How to Measure Whether an AI Training Video Has Helped People Learn

Creating an AI training video is only one part of the learning process. A video may look professional, sound natural, and explain a topic clearly, but these features do not necessarily mean that viewers have understood or remembered the content. To evaluate its effectiveness, you need to measure what learners know before watching, what they remember afterwards, and whether they can apply the information in a different situation.

Measuring learning outcomes helps distinguish an engaging video from an effective training resource. By combining knowledge tests, practical tasks, learner feedback, and performance data, educators and content creators can identify what works and what needs improvement. For those looking to develop these skills, PW Skills School of Design’s AI Video Creator course covers scripting, prompting, AI voiceovers, image-to-video creation, editing, and portfolio building through self-paced recorded lessons.

Ways to Measure the Effectiveness of AI Training Videos

The effectiveness of an AI training video can be evaluated through multiple measures. Each one captures a different aspect of learning, from remembering information to applying it in a new context.

1. Compare Pre-Test and Post-Test Scores

A pre-test measures what learners already know before watching the video, while a post-test evaluates their knowledge after the lesson.

Comparing the two results helps you determine whether learners have improved rather than simply measuring what they knew beforehand.

For example, if a training video explains the basics of AI prompting, you could ask learners to identify effective prompts before watching it. After the video, give them a similar assessment to check whether their understanding has improved.

To make the comparison meaningful:

• Keep the pre-test and post-test aligned with the same learning objectives.

• Use questions that assess understanding rather than only recognition.

• Account for differences in learners' existing knowledge.

• Compare results across a group instead of relying on one learner's score.

A post-test score alone may not show how much someone has learned. Measuring the change from their starting point provides more useful evidence.

2. Measure Knowledge Retention

Retention measures how much information learners can remember after watching a training video.

A learner may understand an explanation while watching it but struggle to recall the same information later. Retention tests help determine whether key concepts have stayed with them.

You can measure retention by asking learners to:

• Recall key terms and definitions without referring to the video.

• Explain the main points in their own words.

• Answer questions about information explicitly covered in the lesson.

• Complete a follow-up assessment after a delay to check longer-term recall.

For example, after an AI training video about text-to-speech tools, learners could be asked to explain what text-to-speech does and identify the steps involved in generating a voiceover.

An immediate post-test measures short-term retention. A delayed assessment, conducted after a suitable interval, can provide additional evidence about how well the information lasts.

3. Test Whether Learners Can Apply What They Have Learned

Remembering information does not always mean that learners can use it effectively. This is why knowledge transfer is another important measure of learning.

Transfer refers to applying knowledge or skills in a different situation from the one demonstrated in the lesson.

For an AI video creation tutorial, you could ask learners to use the techniques demonstrated in the video to create a short video of their own. Instead of repeating the instructor's exact example, they would need to apply the same principles to a new topic.

Practical transfer assessments can include:

• Creating a video using a new prompt or subject.

• Applying a demonstrated workflow to an unfamiliar project.

• Identifying and correcting errors in an AI-generated script.

• Choosing suitable tools for a different production requirement.

• Completing a task without step-by-step guidance from the original video.

Use a consistent scoring rubric to evaluate accuracy, task completion, quality, and independence. This helps you assess whether learners can use their knowledge beyond the original lesson.

4. Collect Learner Feedback

Tests can measure what learners remember and apply, but they do not explain every aspect of their experience. Feedback can help you understand whether the video felt clear, manageable, relevant, and motivating.

After the lesson, ask learners to rate statements such as:

• The explanations were easy to understand.

• The video presented information in a logical order.

• The pace allowed me to follow the lesson.

• The examples helped me understand the topic.

• I feel confident applying what I learned.

A five-point rating scale, from strongly disagree to strongly agree, can make responses easier to compare. You can also include an open-ended question asking what learners found confusing or what they would change.

However, learner satisfaction is not the same as learning achievement. A video may receive positive feedback without producing measurable improvements in knowledge. Feedback should therefore complement assessment results rather than replace them.

