AI avatars in education: effects on knowledge acquisition, academic motivation, and cognitive load

As digital learning environments increasingly incorporate artificial intelligence, empirical evidence regarding the educational effectiveness of AI-powered digital avatars remains limited and methodologically heterogeneous. This randomized matched-pair study examined whether learning with a human-like AI-avatar instructional system implemented in Unity 3D with Convai and OpenAI GPT-4o for real-time verbal interaction improves knowledge acquisition, academic motivation, and perceived cognitive load compared with autonomous internet-based learning. Forty-eight university students were included in the final analytic sample and were balanced across instructional condition and gender (AI-avatar: n = 24; control: n = 24; 12 males and 12 females in each condition). Participants completed an instructional task on introductory artificial intelligence and neural-network concepts, followed by a 20-item knowledge test, the Academic Motivation Scale, and a shortened Cognitive Load Scale. Separate 2 x 2 analyses of variance tested the effects of instructional condition and gender for each dependent variable. Reliability was satisfactory for the Academic Motivation Scale (pre-test Cronbach's α = 0.88; post-test α = 0.97) and the Cognitive Load Scale (α =.77). The AI-avatar group obtained higher knowledge-test scores than the control group (M = 10.63, SD = 2.39 vs. M = 8.92, SD = 3.11), F(1, 44) = 4.66, p = 0.036, partial η ² = 0.096. Academic motivation showed a more favorable pre-to-post trajectory in the AI-avatar condition (ΔSDI M = -1.15, SD = 3.67) than in the control condition (ΔSDI M = -4.36, SD = 3.70), F(1, 44) = 9.22, p = 0.004, partial η ² = 0.173; the unadjusted post-test SDI condition effect approached significance (p = 0.051), and an ANCOVA controlling for baseline motivation was significant (p = 0.006). Perceived cognitive load did not differ significantly between conditions, F(1, 44) = 0.16, p = 0.688. No significant gender main effects or condition-by-gender interactions emerged for the main outcomes. These findings suggest that AI-avatar-supported instruction may enhance knowledge acquisition and support motivational regulation, while evidence for cognitive-load reduction was not observed in the present data. The novelty of the study lies in its direct empirical comparison of next-generation, human-like AI-avatar learning support with conventional self-directed online learning, while also reporting outcome-specific analyses and limitations relevant to reproducibility.