This thesis examines the design and evaluation of haptic systems for learning across three distinct domains. The first system, HaptiClay, explores how haptics and gesture can support mathematics learning by helping students construct concrete representations of terms in polynomial functions. The thesis traces the iterative design of the device and reports interventions with students that use haptics to encourage gestural movements while molding polynomial functions and relate those gestures to specific terms in the polynomials. We then analyze learning outcomes to understand the effectiveness of the intervention. The second system, DexKit, enables students to experience dexterity concepts in dexterous teleoperation through touch, including robotic manipulation control, object interaction, and stiffness variation. It introduces a dexterous manipulation platform that combines a soft robotic hand with a three-finger haptic interface, including a novel two-degree-of-freedom mechanism for the index and middle fingers and a soft delta mechanism for the thumb. The third system, VibroGait, is a wearable haptic device for gait correction that helps users learn improved walking patterns through vibrotactile feedback. The thesis presents the design of a flexible skin-interfacing device, the gait prediction algorithms and their implementation, and studies comparing multiple haptic feedback patterns for gait correction.

Across these case studies, the thesis investigates how effective, low-cost learning tools can be designed, which design principles generalize across domains, how haptics influence learning beyond task success, and how haptic systems for learning can be evaluated rigorously. By bringing together mathematics learning, robotic teleoperation, and gait correction, this work expands the evidence base for accessible haptic learning technologies and contributes practical design knowledge for future low-cost, open-source haptic systems.