arXiv:2606.13684v1 Announce Type: cross Abstract: Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent. Prior machine learning (ML) and deep learning (DL) approaches reported strong within-dataset results, yet were rarely evaluated in cross-dataset settings, leaving real-world generalizability unclear; meanwhile, LLM effectiveness for Bloom question classification has not been systematically studied. We evaluated the cross-dataset generalization of existing ML/DL methods and assessed LLM
The proliferation of LLMs creates a timely need to evaluate their practical application in automating previously laborious educational tasks, pushing the boundaries of AI utility in the real world.
Improving the objectivity and efficiency of educational assessment through automated Bloom's taxonomy classification holds significant implications for instructor workload, pedagogical consistency, and the scalability of adaptive learning systems.
The research moves beyond theoretical within-dataset performance to address the critical real-world generalizability of AI models for educational assessment and systematically assesses LLM effectiveness in this domain.
- · Educational technology providers
- · Instructors
- · Students (through better assessment)
- · AI developers in education
- · Traditional manual assessment methods
- · AI models with poor cross-dataset generalization
Automated educational assessment tools become more reliable and widely adopted due to improved generalization.
Instructors are freed from substantial grading burdens, allowing more focus on personalized teaching and curriculum development.
The educational landscape could shift towards more dynamic, adaptive, and scalable learning paths, powered by consistent and objective AI-driven assessment.
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Read at arXiv cs.AI