Coaching technology, wearables, and exergames can provide quantitative feedback based on measured activity, but there is little evidence of effective qualitative feedback to aid technique improvement. To achieve personalized qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique utilizing three-dimensional (3D) tennis motion data acquired from multicamera video. Expert data labeling relied on virtual 3D stick figure replay. Diverse assessment criteria for novice to those with intermediate skill levels and configurable coaching scenarios matched with a variety of tennis swings (22 backhands and 21 forehands), including good technique and common errors. A set of selected coaching rules (CRs) was transferred to adaptive assessment modules able to learn from data, evolve their internal structures, and produce autonomous personalized feedback, including verbal cues over virtual camera 3D replay and an end-of-session progress report. The prototype demonstrated autonomous assessment on future data based on learning from prior examples, aligned with skill level, flexible coaching scenarios, and CRs. The generated intuitive diagnostic feedback consisted of elements of safety and performance for tennis swing technique, where each swing sample was compared with the expert. For safety aspects of the relative swing width, the prototype showed improved assessment (from 81% to 91%) when taking into account occluded parts of the pelvis. This study has shown proof of concept for personalized qualitative feedback. The next generation of augmented coaching and exergaming systems will be able to help improve end user's sport discipline-specific techniques. By learning from small expert-labeled data sets, such systems will be able to adapt and provide personalized intuitive autonomous assessment and diagnostic feedback aligned with a specified coaching program and context requirements.