Balancing warmth and clarity: an experimental evaluation of interactive features for engagement in GenAI chatbots

Media Richness Theory (MRT) distinguishes two core functions of communication media: reducing task ambiguity (informational richness) and conveying socio-affective cues (social presence). Yet, little is known about how these two theoretically distinct dimensions operate – independently or interactively – when instantiated in generative AI (GenAI) chatbots for language learning. This study experimentally disentangles these two functions by manipulating emoji use (operationalizing socio-emotional warmth) and corrective feedback (operationalizing instructional clarity) in a 2 (emoji: absent vs. present) × 2 (feedback: none vs. corrective) between-groups experiment with 96 intermediate-level Chinese learners. Results indicated that (1) Emoji use did not exert significant effects on behavioral, cognitive, or emotional engagement. (2) Corrective feedback, however, demonstrated significant effects on cognitive and emotional engagement, while its effect on behavioral engagement was not significant. (3) No significant interaction between emoji use and corrective feedback was observed. The findings provide an evidence-based, innovative design heuristic for prioritizing feature development in ‌AI-assisted interactive learning environments‌, offering educators and designers a novel, actionable framework for evaluating and selecting such technologies.