*Contributing authors: Dr Lisa Evans (Academic Director), David Haigh (LDT Faculty), Trudy Blackmon (Instructional Designer), Heather Leslie (Instructional Designer), Jeff Simmons (Instructional Designer).
Have you ever wondered how much time you could save if artificial intelligence (AI) handled some of the more tedious aspects of instructional design? AI is transforming the creation and delivery of educational content by automating routine tasks and providing personalized learning experiences. Imagine creating a 30-minute training video in as few as 10 minutes — AI makes this possible.
This post will explore the various applications of AI in instructional design, examine the benefits and risks and highlight some of the most effective tools available. Keep reading to discover how AI can improve workflows and educational impact.
The Role of AI in Automating Content Creation
AI is revolutionizing instructional design by automating time-consuming tasks in course and content creation. This technology enables educators to develop high-quality materials quickly, allowing more time for crafting engaging learning experiences.
Practical Applications: What Tasks Can AI Help With?
AI can streamline the instructional design process by taking over a range of routine to complex tasks
- Content generation: AI can automate the creation of instructional materials and assessments. For example, ChatGPT and Microsoft’s Copilot can generate scripts and storyboards, dramatically reducing the time needed to develop training videos from hours or days to just minutes.
- Adaptive assessments: AI can develop and grade tests that adapt to student performance. This allows for personalized and responsive assessments, ensuring that each student is challenged appropriately and supported where needed.
- Learner engagement: AI-driven tools can provide real-time feedback and support, including instantly responding to student inquiries, facilitating interactive learning experiences and keeping learners engaged through personalized content delivery.
Optimizing AI Use: Effective Prompts and Results
To maximize the benefits of AI, you need to understand how to use AI tools effectively. The right prompts can lead to high-quality educational content and improved learning outcomes.
- Effective prompting: When generating content, prompts should be detailed and specific to guide the AI in producing relevant and accurate material. An example of an effective prompt could be:
“Acting as an expert on workplace safety, create a detailed storyboard outline for a 30-minute training video on workplace safety aimed at new employees in a manufacturing facility. The video should include the following key topics:
– Introduction to workplace safety
– Personal protective equipment
– Safe machine operation
– Emergency procedures
– Incident reportingEach section should have clear learning objectives and engaging visual elements.”
AI tools like Microsoft Copilot for 365 can take similar prompts to generate complete PowerPoint presentations, which can be further customized and even exported as videos, making them versatile for various learning formats and audiences.
- Example outputs: Reviewing the results from different prompts can help instructional designers understand which prompts yield the best results. Reviewing your responses can be as simple as keeping a running list in your Notes app or logging your prompts and responses in a spreadsheet to track over time. Using a detailed prompt like the one above can result in a comprehensive and structured storyboard, demonstrating the AI’s capability in content generation.
On the other hand, using a more general or unclear prompt will often result in a broad and less detailed response.
By leveraging AI’s potential in these areas, instructional designers can enhance their workflows, allowing more time to focus on creative and strategic aspects of instructional design.
3 Ways AI Supports Instructional Design
AI brings many benefits to instructional design, from automating mundane tasks to providing personalized content at scale.
1. Increased Efficiency
AI reduces time spent on administrative and repetitive tasks, enabling instructional designers to focus on developing high-quality learning experiences. AI can automate the creation of detailed lesson plans and interactive learning modules, significantly reducing preparation time.
Tools like ChatGPT can analyze curriculum guidelines instantly and recommend structured lesson plans within minutes, saving hours of manual work for instructional designers. This efficiency allows instructional designers to allocate more time to the creative and strategic aspects of instructional content development, such as designing engaging activities and assessments that improve learner outcomes.
2. Enhanced Personalization
While instructional designers have long analyzed learner performance data to personalize instruction, AI takes this to a new level by providing real-time, data-driven insights that allow for immediate, precise adjustments.
Continuous performance analysis enables AI to adapt content and assessments instantly to ensure each learner receives the appropriate level of challenge and support. An AI tool, such as Hyperspace, can suggest personalized learning paths based on a learner’s progress, which can help instructional designers craft content that addresses specific knowledge gaps more quickly than traditional methods.
