For trainers, understanding and mastering the art of creating effective prompts with AI has become essential, as it enables them to integrate AI more meaningfully into their pedagogical practices.
A prompt, in the context of AI, can be defined as an instruction intended to guide the AI in generating specific responses or content. The quality and accuracy of the results obtained depend heavily on how these prompts are designed. A well-crafted prompt can lead the AI to produce more precise, relevant and useful results, transforming the tool into a teaching assistant capable of enriching your learners' learning experience.
In this article, we're keen to explain the importance of the art of creating AI prompts, highlighting the key skills required, the different categories of prompts and their applications in training. We'll also provide practical tips and real-world examples to guide you in creating effective prompts and enabling you to make the most of generative AI technologies in your teaching practices.
What are the key skills for creating effective prompts?
Creating effective prompts for artificial intelligence (AI) in an educational context requires a combination of specific skills and knowledge. These skills enable instructors to formulate instructions that guide AI towards the production of relevant and useful results, thus enriching the learning experience for students. Here are the key skills needed to create effective prompts:
Understanding AI capabilities
Trainers need to have a good understanding of the capabilities and limitations of the AI models they work with. This includes knowledge of the types of tasks AI can perform, how it processes information and the factors that can influence the quality of results. A thorough understanding enables the formulation of prompts that make the most of AI's capabilities.
Computational creativity
Computational creativity refers to the ability to think creatively in the context of computer technologies, including AI. To create effective prompts, trainers need to be able to design original and innovative instructions that encourage AI to generate creative and relevant responses. This may involve thinking of unique ways of approaching a topic or asking a question.
Metacognition and critical thinking
Metacognition, or reflection on one's own thinking, is essential for evaluating the effectiveness of prompts and adjusting instructions according to the results obtained. Trainers need to be able to analyze AI responses, identify areas for improvement and reformulate prompts to achieve better results. Critical thinking also plays an important role in designing prompts that encourage students to develop their analytical and critical skills.
Knowledge of the educational field
In-depth knowledge of the relevant educational field is fundamental to creating prompts that are relevant and aligned with learning objectives. Above all, trainers need to understand the key concepts, theories and practices in their field to formulate prompts that guide AI towards the production of high-quality educational content.
Adaptability
Finally, adaptability is essential to meet changing student needs and evolving AI technologies. Trainers must be prepared to experiment with different types of prompts, explore new pedagogical approaches and incorporate student feedback to continually improve the effectiveness of prompts.
By developing these skills, trainers can create prompts that maximize the potential of AI technologies in education and offer an enriching and even personalized learning experience for learners.
To make the most of artificial intelligence (AI), it's essential to master a specific set of skills. These skills are not limited solely to a technical understanding of AI, but also encompass the soft and interpersonal skills that enable effective use of this technology.
What are the categories of prompts and their applications in training?
In the field of training, and in particular for my teaching area "digital marketing", prompts can be classified into several categories, each with specific applications that can help me enrich my learners' learning path.
Text
Text prompts are used to generate summaries, answers to questions, text classifications and more. For example, a prompt might ask the AI to summarize the latest trends in SEO or create content for a blog on digital marketing. If the prompts are designed by the learners themselves, it helps them to quickly understand complex concepts and develop writing and content analysis skills. And I'm often surprised by the quality of the knowledge acquired when I interview them individually after the research.
Image
In digital marketing, images play a role in illustrating concepts. Professional image creation prompts with generative AI can be used to illustrate these marketing concepts or to create visuals for campaigns. For example, a prompt could be used to generate images that illustrate the impact of social networks on branding. It's a way for my learners to get inspired and start designing.
Code
It's less common in my courses, but code generation prompts can be used to teach learners how to automate certain digital marketing tasks, such as scripting for data analysis or website development. A prompt might ask the AI to generate sample code to track conversions from an online advertising campaign. I've never really tried it, but as I explore other tools that require custom code to work, I think it might make sense and it would give learners something concrete to work on.
Music
Although less common in digital marketing, music can be used to create moods in advertisements or branded content generated by learners when creating content for digital channels. Prompts can be used to compose jingles or background music for promotional videos. Here again, the pedagogical approach is interesting, as it allows the student to create a soundtrack adapted to their content, without having to create the piece of music themselves.
