The usefulness of computer-based tools in supporting singing pedagogy has been demonstrated. With the increasing use of artificial intelligence (AI) in education, machine learning (ML) has been applied in music-pedagogy related tasks too, e. g., singing technique recognition. Research has also shown that comparing ML performance with human perception can elucidate the usability of AI in real-life scenarios. Nevertheless, this assessment is still missing for singing technique recognition. Thus, we comparatively evaluate classification and perceptual results from the identification of singing techniques. Since computer-assisted singing often relays on visual feedback, both an auditory task (recognition from a capella singing), and a visual one (recognition from spectrograms) were performed. Responses by 60 humans were compared with ML outcomes. By guaranteeing comparable setups, our results indicate that ML can capture differences in human auditory and visual perception. This opens new horizons in the application of AI-supported learning.