Hacking AI Chatbots for Critical AI Literacy in the Library

Ford, H; Burrell, A; Monin, M; Narayan, B; Jethani, S

Hacking AI Chatbots for Critical AI Literacy in the Library

Ford, H Burrell, A Monin, M Narayan, B

Jethani, S

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Publisher:: Taylor & Francis
Publication Type:: Journal Article
Citation:: Journal of the Australian Library and Information Association, 2026, 75, (1)
Issue Date:: 2026-03-01

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Field	Value	Language
dc.contributor.author	Ford, H
dc.contributor.author	Burrell, A
dc.contributor.author	Monin, M
dc.contributor.author	Narayan, B https://orcid.org/0000-0001-8852-5589
dc.contributor.author	Jethani, S
dc.date.accessioned	2026-02-05T07:03:08Z
dc.date.available	2026-01-05
dc.date.available	2026-02-05T07:03:08Z
dc.date.issued	2026-03-01
dc.identifier.citation	Journal of the Australian Library and Information Association, 2026, 75, (1)
dc.identifier.issn	2475-0158
dc.identifier.issn	2475-0166
dc.identifier.uri	http://hdl.handle.net/10453/193020
dc.description.abstract	AI is seeping into the fabric of our information environment as generative AI tools are increasingly used to search for and discover information. Despite their promise for improving efficiency, AI systems regularly produce errors (also known as ‘hallucinations’), which demonstrate that uncertainty is a feature rather than a bug of such systems. Despite this problem, we regularly hear stories about people who have mistakenly used false information provided by these tools in their communications and outputs – from lawyers’ reports to government hearings. There is wide agreement about the need for AI literacy to recognise how to use AI effectively and ethically but less consensus on how AI literacy is best achieved. A key component of many AI literacy frameworks is an understanding of how AI works. Using a case study of a critical AI literacy intervention in four Greater Sydney libraries, we argue that instead of learning only about how AI works, AI literacy might involve learning when, how, and why AI doesn’t work. The concept of socio-technical error and uncertainty is a useful heuristic for understanding AI – particularly in the context of information search and discovery, a primary practice in both public and academic libraries.
dc.language	en
dc.publisher	Taylor & Francis
dc.relation	University of Technology Sydney
dc.relation.ispartof	Journal of the Australian Library and Information Association
dc.relation.isbasedon	10.1080/24750158.2026.2614000
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Hacking AI Chatbots for Critical AI Literacy in the Library
dc.type	Journal Article
utslib.citation.volume	75
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Research on Education in a Digital Society (CREDS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Research on Education in a Digital Society (CREDS)/Centre for Research on Education in a Digital Society (CREDS) Associate Members
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
pubs.organisational-group	University of Technology Sydney/UTS Groups/UTS LGBTIQA+ Research Network
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-02-05T07:03:05Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	75
utslib.citation.issue	1

Abstract:

AI is seeping into the fabric of our information environment as generative AI tools are increasingly used to search for and discover information. Despite their promise for improving efficiency, AI systems regularly produce errors (also known as ‘hallucinations’), which demonstrate that uncertainty is a feature rather than a bug of such systems. Despite this problem, we regularly hear stories about people who have mistakenly used false information provided by these tools in their communications and outputs – from lawyers’ reports to government hearings. There is wide agreement about the need for AI literacy to recognise how to use AI effectively and ethically but less consensus on how AI literacy is best achieved. A key component of many AI literacy frameworks is an understanding of how AI works. Using a case study of a critical AI literacy intervention in four Greater Sydney libraries, we argue that instead of learning only about how AI works, AI literacy might involve learning when, how, and why AI doesn’t work. The concept of socio-technical error and uncertainty is a useful heuristic for understanding AI – particularly in the context of information search and discovery, a primary practice in both public and academic libraries.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/193020