arXiv:2605.25502v1 Announce Type: new Abstract: Educational aspect-based sentiment analysis (ABSA) can support course improvement, but public aspect-labeled student feedback remains scarce because educational reviews are private, institution-specific, and expensive to annotate. This study introduces a controlled synthetic benchmark for educational ABSA built from 10,000 synthetic course reviews with explicit train-validation-test splits and a 20-aspect pedagogical schema spanning instructional quality, assessment and course management, learning demand, learning environment, and engagement. The
The scarcity of real-world, privacy-sensitive educational data necessitates synthetic approaches, aligning with current trends in AI development where data generation augments learning in niche domains.
This development can accelerate the application of AI-powered sentiment analysis in education, providing institutions with granular insights for course improvement without privacy hurdles, benefiting both learners and educators.
The availability of a controlled, synthetic benchmark lowers the barrier for AI researchers and developers to build and test educational aspect-based sentiment analysis models, independent of proprietary institutional data.
- · AI researchers in NLP
- · Educational technology providers
- · Academic institutions
- · Traditional manual feedback analysis methods
- · Institutions without AI adoption strategies
Improved educational course design and student experience through data-driven feedback analysis.
Increased adoption of AI tools within academic administration and teaching methodologies.
The benchmark might influence similar synthetic data generation efforts in other privacy-sensitive domains, fostering broader AI applications.
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