3. Greater Scalability
AI enables instructional designers to support a large number of learners without compromising educational quality. AI-powered adaptive learning platforms can provide real-time, individualized feedback and support thousands of learners simultaneously. This scalability ensures that instructional designers can maintain high standards even in large-scale programs or corporate training environments where resources may be limited.
By automating routine tasks and providing personalized support at scale, AI enables instructional designers to manage and enhance learning experiences for a growing number of participants.
7 Challenges and Ethical Issues Associated with AI in Instructional Design
Integrating AI into instructional design is not without its challenges and ethical issues to consider. Concerns about ethical implications, over-reliance and the quality of automated tasks need careful consideration.
1. Data Privacy and Safety Concerns
Generative AI raises significant data privacy and safety concerns, including the collection and use of sensitive or personal data without consent, potential exposure of proprietary information and risks of data breaches or malicious exploitation. Users interacting with AI tools may unknowingly share confidential data, while the opaque nature of AI processes complicates transparency and regulatory compliance.
To mitigate these risks, robust security measures, data minimization, transparency and adherence to privacy laws like GDPR and CCPA are essential to ensure ethical and safe AI usage. Organizations should set clear guidelines and policies on data use and continuously monitor AI systems to protect learner privacy.
Instructional designers are critical in this process. Here’s how they can help:
- Develop and enforce clear privacy policies for data use, storage and sharing. Ensure these policies are transparent and available to all learners.
- Implement informed consent mechanisms. Clearly explain data usage, its purpose and benefits to learners.
- Only use the data necessary for instructional purposes. Avoid excessive data collection.
- Educate instructors and designers on data privacy and security practices to prevent breaches.
- Use anonymization techniques in research or analysis to protect sensitive information.
- Carry out regular security audits to identify and address any system vulnerabilities.
2. Copyright Issues
The data used to train AI models have been scraped from the Internet including copyrighted works from authors, poets, scholars, artists and creators without their knowledge or consent. It’s been called “systematic theft on a mass scale” and AI companies are facing litigation for copyright infringement. This raises ethical, legal and academic integrity concerns, as companies profit from AI models trained on data produced by humans without giving proper credit or compensation to the original creators.
3. Exploited Labor Used to Train AI Models
AI systems rely on the labor of millions of underpaid workers worldwide, who perform repetitive tasks to filter the data under precarious conditions and are often exposed to traumatizing content. These workers—often from impoverished communities—are paid as little as $1.46 per hour after taxes. Despite their critical role, the exploitation of this labor remains largely absent from discussions on the ethical development and deployment of AI systems.
4. Bias Baked Into AI
AI produces bias because it is trained on large datasets that often reflect existing societal biases. If the data contains stereotypes, prejudices or imbalances, the AI will learn and replicate these patterns in its outputs. This can reinforce harmful stereotypes and lead to discriminatory outcomes, particularly against marginalized or underrepresented groups.
Additionally, biases can arise from the design of the algorithms, the decisions made during model training and a lack of diverse representation in the data, further amplifying inequities in areas like hiring, law enforcement and access to resources.
Some studies have shown that bias in AI is worse than human bias. But unlike human bias, AI bias feels more objective and can scale rapidly, spreading distorted representations that compound over time, as these AI-generated outputs may feed into future models.
5. Dependence on Technology
One potential consequence of reduced human involvement in educational processes is over-reliance on technology. While AI can handle many tasks efficiently, it lacks the creativity and nuanced understanding that human educators provide. AI can generate content, but may not always expand on it creatively; necessitating human oversight to ensure quality and relevance.
6. Quality Concerns
AI’s ability to fully understand complex educational or training content and contextual nuances is limited. For instance, when generating training materials, an AI tool may include irrelevant or inappropriate citations. This highlights the need for instructional designers to carefully review and validate AI-generated content to maintain high standards of educational quality.
Instructional designers must ensure that AI-generated outputs align with the specific learning objectives and contextual requirements of their training sessions. Large Language Models are not knowledge bases. They are predictive text generators. Therefore, it may require more time to verify all the information that AI generates is accurate than creating the content yourself based on verified sources.
