Storytelling Matters: Effects of Different Narrative Types in Immersive XR Avatar-Based Learning
Year: 2026
Type: conference-paper
Abstract: This study investigates the impact of three delivery styles (Humorous, Enthusiastic, and Formal) presented by a life-sized holographic avatar on user experience, narrative engagement, and short-term learning in an instructional scenario about industrial robots. The instructional content was kept constant across conditions, differing only in delivery style. Quantitative performance measures, the Narrative Engagement Measure (NEM), and qualitative feedback were combined in a mixed-methods evaluation. Results indicated overall learning success across all conditions, although a descriptive trend suggests that the ‘Humorous’ and ‘Enthusiastic’ styles may better maintain short-term learning performance during later or potentially more demanding content segments compared to ‘Formal’ narratives, indicating that avatar-mediated explanations can support immediate knowledge acquisition in short instructional sessions. Although no statistically significant differences were found between delivery styles in the NEM analysis, descriptive results revealed distinct learning patterns across the instructional sequence. The Enthusiastic condition showed relatively stable learning gains across robot types, while the Formal condition showed a marked decrease from the Articulated robot (100%) to the Cartesian (83.3%) and Delta (58.3%) robots. The Humorous condition maintained high learning gains across robot types and was associated with more positive subjective feedback. Learning outcomes also varied by robot content, with gain rates decreasing from Articulated (97.2%) and Cartesian (91.7%) to Delta (80.6%), which was presented in the final instructional segment. These findings are subject to limitations regarding the potential confounding of robot type with presentation order, which may have influenced learning dynamics. Overall, the results indicate that delivery style may shape users’ experiential perception and short-term learning dynamics, supporting its consideration as a design parameter in holographic avatar-based instruction.
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7204232552