Proof-of-concept of feasibility of human–machine peer learning for German noun vocabulary learning - Hromada, Daniel D.; Kim, Hyungjoong (Frontiers in Education, 2023)
We provide the first empiric evidence that creation of human - machine peer learning (HMPL) couples can lead to increase of level of mastery of different competences in both humans and machines alike. The feasibility of the HMPL approach is demonstrated by means of an exercise whereby the human learner H gradually acquires a vocabulary of foreign language while the artificial learner fine-tunes his ability to understand H’s speech. We evaluated the feasibility of the HMPL approach in an proof-of-concept experiment composed of pre-learn assessment, mutual learning phase and post-learn assessment components. Pre-learn assessment allowed us to estimate prior knowledge of foreign language learners by asking them to name visual cues corresponding to one among 100 German nouns. In a subsequent mutual learning phase, learners are asked to repeat the audio recording containing the label of a simultaneously presented with the visual cue. After the mutual learning phase is over, the subjacent speech-to-text (STT) neural network fine-tunes its parameters and adapts itself to peculiar properties of H’s voice. Finally, exercise is terminated by the post-learn assessment phase. In both assessment phases, number of mis-matches between expected answer and answer provided by human and recognized by machine provides the main evaluation metrics. In case of all six learners who participated in the proof-of-concept experiment, we observed increase in amount of matches between expected and predicted labels which was caused both by increase of human learner’s vocabulary, as well as by increase of recognition accuracy of machine’s speech-to-text model. Therefore, we consider as reasonable to postulate that curricula could be drafted and deployed for different domains of expertise whereby humans learn from AIs in the same time as AIs learn from humans.