IBL News | New York
AI video generation platforms like Colossyan, Synthesia, HeyGen, and NotebookLM are being adopted by L&D tech teams for rapid production, avatar realism, and multi-language output.
But only two platforms—Colossyan and Synthesia—support evidence-based instructional design in ways that matter most: embedded retrieval practice, learner control, and learning-relevant measurement.
However, critical gaps remain: none automate spacing, none actively prevent cognitive overload from over-signalling, and none make frequent generative retrieval practice the default.
• When researchers analysed 6.9 million MOOC video sessions, they found roughly 100% watching in the first three minutes, about 50% by six to nine minutes, and around 20% by nine to twelve minutes.
• Other studies found that well-designed interactive videos of 10–15 minutes produced learning outcomes equal to or better than those from shorter videos.
• Reports say that quizzes interpolated between video segments reduce mind-wandering and boost final test scores. Also, adding interactivity can expand learners’ effective engagement beyond the so-called “six-minute limit.”
The research is clear: segmentation reduces wasted mental effort. Retrieval practice strengthens memory. Spacing creates durable retention. Measurement enables improvement.
Dr. Philippa Hardman highlighted six principles for designing videos that actually produce learning, citing several research papers. These principles reduce cognitive overload, strengthen memory, activate motivation, respect autonomy, and build durable retention.
1. Intentional Segmentation
Breaking content into meaningful chunks improves learning.
Segmented videos beat continuous videos in terms of memory and transfer. Well-structured interactive videos of 10–15 minutes can perform as well as or better than shorter videos.
These segmented videos reduce cognitive load and improve retention compared to a continuous presentation
2. Embedded Retrieval Practice
Quizzes inserted between segments reduce mind-wandering and boost performance.
Retrieval within video content strengthens memory and interrupts passive viewing.
This effect is particularly pronounced in video-based learning, as research using Coursera lectures has found.
3. Strategic Signalling
Well-used visual and verbal cues in video content improve recall, but only when used selectively.
However, excessive or multi-coloured highlighting will backfire.
Effective signalling physically and temporally integrates text with visuals, using 3–4 selective cues per segment rather than cluttering screens with competing emphases.
4. Instructional Presence
Seeing a visible instructor as a presenter (human or hyper-realistic AI) increases motivation, trust, and transfer.
Gestures, facial expressions, and eye contact create a sense of social presence and interpersonal interaction, even when the instructor isn’t physically present.
5. Learner Control
People learn better when multimedia is presented in learner-paced segments rather than continuous units, allowing learners to manage cognitive load by pausing when needed.
Online learners can retain 25–60% more information than in traditional classrooms when they can learn at their own pace and revisit challenging content without time pressure.
6. Distributed Practice (Spacing)
A sequence of shorter sessions spaced days or weeks apart across multiple sessions dramatically improves long-term retention.
Treating video-based learning as a sequence with follow-up days/weeks later builds stronger memory.
To assess how well the mentioned video generation platforms support learning, Dr. Philippa Hardman turned these six evidence-based principles into a scoring rubric.
- Segmentation Support: How well does the platform make it easy to chunk content into scenes or chapters?
- Retrieval Practice: Can you embed quizzes directly in videos, at any point in the video?
- Signalling & Guidance: Are there tools for text highlighting and emphasis that don’t overwhelm?
- Learner Control: Can learners navigate by chapter, adjust playback speed, and replay sections?
- Spacing Workflow: Does the platform help you create and schedule follow-up content for spaced practice?
- Instructor Presence: Do avatars/presenters show realistic facial expressions and gestures?
- Measurement Quality: Does the platform measure learning outcomes (quiz scores) or only engagement (completion)? Can you track results through SCORM or xAPI?
- Iteration Stability: Can you edit videos without starting over while keeping instructional structure intact?
Key takeaways from the tests were:
• Only Colossyan and Synthesia scored Strong on embedded retrieval practice—what research identifies as one of the strongest predictors of retention. Both platforms offer built-in quizzes with manual designer placement, SCORM/xAPI export, pass-rate tracking, and immediate feedback.
• HeyGen and NotebookLM score very poorly: they lack native quiz support, no way to embed retrieval prompts, and no score tracking. They can scale content production efficiently, but they don’t easily support the mechanism that turns watching into durable learning.
• Colossyan and Synthesia also support learning-relevant measurement: capturing quiz performance data, pass rates, and learner-level results—the kind of feedback loop L&D teams rely on to improve instruction one of the platforms automate or optimise distributed practice workflows and spacing, which is essential for durable learning teams must manually schedule follow-up quizzes and application tasks 2–3 days and 1–2 weeks after initial video viewing—outside the video platform entirely.
• Platforms provide overlays, animated text, highlights—but none intentionally prevent overuse.
• “For AI video generation to truly transform L&D—not just accelerate it—vendors need to build instructional design workflows that make bad instructional choices hard and good ones easy.”
• “The future of video-based learning isn’t faster production or more convincing avatars—it’s tools that operationalise learning science by default, so that creating effective instruction becomes the path of least resistance.”
• “The AI-video-generation platforms that will most likely win in the L&D space will be the platforms that enable and orchestrate effective learning at pace and scale.”