Name: From Quality to Transparency: Leveraging AI for Assessment and Version Tracking in Open Educational Resources
Start: 2026-10-09T11:05:00-0400
End: 2026-10-09T11:35:00-0400

All sessions are available online except round tables, special activities, and workshops.

From Quality to Transparency: Leveraging AI for Assessment and Version Tracking in Open Educational Resources

Friday October 9, 2026 11:05am - 11:35am EDT

6 DR4

ID: 32776

Open Educational Resources (OER) are designed to be openly reused, revised, and remixed, resulting in continuous and often decentralized evolution of content. While this dynamic nature is central to the ethos of open education, it also creates persistent challenges related to quality assurance, transparency, and the fair recognition of contributors. In current OER ecosystems, quality evaluation is frequently manual, subjective, and difficult to scale, while existing versioning mechanisms primarily document structural changes without capturing their semantic, pedagogical, or epistemic impact. As a result, it remains unclear how individual contributions influence the overall quality of a resource over time.This presentation proposes two novel perspectives: 1. AI-driven content quality assessment and 2. AI-based version tracking. Building on recent advances in generative AI and natural language processing, we explore how large language models and semantic evaluation techniques can be used to assess textual OER along multiple criteria. These criteria are operationalized to enable systematic, scalable, and partially explainable assessments that approximate human judgment while maintaining consistency across large collections of resources.Crucially, this work extends the role of quality assessment beyond static evaluation. By comparing successive versions of an OER, AI-based assessments can be used to measure how specific edits influence quality dimensions. Based on this foundation, the presentation introduces an AI-driven approach to version tracking that integrates semantic comparison with quality-aware evaluation. The proposed framework identifies meaningful changes between versions, classifies them according to their functional and pedagogical relevance, and links them to shifts in quality metrics. Overall, this research positions AI not as a replacement for human judgment, but as an augmentative tool that can enhance transparency, scalability, and fairness in OER practices. It offers a conceptual and technical foundation for rethinking how quality, contribution, and evolution are interconnected in the next generation of open educational infrastructures.

Speakers

Shahla Rasulzade

PhD candidate, DIPF | Leibniz Institute for Research and Information in Education

I am Shahla Rasulzade, a PhD candidate in Computer Science and a system architect working on the OERInfo project, funded by the German Federal Ministry of Education and Research (BMBF). My research focuses on the intersection of artificial intelligence and Open Educational Resources... Read More →

Friday October 9, 2026 11:05am - 11:35am EDT
6 DR4 MIT Samberg Conference Center, 50 Memorial Drive, Cambridge MA 02139 USA

Presentation

Speaker Modality Hybrid
Track Exploring Emergent Technologies and the Future of Openness

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OEGlobal 2026

Shahla Rasulzade

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Shahla Rasulzade

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