A multidimensional comparative analysis of human expert vs. AI-driven feedback approaches on learner-centered and collaborative groups

The aim of this study is to examine the multidimensional effects of AI-based feedback in learner-centered and collaborative learning environments among university students. The study employed a five-group experimental design: two individual learning groups receiving either AI-based feedback(G1) or human expert feedback(G2), two collaborative learning groups receiving either AI(G3) or human expert feedback(G4), and control group(G5). According to the research results, G4 showed the highest level of development in the areas of creative problem solving, internal-external motivation, and critical thinking. G1 was the group with the highest performance, particularly in terms of system interaction, completed activities, and assignments. In contrast, G2 showed the lowest results in terms of both cognitive development and learning analytics. AI-based feedback in collaborative learning environments provided the highest development in cognitive skills, while AI-based in individual work was more effective in increasing system participation. Factorial MANCOVA indicated significant interactions between learning environment and feedback type on posttest outcomes, with strongest effects on self-efficacy, intrinsic motivation, and flexibility. These results show that AI-based feedback has different effects in both individual and collaborative learning contexts. Qualitative thematic analysis highlighted themes of cognitive facilitation, creativity enhancement, feedback quality perceptions, and environment preferences.