Be strategic in how you use AI for different tasks. (e.g., professional boilerplate verbiage for things like generating memos is great for AI but using AI to write an academic article citing the latest research may contain inaccuracies and can limit the writer’s ability to think deeply, synthesize information and form new insights during the writing process).
7. Environmental Harms and Risks
AI poses several environmental risks, primarily due to its high energy consumption for training models and operating data centers, which contribute to significant carbon emissions. The production of specialized hardware for AI, such as GPUs, also leads to resource depletion and e-waste, as these devices require rare-earth metals and often result in environmental degradation from mining.
Additionally, the growing demand for AI infrastructure could exacerbate these environmental impacts unless energy-efficient solutions and renewable energy sources are prioritized. While AI holds potential for promoting sustainability, its current environmental costs highlight the need for more eco-friendly approaches in its development and deployment.
Top 7 AI Tools for Instructional Design
Numerous AI tools can help improve the instructional design process. Here are some of the best tools currently on the market, categorized by their functionalities and ideal use cases.
Content Generation Tools
ChatGPT
ChatGPT by OpenAI is a natural language processing tool that excels at generating conversational and contextually relevant responses. Instructional designers can use ChatGPT to ideate design ideas, research topics and create prototypes. Its latest version, ChatGPT-4o, can reason across audio, vision and text in real-time, making it a versatile tool for various design tasks. This version offers enhanced capabilities and continuous updates to address potential biases and ensure ethical use.
Top features:
- Easy to use for a wide range of tasks
- Delivers conversational text responses
- Continuous improvement and ethical considerations
MidJourney
MidJourney is an AI art generator that translates text into images, enabling designers to create visual content for prototypes and references quickly. Its ease of use and features, such as “Image Remix” and “Inpainting,” eliminate several design steps, making it a powerful tool for visual content creation.
Top features:
- Transforms text into stunning visuals
- Allows for real-world photo transformation
- Includes advanced image editing features
DALL-E 2
DALL-E 2 by OpenAI creates realistic images and art from text descriptions. It supports various painting styles and can expand pictures beyond the initial canvas, making it ideal for creating unique and detailed visuals.
Top features:
- Easy to use and user-friendly
- Supports different painting styles
- Allows for image variations while maintaining style consistency
Adaptive Learning Platforms
Canva
Canva is an online design platform that has integrated AI tools to enhance its functionality. It offers thousands of customizable templates and easy-to-use tools that rival traditional design software. Features like “Magic Resize” automatically adjust graphics to different sizes, simplifying the design process.
Top features:
- Thousands of customizable templates
- Advanced editing tools
- Magic Resize for automatic adjustments
Jasper.ai
Jasper.ai offers both AI art generation and a writing tool for generating instructional content. It includes features like plagiarism checking, brand voice customization and various copy templates to streamline content creation.
Top features:
- Plagiarism checker and revision history
- Brand voice customization
- Numerous copy templates for different needs
Assessment Engines
Adobe Sensei
Adobe Sensei is embedded within Adobe’s Creative Cloud suite and offers AI features that enhance content creation and management. It analyzes text to understand tone, summarizes content and accelerates business processes relevant to instructional designers, such as automating image tagging, recommending design elements and optimizing layouts.
Top features:
- Integrated into Adobe Creative Cloud
- Analyzes and summarizes text
- Automates image tagging and recommends design elements
- Optimizes layout designs for better visual communication
Adobe Firefly
Adobe Firefly, another AI feature within Adobe products, transforms text prompts into images, alters typography and recolors designs. The Generative Fill feature allows users to select areas and fill them with specific prompts, enhancing creativity and efficiency.
Top features:
- Text-to-image generation
- Typography and style transformations
- Generative recoloring
AI-Integrated Learning Experiences
AI is changing administrative aspects of instructional design and directly enhancing the learning experience. By integrating AI technologies into learning environments, educators can improve the effectiveness and engagement of their instructional methods.
Here’s a closer look at three AI subfields and how to incorporate them within instructional design.
- Machine learning can help personalize learning experiences by analyzing data to adapt content and assessments uniquely for each student. By continuously learning from student interactions and performance, machine learning algorithms can adjust the difficulty of tasks, recommend resources and provide tailored feedback, enhancing the overall learning experience.
