The DOI matches its Crossref record, with a small difference in title or year; 56 of the 56 references it cites were found in a registry or an index.
Generative AI’s Impact on Critical Thinking: Revisiting Bloom’s Taxonomy
Open accessAbstract
The integration of generative artificial intelligence (AI) tools like ChatGPT in education has raised concerns that students may become dependent on AI-generated solutions, potentially stifling the development of critical thinking skills. Compounding this issue is the fact that Bloom’s Taxonomy—the widely used framework for designing educational goals—fails to address the cognitive demands of AI-assisted learning. This exploratory study presents a revised framework that incorporates AI-specific competencies, offering a more relevant model for nurturing critical thinking in an AI-driven environment. Using a conceptual approach supported by empirical evidence from MSc Marketing students’ interactions with AI tools over 4 weeks, the study found that AI can both enhance and challenge critical thinking across cognitive, affective, and metacognitive domains. Key elements such as melioration, ethical reasoning, collaboration, and reflective thinking were identified as critical for developing deeper engagement with AI-generated content. The framework proposes 12 propositions that inform future research and pedagogical strategies. This study outlines a research agenda for examining AI’s impact on cognitive development, serving as a resource for educators, policymakers, and researchers seeking to adapt teaching methods for AI-assisted education.
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In the citing paper
“Gonsalves [80] found that students moved fluidly across cognitive, affective, and metacognitive domains and identified 12 AI-specific competencies (e.g., melioration, ethical reasoning) underpinning a revised Bloom's taxonomy.”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [80]
In the citing paper
“Key moderating factors were identified, including proper instructional design, where integration of AI in education necessitates a revised framework that incorporates AIspecific competencies to effectively nurture CT [80].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [80]
In the citing paper
“Overreliance on GenAI tools can lead to cognitive dependency, diminishing students' ability to formulate original arguments, critically assess alternative perspectives and decision-making processes [33,80,100].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [80]
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Metadata
- Authors
- Gonsalves, C.
- Affiliations
- King's College London (GB)
- Corresponding author
- Gonsalves, Chahna
- ORCID
- Gonsalves, Chahna
- Venue
- Journal of Marketing Education
- Publisher
- SAGE Publications
- Published
- 2024-12-23
- Volume
- 48
- Issue
- 1
- Pages
- 4-19
- ISSN
- 0273-4753 (ISSN-L)
- 1552-6550
- OpenAlex
- W4405726304
- Cited by
- 161
- References
- 56
- Citations by year
- 2026: 96 · 2025: 65
- FWCI
- 60.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Marketing Education (publishedVersion, cc-by-nc)
- Research Portal (King's College London) (submittedVersion, cc-by-nd)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Education and Critical Thinking Development
- Social Sciences › Education › Educational Assessment and Pedagogy
- Keywords
- generative AI in education, ChatGPT, critical thinking, Bloom's taxonomy, AI-assisted learning, metacognition, melioration, moral reasoning, cooperative learning, reflective thinking, marketing education, AI dependency, cognitive development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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