This study compares the effectiveness of virtual reality (VR) and videoconferencing (VC) platforms for online learning. Comparative meta-analyses of 29 articles from 2003 to 2023 (15 for VR, 14 for VC) revealed that both technologies positively affect learning outcomes, with VR demonstrating a larger effect size (ES = 0.913) compared with VC (ES = 0.284). VR proved more beneficial in regular-sized classes, especially for natural science subjects, and excelled in experiential or collaborative learning environments. VC showed a greater impact in smaller classes, with significant variations depending on the program brand and camera options; it also favored natural science. These findings provide valuable guidance for educators and learners in choosing the most suitable technology for different online learning scenarios and shed light on future research in this field.

This study examines how agent type (customized vs. general-purpose) and feedback style (Socratic vs. directive) are associated with learners’ engagement with artificial intelligence (AI)-generated feedback in self-directed learning (SDL), with particular attention to patterns in feedback quality, self-regulatory behaviors, learning experiences, and learning outcomes. A 2 × 2 mixed factorial experiment was conducted with 51 postgraduate students who completed two instructional design tasks under different feedback conditions. Quantitative results indicated that customized agents generated feedback with higher accuracy and specificity than general-purpose agents. Socratic feedback was associated with stronger comprehension monitoring, whereas directive feedback was associated with higher cognitive load. A significant interaction suggested that the advantage of customized agents in learning outcomes, operationalized as short-term task improvement, emerged under directive feedback but not under Socratic feedback. Qualitative analysis indicated that Socratic prompts encouraged deeper, logic-oriented reflection, whereas directive feedback provided actionable guidance that facilitated task completion. Learners adopted feedback selectively based on perceived accuracy, and trust in customized agents was higher when feedback was clear and contextually aligned. These findings suggest that the effectiveness of AI-generated feedback is shaped not only by agent type and feedback style but also by how learners evaluate and use feedback.