IWILDS'25: The 5th International Workshop on Investigating Learning During Web Search
Web-based learning is evolving rapidly as traditional search engines are complemented by Large Language Models (LLMs) and other AI technologies. This evolution offers new opportunities, such as automated information synthesis and personalized learning experiences. However, this also presents new challenges, including the need for learners to be aware of potential biases and misinformation in AI-generated content, and to maintain focus and depth in their learning journeys.
Authors
- Nilavra Bhattacharya (ORCID: https://orcid.org/0000-0001-7864-7726)
- Ran Yu (ORCID: https://orcid.org/0000-0002-1619-3164)
- Anett Hoppe (ORCID: https://orcid.org/0000-0002-1452-9509)
- Jiqun Liu (ORCID: https://orcid.org/0000-0003-3643-2182)
Institutions
- University of Bonn (DE)
- Technische Informationsbibliothek (TIB) (DE)
- L3S Research Center (DE)
- ABB (Germany) (DE)
- University of Oklahoma (US)
Publication Details
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1145/3701551.3705709
- Citations
- 1
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 2.48