It is challenging for autistic people to interpret and respond appropriately to social scenarios due to differences in emotion understanding and recognition abilities. This paper aims to design, implement, and evaluate an adaptive, gamified tool to support emotion recognition skill development in children with ASD. First, we propose a novel adaptive algorithm that facilitates a personalized progression from avatar to human faces according to the user’s real-time performance accuracy. Second, we present the implementation of EmoBridge, a mobile application that integrates this algorithm within a gamified learning session and includes a browse section for users to view emotion-categorized images and upload their own. Third, we report findings from a preliminary experiment involving 18 children aged 7-12, including both neurotypical and ASD-diagnosed participants, and their parents. The results demonstrate EmoBridge’s potential to support emotion recognition, particularly for verbal chi ldren with ASD, while also revealing specific limitations for non-verbal users. The feedback collected provides valuable insights that will guide future iterations and research, contributing to the development of more inclusive and adaptive assistive technologies.

Beginner programmers can develop an intuitive understanding of programming by leveraging the motivating field of multimedia to visually inspect outputs and experiment with different ways to solve problems. This paper presents MediaLib, a Python library designed to facilitate multimedia programming and lessen the cognitive load associated with programming for novice programmers. In addition, we designed an official MediaLib website which contains the library itself, two tutorials, and clear documentation. The tutorial clearly presents the learning objectives of each lesson and contains exercises related to MediaLib. We designed these exercises to help students gain knowledge incrementally, without requiring in-depth maths knowledge.