- Natural language processing (NLP) can provide real-time feedback, automate grading and facilitate sophisticated interactions between students and digital learning platforms. By understanding and processing human language, NLP can engage in meaningful dialogues with students, answer questions and offer explanations, creating an interactive and responsive learning environment.
- Data analytics can refine curriculum development through in-depth insights into learning patterns and outcomes. By analyzing vast amounts of educational data, AI can identify trends, predict learning challenges and inform instructional strategies, enabling educators to make data-driven decisions that improve student performance and curriculum effectiveness.
Integrating AI into Learning Experiences
Here are some specific ways to integrate AI technologies into learning experiences to improve effectiveness and engagement. Several of these categories can employ real-time experiences in asynchronous online courses, something difficult to achieve until now.
Personalized Learning
Personalized learning uses machine learning algorithms to analyze data and customize instruction to each student’s needs, preferences and skill levels. For example, in corporate training, AI can assess individual employee performance and suggest tailored training modules that address specific skill gaps.
Example: In corporate training settings, AI can assess individual employee performance and suggest training modules that address specific skill gaps.
Adaptive Learning
Adaptive learning employs machine learning to adjust the difficulty and pace of learning content based on student interaction, improving retention and engagement.
Example: The adaptive learning platform Realizeit can adjust the complexity of training materials in a professional development program based on real-time feedback from participants so each learner can progress at an optimal pace.
Conversational Interactions
Conversational interactions apply natural language processing to understand and respond to human language, enhancing engagement through dialogue-based learning.
Example: Tools like ChatGPT can convert spoken language into actionable data and personalized feedback, thereby creating interactive, responsive learning environments.
Further, they can enable language learners to engage in personalized, realistic, role-based, spoken conversations (e.g., as a customer at a café or airport) in many languages and proficiency levels while receiving immediate corrections for mistakes.
This functionality is very effective when accessed through dedicated mobile apps but is also compatible with personal computers (desktops and laptops), offering flexibility in learning environments.
Gamification
Gamification integrates machine learning to personalize game elements in learning, making it more engaging by adjusting challenges and rewards based on progress.
Example: Kahoot! and other gamification tools can customize quizzes and interactive activities to match the learner’s development, providing insights and tailored feedback to maintain engagement and reinforce learning objectives. Or you can simply enter a prompt for the AI to create turn-based games with clearly defined goals.
Predictive Analytics
Predictive analytics uses data analytics to analyze trends and forecast learning challenges, personalizing educational pathways by identifying and addressing potential obstacles.
Example: In biomedical training simulations, AI can predict areas where learners might struggle and adjust the curriculum in advance to provide additional support for a more effective and smooth learning experience.
Content Creation
Content creation automates the generation of educational materials using machine learning, making course development more efficient and tailored to student needs.
Example: Platforms like Articulate 360 streamline the creation of interactive content, adapting materials in real-time based on learner interactions and performance so that content remains relevant and effective.
Assessment and Feedback
Assessment and feedback employ natural language processing to provide automated, real-time feedback on student submissions, enabling instant instructional adjustments.
Example: TheAI-assisted tool Gradescope uses natural language processing to evaluate written responses and offer detailed feedback in order to help learners understand their mistakes and improve their skills.
Implementing AI in Instructional Design Workflows
Integrating AI into instructional design workflows requires identifying areas where AI can enhance efficiency, selecting the appropriate tools and continuously monitoring and adjusting their use. Below are two essential aspects to consider: the seven steps for integrating AI and the five best practices for successful implementation.
Frequently Asked Questions
Advance Your Instructional Design Skills with AI
AI holds transformative potential for instructional design, streamlining content creation, personalizing learning experiences and enhancing engagement. Embracing these advancements is crucial for staying relevant and effective in the field.
The University of San Diego’s Master of Science in Learning Design and Technology program offers a comprehensive pathway to dive deeper into instructional design, equipping professionals with the skills needed to harness AI technologies.
To learn more about advancing your career with AI in instructional design, download the eBook, 9 Things to Know About Careers in Instructional Design.