By using well-designed prompts, trainers can encourage learners to explore and experiment with different facets of digital marketing, while developing practical and theoretical skills. How about that?
Apolearn offers some practical advice and examples.
To write prompts that maximize the effectiveness of AI in training, it's essential to follow some practical advice. Our tips will help you create prompts that are not only clear and precise, but also tailored to learners' needs and pedagogical objectives. Here are the key tips to consider:
1. Defining the role of AI
Before writing a prompt, it's very important for you to determine the role the AI is to play. This could include generating content, assisting with learner assessment, or providing personalized feedback. The more clearly you define its role, the more the prompt will guide the AI towards carrying out specific tasks that match your expectations.
2. Contextualize the prompt
The context in which the prompt is used must be taken into account. This means understanding the learners' level of knowledge, the course objectives and the skills they need to acquire. A well-contextualized prompt is sure to enable AI to provide relevant information tailored to the level of your learners.
3. Structure the steps
A good prompt should guide the AI through the various steps required to achieve the final objective. This may involve creating a lesson plan, defining pedagogical objectives, or proposing practical exercises. Each step must be clearly defined so that the AI can follow a logical progression.
4. Specify the scope of the answer
It's also important to clearly define the scope of the response expected from the AI. This includes, for example, the length of the content, the level of detail, and the style of presentation. By specifying these elements, your prompts will help the AI to produce responses that match expectations and needs.
5. Observe constraints
In the last step, we'll tell you about constraints such as relevance, adaptability and professional context that need to be taken into account when writing prompts. These constraints ensure that AI-generated content is not only accurate and useful, but also aligned with professional realities.
Example of a prompt
To illustrate our advice, we've put together a small example of a prompt :
The ideal prompt to personalize and paste into ChatGPT for optimal results!
Digital marketing lesson plan for master marketers
Introduction to Digital Marketing
Objective: Understand the basics and the importance of digital marketing in today's context.
Content: History of digital marketing, differences with traditional marketing, current panorama.
Module 1: Content Marketing
Objective: Learn how to create and use content to attract and engage a targeted audience.
Content: Content strategies, storytelling, content mapping, content creation and curation, measuring effectiveness.
Exercises: Creating a content plan for a fictitious brand, case analysis.
Module 2: Search Engine Optimization (SEO)
Objective: Master techniques to improve visibility and ranking in search engines.
Content: SEO principles, keywords, on-page and off-page optimization, technical SEO, SEO tools.
Practical workshop: SEO audit of an existing site, implementation of an SEO strategy for a fictitious project.
Module 3: Search Engine Advertising (SEA)
Objective: Understand and apply paid search engine advertising techniques.
Content: How SEA works, creating campaigns, targeting, optimizing bids, analyzing performance.
Project: Design and simulation of a SEA campaign for a product or service.
Module 4: Social networks
Objective: Use social networks effectively for marketing and brand communication.
Content: Social network strategies, creating appropriate content, community management, social network advertising.
Group work: Development of a social marketing campaign for a fictitious company.
Module 5: Email Marketing
Objective: Learn how to design and execute effective email marketing campaigns.
Content: Principles of email marketing, newsletter creation, segmentation, automation, performance analysis.
Practical exercise: Creating an email campaign for a given situation.
Module 6: Web Analysis
Objective: Acquire the skills to analyze and interpret web data to optimize digital strategies.
Content: Introduction to web analytics, analysis tools, data interpretation, data-driven decision-making.
Tutored project: Data analysis of an existing website and strategic recommendations.
Evaluation and final project
Class participation and practical work throughout the course.
Final project integrating several aspects of digital marketing to solve a practical case.
Pedagogy
Variety of formats to suit different learning styles: lectures, workshops, case studies, projects.
Encouragement of group work and sharing of experiences to enrich learning.
Assessment
Based on Bloom's taxonomy: knowledge, understanding, application, analysis, synthesis and evaluation.
Use of MCQs, projects and presentations to assess skills acquired.
Content specifications
Clarity and simplicity: language adapted to the level of the students, logical structure.
Use of real-life examples and practical exercises to ground theory in practice.
Each module should be succinct (500 to 1,000 words per lesson), with a quotation in APA style.