5. Evaluate Cognitive Load and Engagement

Cognitive load refers to the mental effort involved in processing information. If a training video introduces too many unfamiliar ideas at once, uses confusing visuals, or moves too quickly, learners may struggle to follow it.

After watching the video, ask learners to rate how mentally demanding the lesson felt and how much effort they needed to understand it.

You can also examine indicators such as:

• Whether learners complete the video.

• Which sections they replay or pause.

• Where viewers tend to stop watching.

• Whether learners can answer questions about the most important sections.

• How frequently learners need additional explanations.

These indicators can help identify areas where the video may need improvement. However, watch time and completion rates are engagement metrics, not direct proof of learning. A learner can watch an entire video without understanding it, while another may learn effectively from a shorter session.

6. Compare AI-Generated Videos with Other Training Formats

If you want to know whether an AI-generated training video is effective, compare it with an appropriate alternative, such as a traditionally recorded video or another instructional format.

For a fair comparison, keep the learning objectives, subject matter, lesson duration, difficulty, and assessment methods as similar as possible. Ideally, assign learners randomly to the different formats and check that their prior knowledge is comparable.

Measure the same outcomes for each group:

• Pre-test and post-test performance.

• Retention of key information.

• Ability to apply the information in a new context.

• Cognitive load and learner feedback.

• Completion and engagement data.

A comparison can help identify whether differences are associated with the video format. If you want to establish whether AI-generated videos improve learning, rather than simply perform similarly, you should also examine whether the differences are statistically meaningful.

What Does Research Say About Measuring AI Training Video Effectiveness?

A study published in the British Journal of Educational Technology in 2024, titled From recorded to AI-generated instructional videos: A comparison of learning performance and experience, provides an example of how these measures can be used together.

The study compared AI-generated instructional videos with traditionally recorded videos. The AI-generated version included an AI-generated lecture script, voice, and virtual teacher. Both formats were used to teach English vocabulary.

The experiment involved 76 undergraduate and graduate students, randomly divided into two groups of 38. Participants completed a pre-test, watched a video, and then completed post-tests and learner-experience ratings.

Key Findings

The findings illustrate why learning effectiveness should not be judged using a single metric.

• Retention improved: Participants who watched the AI-generated video scored higher on the test measuring recall of explicitly taught information.

• Transfer did not differ significantly: The results did not establish a statistically significant advantage for either video format in applying vocabulary knowledge to other tasks.

• Overall performance did not differ significantly: Although the AI-generated group had a higher average score, the difference was not statistically significant.

• Social presence was lower: Participants reported a weaker sense of the instructor's presence in the AI-generated video.

• Satisfaction was comparable: The difference in satisfaction ratings was not statistically significant.

The study also found no statistically significant differences in motivation, emotional ratings, trust, parasocial interaction, or the measured dimensions of cognitive load. Although several ratings were numerically lower for the AI-generated group, those differences should not be interpreted as established effects.

Develop Practical AI Video Creation Skills with PW Skills School of Design

Creating effective training videos requires more than measuring learning outcomes. It also involves planning clear scripts, choosing suitable visuals, producing engaging narration, and refining the final video. The AI Video Creator course by PW Skills School of Design helps beginners learn AI-powered video production through self-paced recorded lessons in Hinglish. The curriculum covers content strategy, AI prompt engineering, scripting, storyboarding, AI voiceovers, image-to-video creation, video editing, and thumbnail design.

What Does the Course Include?

• AI video production: Learn workflows for turning ideas and scripts into videos using AI tools.

• Hands-on projects: Practise your skills through assignments, real-world case studies, and portfolio checkpoints.

• Editing and visual planning: Explore pre-production, visual planning, post-production, and video packaging.

• Portfolio development: Build AI video assets that showcase your skills and creative work.

• Freelancing guidance: Learn about presenting your work, building a profile, and acquiring clients.

Measure AI training video effectiveness through retention tests, practical assessments, and learner feedback rather than views alone. Combining these insights with AI video creation skills can help you develop clearer, more effective training content. PW School of Design’s AI Video Creator course covers scripting, prompting, voiceovers, and editing to help beginners build these skills.