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Cited by 18Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationDigital Education and Society

Editorial: Artificial Intelligence is Awesome, but Good Teaching Should Always Come First.

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Crawford, Joseph, Vallis, Carmen, Yang, Jianhua, Fitzgerald, Rachel, O'Dea, Christine, Cowling, Michael·2024·Journal of University Teaching and Learning Practice
DOI 10.53761/1.20.7.01
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Abstract

The explosion of generative artificial intelligence into the mainstream of society some twelve months ago has seriously challenged learning and teaching practice. Since then, AI companies such as OpenAI are constantly improving their language models and releasing new features to make them more capable and useful. So, given there have been many disruptors in the past and emerging disruptions in the present, what can we learn in this situation, where Generative AI stands poised to challenge the purpose and relevance of assessment models? From our examples, disruptive technologies only have a major impact when they positively transform practice and are informed by pedagogic models and learning theory. GenAI as a disruptor is only likely to have this positive impact when it informs quality learning and teaching practice. We should be focused on the opportunities that GenAI now presents to higher education. It is argued here and elsewhere that the relative weakness of GenAI is that it creates poor quality output, delivering uninformed, incorrect, biased and bland responses. In itself, this offers opportunities for ‘teachable moments’ (Newell et al, 2023) and gives us room to support students with their capabilities in an AI informed world. Historically, these opportunities enable higher education to grow and progress. What we have learned so far would appears to be that for research to contribute to the literature, they needed to be informed by it. Likewise, need to ensure that pedagogy, andragogy, and heutagogy come first. We also need to remember that people processes happen, artificial intelligence happens around them, and that artificial intelligence comes after human intelligence.

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  • GenAI's inherent limitations (subpar quality, inaccuracies, biases, lack of originality) present fertile ground for 'teachable moments' to enhance student competencies in navigating an AI-influenced landscape.

    “Crawford et al. (2023c) posit that GenAI's very inherent limitations (leading to subpar quality, inaccuracies, biases, and a lack of originality) present fertile ground for 'teachable moments', thereby providing an avenue to enhance student competencies in navigating an AI-influenced landscape.”

    From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Crawford et al., 2023c

  • Many caution that GenAI benefits can only be obtained when balanced with good teaching.

    “Many nevertheless caution that its benefits can only be obtained when balanced with good teaching (e.g., [18, 23]) and many questions still remain [37].”

    From Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines (2025) · cites this paper as [18]

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Cited by

Sorted by citations

Authors
Crawford, J., Vallis, C., Yang, J., Fitzgerald, R., O'Dea, C., & Cowling, M.
Affiliations
  • University of Tasmania (AU)
  • The University of Sydney (AU)
  • University of Warwick (GB)
  • The University of Queensland (AU)
  • Central Queensland University (AU)
ORCID
Venue
Journal of University Teaching and Learning Practice
Publisher
Open Access Publishing Association
Published
2024-07-15
Volume
20
Issue
7
ISSN
1449-9789 (ISSN-L)
DOI
10.53761/1.20.7.01
OpenAlex
W4388461299
Cited by
18
References
8
Citations by year
2026: 2 · 2025: 10 · 2024: 6
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of University Teaching and Learning Practice (publishedVersion, cc-by-nd)
  • Research Portal (King's College London) (publishedVersion, cc-by-nd)
  • Research Online (University of Wollongong) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
Keywords
generative AI, university teaching, pedagogy, andragogy, heutagogy, evaluation model, disruptive technologies, teachable moments, student support, human intelligence, artificial intelligence in education

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