In recent years, new teaching opportunities have emerged as artificial intelligence has gained
increasing attention in computational thinking education. However, to design effective pedagogy based on the present research landscape, the technology solution must be tailored
to a learning environment through a collaboration between human-computer interaction
and human-artificial intelligence interaction research. The thesis aims to enhance programming experiences and increase accessibility to programming resources for students in remote schools and post-secondary graduate settings using human-computer interaction and
human-artificial intelligence interaction techniques. It addresses the limited computational
thinking education resources and the potential of artificial intelligence-assisted coding in a
self-learning method suitable for remote Northwestern First Nation communities in Canada.
This thesis proposes methods to cater to students’ learning styles in two different learning
environments using human-computer interaction for kindergarten to grade 12 students and
human-artificial intelligence interaction for university students. Incorporating these research
principles can help novice programmers overcome cognitive overload and poor user experience
and achieve an optimal user experience. The thesis begins with bibliometric analysis and
provides a holistic perspective of computational thinking and artificial intelligence trending
strategies. It then presents an empirical study on human-computer interaction, investigating
computational thinking in remote kindergarten to grade 12 schools with blended learning
environments. It also presents another empirical study on human-artificial intelligence interaction to experiment with a self-learning style for artificial intelligence coding assistants for
university students using massive open online courses. [...
