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Bennie Bendiksen

dblp:383/2708 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0006-8867-2523ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 44% Immersive interaction · 44% Learning and educational technologies · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
conversational agents
0.912025
Effects of Embodiment and Personality in LLM-Based Conversational Agents · VR 2025
Immersive interaction
embodiment
0.912025
Effects of Embodiment and Personality in LLM-Based Conversational Agents · VR 2025

Methods — techniques the papers use, named apart from their topics

user study · 0.9LLM-based conversation · 0.9
YearPublicationVenuePosition
2025 Effects of Embodiment and Personality in LLM-Based Conversational Agents
abstract
This work investigates the effects of personality expression and embodiment in conversational agents. We extend a personality-driven conversational agent framework by integrating LLM-based conversation support to provide information about contemporary scientific topics. We describe a user study built on this system to evaluate two opposing personality styles using three models: a dialogue-only model that conveys personality verbally, an animated human model that expresses personality only through dialogue, and an animated human model expressing personality through dialogue and expressive animations. The users perceive all models positively regarding personality and learning outcomes; however, models with high personality traits are perceived as more engaging than those with low personality traits. We provide an analysis of personality perception, learning, and user experience.
Sinan Sonlu, Bennie Bendiksen, Funda Durupinar, Ugur Güdükbay
VR2
2024 Assessing Human Reactions in a Virtual Crowd Based on Crowd Disposition, Perceived Agency, and User Traits
abstract
Immersive virtual environments populated by real and virtual humans provide valuable insights into human decision-making processes under controlled conditions. Existing literature indicates elevated comfort, higher presence, and a more positive user experience when virtual humans exhibit rich behaviors. Based on this knowledge, we conducted a web-based, interactive study, in which participants were embodied within a virtual crowd with complex behaviors driven by an underlying psychological model. While participants interacted with a group of autonomous humanoid agents in a shopping scenario similar to Black Friday, the platform recorded their non-verbal behaviors. In this independent-subjects study, we investigated behavioral and emotional variances across participants with diverse backgrounds focusing on two conditions: perceived agency and the crowd’s emotional disposition. For perceived agency, one group of participants was told that the other crowd members were avatars controlled by humans, whereas another group was told that they were artificial agents. For emotional disposition, the crowd behaved either in a docile or hostile manner. The results suggest that the crowd’s disposition and specific participant traits significantly affected certain emotions and behaviors. For instance, participants collected fewer items and reported a higher increase of negative emotions when placed in a hostile crowd. However, perceived agency did not yield any statistically significant effects.
Bennie Bendiksen, Nana Lin, Jiehyun Kim, Funda Durupinar
ACM Trans. Appl. Percept.1