This paper presents an experimental study on the effectiveness of android-assisted microclasses combined with gamification and personality-aware learning strategies in engineering education. The instructional approach employs ADAI, an autonomous android robot that delivers lectures, manages classroom interaction, and administers diagnostic and post-instruction assessments using computer vision, natural language processing, and speech synthesis. Microclasses were conducted on Internet of Medical Things (IoMT), medical robotics, and exoskeleton technologies. Learning outcomes were evaluated using a pre- and post-class assessment design, while learner behavior and engagement were analyzed through validated personality and playertype models, including the Big Five (OCEAN), gamified player types, and Bartle's taxonomy. Results show a clear improvement in learning performance, with diagnostic accuracies between 51% and 62% and post-instruction results reaching 65% to 87%. Additionally, students with higher Conscientiousness levels and Socializer profiles preferred structured and collaborative activities, whereas reward and interface elements showed no significant influence. These findings support the potential of personality-aware, activity-centered android-assisted instruction in engineering education.
