multimodal-learning-analytics
Filtering by topic multimodal-learning-analytics(1)Clear all filters
- PaperBritish Journal of Educational Technology13 May 2026
Enhancing online learning outcomes through virtual companion AI : The role of identity anthropomorphism
Tiansheng Xia, Minyan Tan, Jiayue Guo
This study introduces identity anthropomorphism in AI learning companions and finds that identity-anthropomorphized AI significantly improves online learning outcomes, comparable to human companionship, through sequential mediation of social presence and positive emotions. Humanities and social sciences learners were more sensitive to this effect than science and engineering learners. The findings support anthropomorphic design in educational AI and highlight the need to consider potential risks like excessive emotional dependence.
Original abstract
Anthropomorphic strategies can improve learners' performance in artificial intelligence (AI) learning environments, yet their underlying mechanisms remain unclear. Grounded in social presence theory, this study introduces the concept of identity anthropomorphism and adopts multimodal learning analytics (MMLA), combining questionnaires, electroencephalography and eye tracking to examine its effects on learning outcomes. Seventy participants completed online learning tasks under three conditions: identity‐anthropomorphised AI, non‐identity‐anthropomorphised AI and human companionship. Results indicated that: (1) identity‐anthropomorphised AI significantly improved learning outcomes compared to non‐anthropomorphised AI and performed comparably to human companionship; (2) social presence and positive emotions sequentially mediated this effect, such that social presence generated a positive net impact only when it evoked positive emotions; (3) learners in the humanities and social sciences were more sensitive than science and engineering learners to the social presence evoked by identity anthropomorphism; (4) although identity anthropomorphism diverted some attention, its emotional benefits offset this disadvantage. These findings extend the mechanism of AI‐induced social presence from interactive behaviours to static identity cues, support anthropomorphic design in educational AI and employ MMLA to complement learning theory. This study also highlights that the long‐term deployment of such systems should account for potential risks, such as excessive emotional dependence. Practitioner notes What is already known about this topic? Online learning environments encounter challenges such as heightened learners' isolation, low engagement, and susceptibility to distraction, with social presence recognised as an important factor in mitigating these issues. Existing research on AI identity is still in its early stages, where the identity information typically assigned to AI remains relatively limited. Multimodal learning analytics (MMLA) leverages multisource data to provide more interpretable evidence for exploring complex learning processes. Effectively integrating MMLA with learning theory offers robust support for further theoretical development. What this paper adds? This study introduces the concept of identity anthropomorphism and shows that static identity cues of AI can evoke learners' sense of social presence. Identity‐anthropomorphised AI companions are associated with improved online learning outcomes, with performance comparable to human companions in this study. Social presence alone does not necessarily translate into better learning outcomes; its positive effects depend on the extent to which it fosters positive emotions. Learners from the humanities and social sciences are more responsive to identity anthropomorphism than those from science and engineering. Implications for practice and/or policy Educational technology developers and instructional designers can consider incorporating identity anthropomorphism into AI learning companions. Designing human‐like identity backgrounds for AI provides a relatively low‐cost approach to enhancing learning outcomes in online courses. When developing AI‐based learning companions, it is not sufficient to focus solely on increasing social presence. Designers should also ensure that the system supports positive emotions and helps offset attentional distractions introduced by anthropomorphic features. Policymakers and educational institutions should also consider potential unintended consequences, such as excessive emotional dependence and issues related to educational equity. These risks may be mitigated through transparent disclosure and ongoing evaluation.