Cristina Cachero

Kim Zierahn, Cristina Cachero, Anna Korhonen et al.

A growing body of research examines personality traits in Large Language Models (LLMs), particularly in human-agent collaboration. Prior work has frequently applied the Big Five inventory to assess LLM behavior analogous to human personality, without questioning the underlying assumptions. This paper critically evaluates whether LLM responses to personality tests satisfy six defining characteristics of personality. We find that none are fully met, indicating that such assessments do not measure a construct equivalent to human personality. We propose a research agenda for shifting from anthropomorphic trait attribution toward functional evaluations, clarifying what personality tests actually capture in LLMs and developing LLM-specific frameworks for characterizing stable, intrinsic behavior.

Samuel Sepúlveda, Mauricio Diéguez, Gonzalo Farías et al.

Context: The importance of defining learning outcomes and the planning stage for a systematic literature review. Objective: A protocol for carrying out a systematic literature review about the evidence for the tool support for the learning outcomes and the teaching-learning process using Bloom's taxonomy to address it. Method: The definition of a protocol to conduct a systematic literature review according to the guidelines of B. Kitchenham. Results: A validated protocol to conduct a systematic literature review. Conclusions: A proposal for the protocol definition of a systematic literature review about the tool support for the learning outcomes, the teaching-learning process using Bloom's taxonomy was built. Initials results show that a more detailed review of the learning outcomes and their alignment with the levels of curricular progress, training cycles, and Bloom's Taxonomy should be carried out.