TY - JOUR
T1 - Your Information ZODIAC
T2 - An Information Evaluation Framework for the Age of AI
AU - Reagan, Kevin
AU - Coates, Kay
AU - Swaringen, Jessica
PY - 2025/12/9
Y1 - 2025/12/9
N2 - Building on information evaluation mnemonics like the CRAAP Test, SIFT, and ACT UP, among others, the authors propose the ZODIAC of information evaluation for an AI-dominated information environment: Zooming in, Other opinions, Dataset, Intent, Authenticity, and Consistency. Each letter introduces students to considerations unique to the critical thinking considerations of AI-generated information, such as datasets and their large language models, evaluating the purpose of a given generative AI, and questioning the authenticity and consistency of generative AI output. Additionally, as an introductory framework geared toward first-year students, the ZODIAC evaluation method does not fully address all aspects of information literacy; social and environmental aspects of AI literacy are noticeably absent. Accordingly, the authors address social and environmental considerations in the discussion, which serves to inspire other information and AI literacy professionals to build a more holistic framework of evaluating AI-generated information.
AB - Building on information evaluation mnemonics like the CRAAP Test, SIFT, and ACT UP, among others, the authors propose the ZODIAC of information evaluation for an AI-dominated information environment: Zooming in, Other opinions, Dataset, Intent, Authenticity, and Consistency. Each letter introduces students to considerations unique to the critical thinking considerations of AI-generated information, such as datasets and their large language models, evaluating the purpose of a given generative AI, and questioning the authenticity and consistency of generative AI output. Additionally, as an introductory framework geared toward first-year students, the ZODIAC evaluation method does not fully address all aspects of information literacy; social and environmental aspects of AI literacy are noticeably absent. Accordingly, the authors address social and environmental considerations in the discussion, which serves to inspire other information and AI literacy professionals to build a more holistic framework of evaluating AI-generated information.
KW - Artificial Intelligence (AI)
UR - https://doi.org/10.33011/newlibs/19/9
U2 - 10.33011/newlibs/19/9
DO - 10.33011/newlibs/19/9
M3 - Article
SN - 2471-3880
VL - 10
SP - 94
EP - 109
JO - Journal of New Librarianship
JF - Journal of New Librarianship
IS - 2
ER -