A custom-branded, interactive web application designed as a 'Prompt Lab' for educational courses. The tool provides a safe sandbox environment for students to experiment with AI prompt engineering, guided by a mascot avatar that offers tips. The system is built to be embeddable within an LMS like Thinkific.
Courses that teach people to use AI at work, such as AI for construction managers or bookkeepers, often explain prompting in videos but give students nowhere safe to practise. Sending learners to a public chatbot means they need their own accounts, may paste sensitive work material, and leave the course environment with no guidance on whether their prompts are any good. Course creators lose visibility of how students are progressing and cannot tailor practice exercises to their industry.
A prompt engineering sandbox gives students a branded practice space embedded directly in the course. They write prompts against realistic industry scenarios, see the AI's response, and can switch on tips from a mascot guide that suggests how to add context, set a format or give examples. Prompt history is saved so students and instructors can see improvement over time. The course creator controls the model, the exercises and the guardrails, which makes AI practice safer and more relevant than sending learners to an open tool.
A React or Next.js app, or a Webflow or Bubble front end, is embedded in Thinkific lessons so students practise without leaving the course.
Each exercise loads an industry scenario and goal, such as drafting a site safety toolbox talk, and the student writes a prompt to achieve it.
The prompt is sent through the course creator's OpenAI API account with system rules that block off-topic or unsafe use and cap response length.
If mascot tips are on, a second AI call reviews the student's prompt and suggests specific improvements, for example adding audience, tone or output format.
Prompts, responses and tips are saved to the student's history, and instructors can view progress summaries or common mistakes across the cohort.
A student in a 'AI for Construction' course uses the embedded Prompt Lab to practice writing prompts. They can toggle 'Mascot Tips' to get suggestions on how to improve their prompts for better results from the AI. They can also save their prompt history to see their progress throughout the course.
React/Next.js, OpenAI API, LMS Integration (Thinkific), Webflow/Bubble
Students' names, emails and saved prompts are personal information under the Privacy Act, so explain in your course terms that prompts are processed by an AI provider and how long history is kept. Remind students not to enter confidential client or employer information into exercises. If you sell to schools or learners under eighteen, apply stricter privacy settings and check any education department requirements. Registered training organisations should check that assessment integrity and record-keeping obligations are met if sandbox activity counts towards assessment. Mascot artwork and scenario content should be original or properly licensed under the Copyright Act 1968.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
No. The sandbox calls the AI through the course creator's API account, so students just log in to the course. This keeps the experience simple and lets the creator control usage limits, content rules and which model is used. The API cost is part of the course's running costs.
Generally yes, if the platform allows embedded web content or custom code blocks. Kajabi, Teachable, Moodle and similar systems often support this. Single sign-on or passing student IDs securely is the part that varies most and needs checking for each platform.
The main factors are how many exercise scenarios are needed, whether the mascot tips require a custom feedback framework, how deeply it integrates with the LMS for sign-in and progress tracking, and the expected student volume. Two to four weeks is typical for a focused first version.
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