Authors:

Yilin Shao

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About Paper:

Adaptive intelligent tutoring systems (ITS) adapt to a human learner's cognitive state, such as self-confidence and mental workload through choosing when to assist based on tutoring objectives. In prior work, a psychomotor ITS for a quadrotor simulator was designed to adapt to learners by choosing when to assist in drone landing attempts. However, participants are only given visual indicator when they are assisted in a drone landing, which offers little transparency in the quadrotor simulator. Communication between the human learner and the ITS is a key component in instructing the learner, and this can be improved upon via multi-modal feedback. In this work, we provide haptic feedback to the learner in addition to an existing assistance algorithm, which determines when to provide automation assistance and tone of feedback, trained using self-confidence, workload, and learning stage Markov decision process models. With the addition of multi-modal feedback to the ITS, an updated assistance algorithm can be trained to intelligently assist a user when learning to land a quadrotor in a 2D simulator and decide when to use different forms of feedback. We designed and incorporated haptics onto the physical controller via Arduino microcontroller employing actuators to create vibrational cues that enhance realism and instructional clarity for the user. Additionally, several LEDs were used to provide supplementary visual augmentation. Through this multimodal feedback approach, we aim to facilitate clearer, more intuitive instruction, thereby improving overall learning outcome. Keywords: Intelligent Tutoring Systems; Multi-Modal Feedback; Haptic Feedback

Source:

Purdue University / 2025

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No topics listed

Co-authors:

Yilin Shao