for instructional design is not disappearing – if anything, it’s growing more sophisticated. Instructional designers who adapt by mastering AI tools will thrive as they can deliver more value and tackle more ambitious learning projects. Those who cling to traditional, manual ways may find their skillset in less demand, but the profession as a whole is poised to continue (and even expand) in partnership with AI.
In summary, AI services like Lovable and Synthesia are best seen as powerful new assistants rather than outright replacements. They change the tools we use and the tasks we do, but the core mission of instructional design – solving learning and performance problems through effective, human-centered solutions – remains. As one expert aptly put it, “our roles are evolving” with AI, and the instructional designers of the future will be part designer, part technologist, and part strategist . Embracing that evolution ensures that we continue to design learning experiences that are not just efficiently produced, but effective, engaging, and truly transformative for our learners in the age of AI.
Discussion Assignment:
Join the conversation and participate with the 24/7 Instructional Design community by completing the discussion question and adding your answer in the comment section below:
In a world where AI can generate entire training modules in minutes, how can instructional designers ensure their human-centered expertise remains visible, valued, and indispensable in the learning experience?
Need Guidance on Navigating the Shift to Instructional Design?
Before You Go...
Discover the Unmatched 24/7 Teach Experience:
Our Instructional Design bootcamps and career coaching services have a 100% success rate. We redefine learning by immersing you in practical, hands-on projects, ensuring you acquire vital professional expertise while making a meaningful difference in your community.
Unlock your true potential today with 24/7 Teach and invest in your future.
or try a prompt below!
