Senior architects are pivotal in shaping architectural projects, yet integrating Generative AI (GenAI) into their workflows presents notable challenges. A formative study (N=11) identified key pain points in their early concept design process. To address these, we developed EarlyArchi, a GenAI-driven system supporting automated concept generation and evaluation. In a within-subject study (N=13), participants used EarlyArchi for early-stage design tasks. Results showed enhanced perceived creativity, improved design competency, and more efficient ideation. However, concerns emerged regarding controllability and domain-specific accuracy, highlighting the need for features that preserve professional autonomy and trust. Further analysis revealed three GenAI involvement modes—fully AI-driven, GenAI-led, and human-led—emphasizing the importance of adaptive role allocation in balancing creative exploration with expert leadership. These findings offer insights into supporting senior architects through GenAI while identifying key considerations for designing future human–AI co-creation systems.

Cultural heritage, a testament to human history and civilization, has gained increasing recognition for its significance in preservation and dissemination. The integration of immersive technologies has transformed how cultural heritage is presented, enabling audiences to engage with it in more vivid, intuitive, and interactive ways. However, the adoption of these technologies also brings a range of challenges and potential risks. This paper presents a systematic review, with an in-depth analysis of 177 selected papers. We comprehensively examine and categorize current applications, technological approaches, and user devices in immersive cultural heritage presentations, while also highlighting the associated risks and challenges. Furthermore, we identify areas for future research in the immersive presentation of cultural heritage. Our goal is to provide a comprehensive reference for researchers and practitioners, enhancing understanding of the technological applications, risks, and challenges in this field, and encouraging further innovation and development.

The rapid increase in the number of individuals with Autism Spectrum Disorder (ASD) has drawn extensive attention from both the general public and researchers. Artificial Intelligence (AI) has been applied in the assessment, early diagnosis, and intervention of ASD to enhance the efficiency of clinicians and reduce tension in medical resources. However, the adoption of AI systems in clinical practice is relatively limited due to the challenge of complexity and diversity of ASD. Thus, involving insights into clinicians' perceptions and barriers toward the role of AI is crucial for enhancing clinicians-AI cooperation for autism. Through conducting the semi-structured interview with 18 physicians across tertiary and secondary hospitals in various regions, this study indicates the positive attitude toward collaborating with AI among physicians. Additionally, some concerns are also reported, such as the complexity of ASD, uncertainty of AI capabilities, and understandability of AI. The findings of this study highlight the significance of human-centered AI in satisfying different stakeholders' needs and discuss the potential implications of AI capabilities for adopting AI in future autism research.

Reading is an important aspect of a child's development. Reading outcome is heavily dependent on the level of engagement while reading. In this paper, we present FOCUS, an EEG-augmented reading system which monitors a child's engagement level in real time, and provides contextual BCI training sessions to improve a child's reading engagement. A laboratory experiment was conducted to assess the validity of the system. Results showed that FOCUS could significantly improve engagement in terms of both EEG-based measurement and teachers' subjective measure on the reading outcome.

In this paper we discuss the basic theory of the probabilistic function of a multiple branch hidden Markov model (MBHMM) for the purposes of automatic speech recognition. Since it has multiple transition branches between two states, the new model can hold much more spectral information in the speech signal than the basic ones, which have only one transition branch between the states. The evaluation, decoding, and training algorithms associated with MBHMM are also derived. The resulting recognizer is tested on a vocabulary of ten Chinese digits over 28 speakers. The recognition results show that MBHMM outperforms the conventional ones.>