Design Blended Learning Experiences
In ChatGPT or your favorite AI chatbot, cut and paste the following prompt to help you adapt traditional learning experiences into effective blended learning experiences. To get started, simply replace each bracket with the relevant information for each section.
Note: This prompt will work best if you upload the learning experience you are working with.
Design Blended Learning Experiences Prompt
You are an experienced instructional design specialist and [GRADE LEVEL/SUBJECT AREA] curriculum expert, skilled in transforming traditional lesson plans into effective blended learning experiences. Your task is to help me redesign my existing [LESSON/UNIT/MODULE] on [TOPIC] to maximize the benefits of both online and face-to-face instruction within a blended learning framework.
My current [LESSON/UNIT/MODULE] includes [DESCRIBE CURRENT INSTRUCTIONAL ACTIVITIES] and typically takes [TIME DURATION] to complete. The learning objectives focus on [LEARNING OBJECTIVES/STANDARDS].
Please provide a comprehensive transformation plan that restructures this content to:
[TRANSFORMATION GOAL 1 - e.g., move foundational content to online pre-learning]
[TRANSFORMATION GOAL 2 - e.g., redesign face-to-face time for active application]
[TRANSFORMATION GOAL 3 - e.g., create flexible pacing options for diverse learners]
The transformation should follow this blended learning structure:
Online preparation phase
Face-to-face application phase
Online extension/reflection phase
Ongoing formative assessment throughout.
In your response, include specific guidance on content delivery methods, activity redesign strategies, assessment modifications, and technology tool recommendations that will support this blended approach while maintaining rigorous learning outcomes.
Example Prompt
You are an experienced statistics instructor at a community college, skilled in transforming traditional lesson plans into effective blended learning experiences. Your task is to help me redesign my existing unit on measures of central tendency and variability to maximize the benefits of both online and face-to-face instruction within a blended learning framework.
My current unit includes traditional lectures on mean, median, mode, range, and standard deviation, followed by homework problem sets from the textbook and typically takes two weeks to complete. The learning objectives focus on students being able to calculate and interpret measures of central tendency and variability, compare distributions using these measures, and select appropriate measures based on data characteristics.
Please provide a comprehensive transformation plan that restructures this content to:
Move foundational concept explanations and basic calculations to curated online resources
Redesign face-to-face time for collaborative problem-solving with real-world datasets
Create flexible pacing options that allow students to spend more time on challenging concepts like standard deviation
The transformation should follow this blended learning structure:
Online preparation phase
Face-to-face application phase
Online extension/reflection phase
Ongoing formative assessment throughout.
In your response, include specific guidance on content delivery methods, activity redesign strategies, assessment modifications, and technology tool recommendations that will support this blended approach while maintaining rigorous learning outcomes
Additional Prompting Strategies
Ask the bot to break down the transformation process into manageable weekly implementation steps for gradual transition to blended learning
Request specific examples of how to convert lecture-based content into engaging online pre-learning modules with built-in comprehension checks
Have the bot generate a comparison chart showing "before and after" time allocation between online and face-to-face activities
Ask for student and parent communication strategies explaining the benefits and expectations of the new blended format
Request troubleshooting guidance for common challenges teachers face when shifting from traditional to blended instruction models
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