Thiemo Wambsganss
Scholar

Thiemo Wambsganss

Google Scholar ID: 4fsjAjoAAAAJ
Tenure Track Research Assistant Professor, Bern University of Applied Sciences
AI for EducationIntelligent Writing SupportConversational Agents
Citations & Impact
All-time
Citations
1,740
 
H-index
24
 
i10-index
45
 
Publications
20
 
Co-authors
18
list available
Publications
1 items
Reviewriter: AI-Generated Instructions For Peer Review Writing
Workshop on Innovative Use of NLP for Building Educational Applications · 2025
Cited
14
Resume (English only)
Academic Achievements
  • Published in Information Systems Research (2024): 'Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling'.
  • Co-authored CHI 2024 paper: 'A Design Space for Intelligent and Interactive Writing Assistants'.
  • Developed LegalWriter, an intelligent writing support system for novice law students, presented at CHI 2024.
  • Contributed to CHI 2024 work on generative AI in creative design, showing graphical interfaces outperform text prompts for ideation.
  • Published multiple papers at top venues including ACL 2021, ACL 2022, CHI 2020, CHI 2021, and CHI 2022.
  • Maintains academic profiles on Google Scholar and ResearchGate.
Background
  • Tenure-track Research Assistant Professor for Digital Technology Management at Bern University of Applied Sciences, Switzerland, and head of the Human-Centered AI-based Learning System (HAIS) Lab.
  • Research focuses on Human-Computer Interaction (HCI), integrating Natural Language Processing (NLP) and Machine Learning (ML).
  • Aims to understand how humans perceive, interact with, and learn from intelligent tools, and designs adaptive user interfaces to enhance digital work and learning experiences.
  • Driven by opportunities to leverage recent advances in NLP and ML to enable self-reliant, individualized learning independent of educators or background.
  • Three main research thrusts: 1) developing pedagogical conversational agents and intelligent writing support systems; 2) modeling student performance using numerical or textual data; 3) designing computational methods to intelligently deliver feedback and self-evaluation.