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Sotheara Veng

dblp:397/5748 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0001-7534-0796ORCID · corroborated

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

Databases, data management, data science and information retrieval · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 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
Learning and educational technologies · 67% Human-AI interaction · 33%
Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
large language model interaction
0.912025
CoachGPT: A Scaffolding-based Academic Writing Assistant · SIGIR 2025
Learning and educational technologies › writing support
scientific writing assistant
0.912025
CoachGPT: A Scaffolding-based Academic Writing Assistant · SIGIR 2025
Learning and educational technologies
writing support
0.912025
CoachGPT: A Scaffolding-based Academic Writing Assistant · SIGIR 2025
Natural language and speech › Language models and text generation
large language model
0.312025
CoachGPT: A Scaffolding-based Academic Writing Assistant · SIGIR 2025

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

large language model · 1.7AI agent · 1.7
YearPublicationVenuePosition
2025 Analyzing Computational Thinking Gameplay: Identifying Struggles and the Role of Experience
Sotheara Veng, Ekaterina Bergwall, Teomara Rutherford
SIGCSE (2)1
2025 CoachGPT: A Scaffolding-based Academic Writing Assistant
abstract
Academic writing skills are crucial for students' success but can feel overwhelming without proper guidance and practice, particularly when writing in a second language. Traditionally, students ask instructors or search dictionaries, which are not universally accessible. Early writing assistants emerged as rule-based systems that focused on detecting misspellings, subject-verb disagreements, and basic punctuation errors but are inaccurate and lack contextual understanding. Machine learning-based assistants demonstrate a strong ability for language understanding but are expensive to train. Large language models (LLMs) have shown remarkable capabilities in generating responses in natural languages based on given prompts, but they have a fundamental limitation in education: they generate essays without teaching, which can have detrimental effects on learning when misused. To address this limitation, we develop CoachGPT, which leverages LLMs to assist academic writing for those with limited educational resources and those who prefer self-paced learning. CoachGPT is an AI agent-based web application that (1) takes instructions from experienced educators (2) converts instructions into sub-tasks, and (3) provides real-time feedback and suggestions using large language models. This unique scaffolding structure makes CoachGPT unique among existing writing assistants. Compared with existing writing assistants, CoachGPT provides a more immersed writing experience with personalized messages. Our user studies prove the usefulness of CoachGPT and the potential of large language models for academic writing.
Fumian Chen, Sotheara Veng, Joshua Wilson, Xiaoming Li 0010, Hui Fang 0001
SIGIR2