Rethinking sociology education in human–robot social interaction learning ecosystems: a mixed method study

Purpose The purpose of this research is to determine whether and how sociology courses that incorporate adaptive algorithms, quantum cognition and human–robot social interaction might improve students’ learning outcomes. The objective of the study is to uncover how AI technology can enhance the traditional learning environment. Design/methodology/approach This is a mixed-methods design, and 93 university students were used in the study. The data is obtained from observations and interviews. The educational technique used is the Pedagogical AI Integration Learning Model. Findings On the basis of the data analysis of the conducted research, it has been found that the quantitative results indicated satisfactory responses from students. Most of the students gave a very satisfied response, which indicated an average score of more than 4.80. This result indicated adequate responses towards emotional support, critical thinking and ethical participation. Semi-structured interviews indicated support towards the role of AI, which assisted respondents in understanding practical sociological theory, and encouraged more free and moderate discussions. Practical implications Integrating AI and human collaboration, along with quantum cognitive theory, the practical implication of the work adds value to the education sector and the education ecosystem regarding context-driven AI and sociology education. The work also tackles the requirements of Indonesian digital pedagogy. Originality/value Sociology education is being transformed from a static mode of knowledge sharing to one that is more dynamic, moral and highly critical through the strengthening of collaboration between artificial intelligence and quantum cognition.