GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy

(2) Deakin University; (3) British University Vietnam; Australia; Jasper Roe (1); Leon Furze (2); Mike Perkins (3) ((1) Durham University; United Kingdom; Vietnam)

REVIEW 3 major objections 6 minor 8 references

GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy

T0 review · 3 major / 6 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper argues that AI-generated content is best understood as a new kind of 'digital plastic'—synthetic, useful, and persistent—and that this metaphor can anchor a badly needed critical AI literacy in classrooms.

desk verdict A genuinely new teaching metaphor—GenAI as digital plastic—with a coherent equity argument, but the persistence claim is asserted rather than tested and the strongest conclusions outrun the evidence. read the letter →

arxiv 2502.08249 v1 pith:XNYLGQKQ submitted 2025-02-12 cs.CY

Jasper Roe (1) , Leon Furze (2) , Mike Perkins (3) ((1) Durham University , United Kingdom , (2) Deakin University , Australia , (3) British University Vietnam , Vietnam) This is my paper · ORCID

classification cs.CY

keywords generativeAIdigitalplasticcriticalliteracymultiliteraciessyntheticmediamodelcollapseeducationalequityconceptualmetaphortheory

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that GenAI content behaves like physical plastic: it is cheap, versatile, and ubiquitous, yet it persists in digital environments, fragments into 'microplastics' of recycled data, and can degrade the digital ecosystem through model collapse. The authors argue this 'digital plastic' metaphor gives educators a transferable frame for teaching learners to notice, appraise, and evaluate synthetic media. They conclude that Critical Artificial Intelligence Literacy (CAIL) must become a core part of multiliteracies pedagogy, developed equitably across geographies and cultures. A sympathetic reader would care because the metaphor turns an abstract technological problem into a discussable object with both affordances and harms, rather than a simple good-or-bad judgment.

What carries the argument

The central object is the 'digital plastic' metaphor itself: a conceptual mapping, grounded in Conceptual Metaphor Theory, that treats GenAI output as a synthetic substance with plastic-like properties. The mapped properties carry the argument: GenAI is ubiquitous, malleable, and cheap like plastic; its outputs persist as discoverable traces; when those traces are recycled into training data they become 'digital microplastics' that can trigger model collapse. The metaphor does the pedagogical work of turning an abstract technological property into a discussable object with affordances and harms. Operationalizing it, CAIL—the ability to notice, appraise, and evaluate synthetic media—is the literacy skill that lets learners use the metaphor to navigate these effects.

What would settle it

A controlled longitudinal study could settle the claim: measure the proportion of AI-generated text in a public corpus over several years and train successive models on each snapshot; if the share does not accumulate, or if recursive training does not degrade output quality, the persistence-and-pollution mapping at the core of the metaphor fails.

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Extended reading notes

Core claim

The paper's central claim is that the 'digital plastic' metaphor, grounded in Conceptual Metaphor Theory and the multiliteracies tradition, captures both sides of GenAI: it lowers barriers to creative and academic production while leaving persistent, accumulating traces that can pollute digital environments. The authors argue that GenAI content mirrors physical plastic's persistence, ubiquity, malleability, and toxicity; the recycling of AI-generated output into training data is described as digital microplastics, leading to model collapse. From this they derive a pedagogical demand: multiliteracies must integrate CAIL—the ability to notice, appraise, and evaluate synthetic media—so that learners worldwide can identify the effects of digital plastics and microplastics. The framework is presented as a theoretical foundation for future curriculum design rather than an empirical demonstration.

Load-bearing premise

The framework's load-bearing premise is that AI-generated content genuinely accumulates and persists in digital environments, and that recycling it into training data degrades those environments, closely enough for the plastic analogy to teach something true.

