The Future of Learning Design: Where AI Meets ADDIE

By the end of this lecture, you will be able to:

1. Differentiate between the capabilities of AI and human educators in the context of learning design.

2. Evaluate the strengths and limitations of AI tools in supporting personalized and scalable learning experiences.

3. Discuss the role of emotional intelligence, creativity, and ethical considerations in human-centered education.

By the end of this lecture, the student will be able to:

• Understand how AI can support teachers in designing learning experiences.

• Recognize how AI can be a team member in the learning design process.

After this lecture, the you will be able to:

• Describe key opportunities AI brings to learning design.

• Recognize major trends from Coursera, Microsoft, LinkedIn, and WEF.

• Identify AI tools used in content creation, personalization, and assessment.

• Explain how AI enhances learner engagement and accessibility.

After this lecture, you will be able to:

1. Understand the limitations and benefits of using AI in learning design.

2. Recognize the importance of data privacy, ethics, and fairness in AI tools.

3. Identify and address bias in AI-generated content.

4. Appreciate the value of human judgment alongside AI.

5. Know when to use AI and when to rely on human expertise.

By the end of this lecture, you will be able to:

• Apply the HIRE framework to evaluate AI tools for learning design.

• Investigate an AI tool’s background and effectiveness.

• Write clear instructions to guide the AI tool outputs.

• Assess the efficiency and value of AI tools in education.

By the end of this lecture, you will be able to:

• Understand the five stages of the ADDIE instructional design model.

• Recognize how AI tools can enhance and streamline each phase of ADDIE.

• Identify practical ways to integrate AI into course design and development processes.

By the end of this lecture, you will be able to:

• Design and distribute effective learner surveys using both quantitative and qualitative questions.

• Use AI tools to support data collection and gain deeper insights into learner needs and preferences.

• Communicate instructional goals clearly to learners to enhance participation and course relevance.

By the end of this lecture, you will be able to:

• Prepare and clean learner survey data for AI analysis.

• Use AI tools to extract meaningful patterns, preferences, and challenges from the data.

• Translate AI-generated insights into actionable decisions for learner-centered course design.

By the end of this lecture, you will be able to:

• Analyze real learner feedback (e.g., survey responses) to uncover key insights.

• Upload and process survey data using ChatGPT without needing a background in data science.

• Know the difference between ChatGPT versions.

• Prompt ChatGPT effectively to extract meaningful results from raw feedback.

• Apply findings to improve your course design and learning experience.

• Understand how to use ChatGPT Plus (GPT-4 Turbo) with features like file uploads and advanced analysis tools.

By the end of this lecture, you will be able to:

• Evaluate the accuracy, clarity, and relevance of AI-generated insights from learner data.

• Test and refine AI prompts to assess tool responsiveness and adaptability.

• Use AI-generated analysis to support informed and effective instructional decisions.

By the end of this lecture, you will be able to:

• Understand the components of an effective AI prompt for educational design tasks.

• Write structured, clear prompts that yield high-quality, instructionally sound AI responses.

• Customize AI prompts to suit specific audiences, subjects, and instructional goals.

By the end of this lecture, you will be able to:

• Understand the role of the Design phase in shaping course structure and objectives.

• Recognize the importance of the Teaching Statement and Learner Survey Results in guiding course design.

By the end of this lecture, you will be able to:

• Explain the role of a Custom GPT in the Design phase of the ADDIE model.

• Set up and configure a personalized GPT to support instructional design tasks.

• Prompt your assistant to generate measurable learning objectives that reflect your teaching philosophy and learner needs.

By the end of this lecture, you will be able to:

• Recognize the importance of enhancing learning objectives beyond basic descriptions

• Apply refined prompting strategies to generate clearer, more aligned learning outcomes using AI

• Distinguish between general and performance-based objectives using Bloom’s higher-order verbs

• Use enhanced objectives to build a structured, real-world, and relevant course plan

By the end of this lecture, you will be able to:

• Identify how AI can enhance lesson planning, content creation, feedback, and student engagement in your teaching practice.

• Apply the S.C.E.S.P. framework (Save time, Create better content, Engage students, Simplify research, Personalize learning) to select AI tools that align with your instructional goals.

• Evaluate AI tools using five key criteria: privacy and security, ease of use, scalability, ethics and fairness, and time-saving value.

• Explore practical examples of AI tools (e.g., ChatGPT, Perplexity, Curipod, Synthesia) that support key teaching tasks, from designing lessons to generating assessments.

By the end of this lecture, you will be able to:

1. Understand the role of the Development phase in the ADDIE model and how AI accelerates content creation.

2. Use AI tools like ChatGPT to generate a motivational, learner-centered video script.

3. Transform your AI-generated script into a visually engaging presentation using Gamma.app.

4. Customize and enhance your slide deck with themes, visuals, and interactive elements.

5. Explore options for converting your presentation into an AI-narrated video using tools like Synthesia or HeyGen.

By the end of this lecture, you will be able to:

• Identify key types of learning activities and explain how they promote active engagement and applied learning.

• Use AI to create realistic and relevant case studies by combining research tools like Perplexity AI and creative design tools like ChatGPT.

• Design skill-based exercises and coding tasks aligned with learning objectives, leveraging AI to provide examples, instructions, and evaluation criteria.

• Generate multiple-choice questions and formative assessments using AI that assess comprehension, apply Bloom’s taxonomy, and provide structured feedback.

By the end of this lecture, you will be able to:

• Explain the role of rubrics in the implementation phase and how they ensure assessment aligns with learning objectives.

• Design clear, measurable analytic rubrics that define criteria, performance levels, and actionable feedback for students.

• Leverage AI to refine rubrics and generate structured feedback, improving grading consistency, clarity, and instructional efficiency.

• Integrate AI-enhanced rubrics into LMS platforms and use rubric data to identify learning trends, guide remediation, and support proactive instructional improvement.

By the end of this lecture, you will be able to:

• Explain the purpose of the Implementation phase in the ADDIE model.

• Identify how course plans and materials are delivered to learners.

• Recognize the instructor’s role in facilitating the learning experience.

By the end of this lecture, you will be able to:

• Identify the key actions required to successfully implement a course.

• Describe how instructors support and monitor learners during course delivery.

• Recognize the importance of feedback and continuous improvement during implementation.

By the end of this lecture, you will be able to:

• Identify how AI enhances the evaluation phase of the ADDIE model by analyzing learner data, detecting patterns, and generating actionable insights.

• Apply AI-powered tools and workflows to evaluate learning experiences, improve course effectiveness, and support data-driven decision-making.

By the end of this lecture, you will be able to:

• Explain how AI is shaping the future of learning design by enabling personalization, automation, and data-driven decision-making.

• Recognize the opportunities and challenges of using AI in learning design, including adaptive learning, learner support, and ethical considerations.