Examining the Cognitive Demand of AI-Generated Learning Tasks.

Pre-Service Teachers’ Assessments Using the Revised Bloom’s Taxonomy

Autori

Ludovica Rizzo; Stefania Pinnelli.

Abstract

In recent years, generative AI systems have increasingly been introduced as tools to support instructional design and personalised teaching. Drawing on Anderson and Krathwohl’s (2001) Revised Bloom’s Taxonomy, this study examines the alignment of AI-generated learning tasks with specific cognitive process levels, alongside pre-service teachers’ evaluations of this alignment. Adopting an Instructional Design Case Studies approach, the research involved 200 fourth-year Primary Education students, organised into 50 working groups. Qualitative analyses reveal that lower- and intermediate-level tasks were more frequently assessed as aligned, whereas higher-order tasks showed greater ambiguity and misalignment. The findings position generative AI not as a substitute for teachers’ instructional design competence, but as a reflective mediator supporting learning task design and professional AI literacy. The contribution will discuss both the strengths and limitations of AI-generated responses.

Keywords

Generative AI; Instructional design; Teacher education.

Bibliografia

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Anderson, L. W., & Krathwohl, D. R. (2001). A revision of Bloom’s taxonomy of educational objectives. Pearson.

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