My research transverses disciplines, integrating insights from cognitive psychology, computer science, and philosophy through the constructivist paradigm to better reflect emotion recognition. My current focus is on how humans and AI systems interpret emotion in ways that account for context, meaning-making, and adaptive response. This work is grounded in the view that technological advancement can influence and shape human understanding, and that human understanding can, in turn, inform technology. This can be seen through historical developments where tools designed for one purpose may lead to discoveries in entirely different domains. The telescope, for example, extended human perception outward but also played a role in uncovering the microscopic world. Building on this perspective, I seek to examine the foundations and function of AI models and transformer architectures. My goal is to develop evaluation methods that help clarify how these systems operate which may contribute to understanding the principles that shape model behavior, the consequences of their design, and their relevance to human cognitive development.
Ales, T., & Edyburn, D. L. (2026) Social Robots & Computational Emotion Recognition: Trends and Challenges in Human-Centered AI. Vernon Press. [Book Chapter - in development].
Ales, T., Edyburn, D. L., Boesch, M. C., & McMahan, T. (2026). Barriers to Technology Integration in Higher Education Programs for Students with Intellectual and Developmental Disabilities. Career Development and Transition for Exceptional Individuals. [Manuscript – in preparation].
Ales, T., Edyburn, D. L., Boesch, M. C., & McMahan, T. (2026). The Current Status of Technology in Postsecondary Programs for Students with IDD. Journal of Inclusive Postsecondary Education [Manuscript – under review].
Ales, T. (2026) Sentiment Analysis as a Lens for Evaluating Transformer Architectures: A Comparative Case Study. Journal of Artificial Intelligence Research. [Manuscript – in preparation].