Why Can AI Avatars Feel Unnatural or Distracting?
In an earlier post, Do Flaws in AI Avatars Hurt Learning?, I looked at the broader question of whether flaws in AI avatars actually hurt learning. Here, I want to focus on one possible reason some avatars feel distracting in the first place.
A recent discussion in Reddit’s r/Training community caught my attention. Practitioners were comparing experiences with AI avatars, and although the reactions were mixed, a recurring complaint was that some avatars feel artificial, robotic, distracting, or simply hard to take seriously.
Read the r/Training discussion
Those comments are not research evidence. They are a signal. And they raise a useful question: why can an almost-human presenter become the thing we notice instead of the material we are supposed to learn?
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ATTENTION
What Might Be Pulling Attention Away?
A realistic avatar gives us a familiar reference point: another person. We already know, usually without thinking about it, how faces move, how speech is timed, where eyes look, and how expressions follow conversation.
Then something misses.
The mouth movement is a little late. The pause comes in an odd place. The eyes do not quite behave as expected. The voice and expression do not seem to belong to the same person.
I suspect the problem for learning may begin when the learner keeps checking those mismatches. The question is no longer just whether the avatar feels eerie. The avatar may start competing with the lesson for attention.
That could create extraneous cognitive load, mental effort spent interpreting the presenter rather than learning the material. We do not yet have enough evidence to say that this is what happens in AI-avatar training. It is a hypothesis worth testing.
EXPLANATION
Short Answer
One possible explanation is the uncanny valley. Roboticist Masahiro Mori proposed the idea in 1970: as an artificial figure becomes more human-like, our affinity for it may increase until it becomes almost human, but not quite. At that point, small inconsistencies can produce discomfort or eeriness.[1]
Later research has found that the evidence for a simple, universal “valley” is mixed. But a review of empirical studies found stronger support for perceptual mismatch and unresolved prediction error as possible reasons for the effect. Human-like and nonhuman features that do not fit together can make an artificial character feel uncanny.[2]
Perceptual mismatch
Perceptual mismatch is when parts of an artificial presenter do not quite seem to belong together, such as a realistic face paired with less-natural movement or speech.
Prediction error
Prediction error is the gap between what we expect to happen and what actually happens. With a human-like avatar, the more human it looks, the more human-like behavior we tend to expect.
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EXPECTATIONS
Can We Change the Learner’s Expectations?
This led us at the Workforce Applied Research Center to a second question.
What if the learner never expects a human presenter in the first place?
Instead of having an AI avatar simply appear and behave as though it were human, we could make its artificial nature unmistakable. It might materialize with an obviously digital effect and introduce itself plainly as an AI learning guide.
Now the learner does not have to slowly discover that something is “off.” The category is established from the beginning: this is AI.
Would that allow the learner to notice the imperfections, accept them as expected characteristics of an artificial presenter, and move on?
We do not know yet. That is the question we plan to investigate.
If you use AI avatars in training and have observed learners reacting to their realism, artificiality, or presentation style, use our contact form and select Research Inquiry to let us know what you have seen.
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SOURCES
References
1. Mori, M. (2012). The uncanny valley. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
2. Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses: Support for perceptual mismatch as one road to the valley of eeriness. Frontiers in Psychology, 6, 390. https://doi.org/10.3389/fpsyg.2015.00390
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