AI Trivia – the facts about using the content in your digital product
Trivia has long been a popular method for digital media publishers to captivate and engage their audiences. Traditionally, publishers either created tailored content themselves or licensed it from third-party providers like The Question Co. However, in the past two years, many publishers have begun experimenting with AI-generated content as a cost-effective alternative.
This article delves into the pros and cons of using AI-generated trivia to engage digital audiences:
How is AI Trivia Generated?
AI generates questions through complex algorithms that process vast amounts of data, identifying patterns, extracting key information, and then formulating queries based on the content. These algorithms often use natural language processing (NLP) to understand context, intent, and syntax, enabling them to craft questions that are coherent and relevant. Many publishers do not the process and simply use ChatGPT to generate 100 questions on a chosen topic.
AI TRIVIA: BENEFITS
AI TRIVIA: CONS
Efficiency & Speed
One of the most significant advantages of AI-generated questions is the sheer speed at which they can be produced. Apps such as ChatGPT can create 100s of questions in a matter of minutes.
Content Written In One Voice
Leveraging AI to create trivia content allows for a consistent writing style across all materials. This is a significant benefit for publishers, as the AI can be programmed to emulate and maintain the distinct style of their brand.
Cost
On the surface, AI-generated content is extremely cost-effective to produce. In theory, publishers can utilize a free AI platform, input the appropriate commands, and within minutes have hundreds of questions ready to deploy on their website or app.
Potential Errors
All AI-generated content, including trivia, must undergo QA due to a high error rate. Additionally, AI-generated trivia frequently contains repeated content.
No variation of Format
AI typically relies on patterns in the data it processes. This means that if the data is biased or limited, the questions generated will reflect these shortcomings. For example, if an AI is trained on a narrow dataset, it might produce repetitive or predictable questions, which can reduce the effectiveness of assessments or discussions. Moreover, AI may struggle with creating questions for topics that are less represented in the training data.
Quality Control
Before publishing AI-generated content, publishers must QA each question, as errors or off-topic material could lead to decreased user engagement. The cost of QA for a large dataset may exceed that of licensing content from an established provider.
