{{course_level}}
Course and Student Level
Example: Example: First-year undergraduate economics
Identify course context and learner level.
Ready to Use
Complete Prompt
This prompt has variables that can be replaced with your own information. Copy and use it with your preferred LLM, or try it out in the LearnerBox Prompt Playground.
Act as an experienced university lecturer and flipped-learning instructional designer.
Design an asynchronous preparation sequence and an active in-class session using the information below.
Course and student level:
{{course_level}}
Topic and learning outcomes:
{{topic_outcomes}}
Pre-class time allowance:
{{preclass_time}}
In-class duration and format:
{{class_duration}}
Available content and technology:
{{resources_technology}}
Student access and participation context:
{{student_context}}
Design requirements:
1. Identify which learning should occur before class and which requires synchronous interaction.
2. Keep pre-class preparation within the stated time allowance.
3. Create a concise pre-class sequence using appropriate elements such as:
- short video or reading;
- worked example;
- guiding questions;
- retrieval practice;
- annotation;
- prediction;
- low-stakes quiz; and
- muddiest-point submission.
4. Explain the purpose of each pre-class element.
5. Design accountability that is low stakes and supports learning rather than punishment.
6. Use pre-class evidence to determine the in-class opening.
7. Design the in-class session around:
- misconception clarification;
- application;
- problem solving;
- peer explanation;
- case analysis;
- practice with feedback;
- synthesis; and
- reflection.
8. Provide approximate timings and transitions.
9. Include a plan for students who could not complete the preparation without repeating the entire pre-class lesson.
10. Include accessibility, captioning, bandwidth, device, and time-zone considerations.
11. Include a post-class consolidation task.
12. Do not assume access to paid platforms or resources not supplied.
13. Do not simply move a full lecture video outside class.
14. Flag content requiring copyright or accessibility review.
Present the result as:
{{output_format}}
Include:
- learning allocation rationale;
- pre-class sequence;
- preparation materials;
- accountability check;
- pre-class data-use plan;
- timed in-class activities;
- misconception response;
- catch-up pathway;
- accessibility provisions;
- post-class consolidation; and
- instructor preparation checklist.Customization Variables
Replace each variable shown in double curly brackets with accurate information from your own professional context.
{{topic_outcomes}}
Topic and Learning Outcomes
Example: Describe the topic and measurable outcomes.
Learning allocation should be driven by the outcomes.
{{preclass_time}}
Pre-Class Time Allowance
Example: Example: 30 minutes
State the realistic preparation time.
{{class_duration}}
In-Class Duration and Format
Example: Example: 90-minute face-to-face seminar
Specify time and delivery mode.
{{resources_technology}}
Available Content and Technology
Example: List readings, videos, LMS tools, polling, simulations, cases, and classroom technology.
The design should use only available resources.
{{student_context}}
Student Access and Participation Context
Example: Describe class size, access limitations, time zones, accessibility needs, and prior knowledge.
This supports inclusive and feasible preparation.
{{output_format}}
Output Format
Choose the format needed for teaching or course development.
Expected Output
A coherent flipped-learning design containing purposeful pre-class preparation, low-stakes accountability, active in-class learning, misconception response, catch-up pathways, accessibility provisions, and post-class consolidation.
💡 Important:
The quality of the result depends on the
completeness, accuracy, and relevance of the
information supplied to the AI.
Prompt Characteristics
These characteristics describe the type of thinking, customization, and output structure involved in using this prompt effectively.
Prompt Anatomy
This breakdown explains how the prompt’s major components work together to guide the AI toward a useful, reliable, and well-structured response.
Role
Positions the AI as a university lecturer and flipped-learning designer.
Context
Defines course, outcomes, preparation time, class time, technology, and access context.
Task
Requires a connected pre-class, in-class, and post-class learning sequence.
Constraints
Prevents overloading, punitive accountability, inaccessible assumptions, and simply moving lectures online.
Output Structure
Requires rationale, preparation, evidence use, active session, catch-up, accessibility, and consolidation.
Input Variables
Course level, outcomes, time allowances, resources, student context, and output format.
Customization Tips
- Keep pre-class work shorter than you initially think.
- Use pre-class responses to shape the opening activity.
- Reserve live time for reasoning, practice, and feedback.
- Provide a catch-up route that does not reward non-preparation with a second lecture.
- Check every media item for accessibility and copyright.
Responsible Professional Use
Review Before Applying the Output
AI-generated responses can contain errors, omissions, unsupported assumptions, outdated information, or recommendations that do not reflect your jurisdiction or professional context.
Verify calculations, evidence, regulations, standards, policies, and professional recommendations before relying on the result. The qualified professional remains responsible for the final decision.