arXiv subjects

Karolina Trajkovska

Publications and source records attributed to Karolina Trajkovska.

4 recordsLinked to original sources

arXiv2026

VocabulARy 2.0: A Generative AR System Enabling Personalised 3D Mnemonics for Vocabulary Learning in Context

Mnemonics support vocabulary learning by linking unfamiliar words to familiar concepts through memorable verbal and visual associations. We present a generative AR system that enables learners to create personalised 3D mnemonics by transforming familiar objects in their surroundings into situated mnemonic anchors. The system scans the environment, detects visible objects, and generates bilingual flashcards. After selecting a word, the learner describes a personal association. The system combines this description with an image of the selected object to generate a textured 3D model using generative AI. Learners can then place the model relative to the real object, transforming it into a situated mnemonic anchor.

cs.HC↗

arXiv2026

Personalised 3D Mnemonics for Vocabulary Learning in Context

Augmented reality (AR) can support vocabulary learning by placing mnemonic cues in the physical contexts where new words are encountered. Prior work has augmented real objects with bilingual labels, audio, keywords, and 3D visualisations of those keywords interacting with the objects, but these cues are typically predefined by system designers. This limits scalability and reduces learners' ability to construct personal mnemonic links. By grounding mnemonics in objects that learners already recognise, use, and associate with prior experiences, a system could connect new vocabulary to existing mental models rather than requiring learners to adopt designer-defined associations. We outline a proof of concept and a storyboard of a generative AR approach in which learners create personalised 3D mnemonics by transforming familiar objects in their surroundings into situated mnemonic anchors. We position this concept within a timeline of related work and argue that recent advances in generative 3D asset creation enable a new design point: personalised 3D mnemonics that do not merely place content in the physical learning environment, but modify familiar real-world objects so that they become part of the mnemonic itself.

cs.HC↗

arXiv2024

Gaze2AOI: Open Source Deep-learning Based System for Automatic Area of Interest Annotation with Eye Tracking Data

Eye gaze is considered an important indicator for understanding and predicting user behaviour, as well as directing their attention across various domains including advertisement design, human-computer interaction and film viewing. In this paper, we present a novel method to enhance the analysis of user behaviour and attention by (i) augmenting video streams with automatically annotating and labelling areas of interest (AOIs), and (ii) integrating AOIs with collected eye gaze and fixation data. The tool provides key features such as time to first fixation, dwell time, and frequency of AOI revisits. By incorporating the YOLOv8 object tracking algorithm, the tool supports over 600 different object classes, providing a comprehensive set for a variety of video streams. This tool will be made available as open-source software, thereby contributing to broader research and development efforts in the field.

cs.SE↗

arXiv2022

VocabulARy replicated: comparing teenagers to young adults

A critical component of user studies is gaining access to a representative sample of the population researches intend to investigate. Nevertheless, the vast majority of human-computer interaction (HCI)studies, including augmented reality (AR) studies, rely on convenience sampling. The outcomes of these studies are often based on results obtained from university students aged between 19 and 26 years. In order to investigate how the results from one of our studies are affected by convenience sampling, we replicated the AR-supported language learning study called VocabulARy with 24 teenagers, aged between 14 and 19 years. The results verified most of the outcomes from the original study. In addition, it also revealed that teenagers found learning significantly less mentally demanding compared to young adults, and completed the study in a significantly shorter time. All this at no cost to learning outcomes.

cs.HC↗