Editorial extensions

If this is right

Multiliteracies curricula should add Critical AI Literacy (CAIL) as a core component, taught alongside reading, writing, and multimodal composition.
Teachers can use the digital plastic metaphor to help students move from noticing GenAI output to evaluating its persistence, bias, and origins.
Assessment frameworks should treat authorship as a spectrum of human-AI collaboration rather than a simple human-or-machine binary.
Equity policy should treat unequal access to quality GenAI tools and CAIL instruction as a new dimension of the digital divide.
Institutions managing training data should treat AI-generated content as a contaminant to be tracked and filtered, to prevent model collapse.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension the paper leaves implicit: if the metaphor is real, then platform-level labeling and filtering of synthetic content should improve information quality in the same way waste management reduces plastic pollution; one could measure whether labeled AI content is less likely to be reused in training data.
The metaphor could be turned into a curriculum audit tool—a 'digital plastic lifecycle' that traces a piece of synthetic media from generation to reuse to potential collapse—giving teachers a concrete checklist rather than an abstract analogy.
The equity argument implies a measurable prediction: learners who receive CAIL instruction should be better at spotting AI-generated text and scams than equally resourced peers who do not, a difference that could be tested with pre/post assessments.
A caution the paper does not develop: if plastic is the model, then restricting synthetic content may create access harms, since free GenAI is often the only writing support available to under-resourced writers; any CAIL curriculum must balance critique with access.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Jasper—quick take. This is a conceptual position paper, not an empirical study, and judged on those terms it mostly works. The digital plastic metaphor is a real addition: I don't know prior work that systematically maps GenAI content to plastic's affordances and harms, and the extension to 'digital microplastics' via model collapse is memorable and teachable. The authors ground it in Conceptual Metaphor Theory and multiliteracies, and they use the frame to make a sensible equity point: CAIL is not just a technical skill but a justice issue, since access and exposure to synthetic media are unevenly distributed. The paper is clearly written and well-connected to the existing literacy/AI literature; the self-citations sit within a coherent research program, not padding.

The soft spot is exactly where the stress-test note lands. The Persistence paragraph is the load-bearing step, and the inference from physical microplastics to digital traces is asserted, not demonstrated. Physical plastics persist because of molecular structure; digital content persists only insofar as platforms and licenses preserve it, and it can be deleted, filtered, or curated. Model collapse is a training-dynamics effect, not an observed degradation of a digital ecosystem. That doesn't sink the metaphor—metaphors don't require perfect source-target alignment—but the paper doesn't acknowledge the mismatch, and the abstract's claim that the metaphor provides 'a theoretical foundation' overstates what a metaphor can do. Relatedly, the conclusion's 'vital' and 'must' language is prescriptive; there is no classroom evidence yet, and the authors could more accurately say 'propose' rather than 'foundation.'

Minor but real: one sentence is truncated ('these mod.'), and a couple of citations (Gray; Singh Chawla) are doing heavy lifting for claims about prevalence. These are fixable, not fatal.

Who this is for: literacy studies, ed tech, and anyone designing GenAI curricula. It deserves serious refereeing—the metaphor is likely to be reused, and the equity framing strengthens it—but a referee should push for modest claims and an explicit paragraph on the metaphor's limits. I'd send it out.

Referee Report

3 major / 6 minor

Summary. This conceptual paper proposes 'digital plastic' as a metaphor for understanding Generative AI (GenAI) content, drawing parallels with physical plastic in terms of malleability, ubiquity, persistence, and potential toxicity. The authors argue that this metaphor, grounded in Conceptual Metaphor Theory (CMT), offers a theoretical foundation for Critical Artificial Intelligence Literacy (CAIL) within a multiliteracies framework. The paper first sets out the equity implications of GenAI, then develops the digital-plastic analogy through properties such as low cost, versatility, and persistence, and concludes with a call to integrate CAIL into multiliteracies pedagogy as a new skill that must be developed equitably across geographies and cultures.

Significance. If the central analogy is accepted, the paper offers a memorable and potentially productive educational frame: learners could be taught to notice, appraise, and evaluate synthetic media by reflecting on its 'plasticky' properties. The paper draws on reputable empirical sources for model collapse (Shumailov et al., 2024), AI text detection (Weber-Wulff et al., 2023), and bias (Nicolleti & Bass, 2023), and it is transparent about the authors' own use of GenAI in drafting. The equity dimension is a genuine strength: the authors connect access to premium GenAI tools with disparities in learning outcomes, which is a salient and important concern. However, the load-bearing premise—that GenAI content persists and accumulates like non-biodegradable plastic—is asserted rather than systematically defended, and the paper does not derive concrete pedagogical practices from the metaphor. The result is a stimulating essay that does not yet fully support its strong theoretical and curricular claims.

major comments (3)

[Exploring GenAI Content as Digital Plastic (Persistence paragraph)] The analogy between physical plastic's environmental persistence and the supposed persistence of digital content is the central load-bearing step of the paper, but it is not established. Physical plastics endure because of their molecular stability; digital content endures only when platforms, licenses, and administrators preserve it, and it can be deleted, filtered, or overwritten. The cited evidence does not close this gap: Tang and Eaton (2024) show that AI-generated text leaves detectable footprints in scholarly publications, but detectability is not irreversibility, and Shumailov et al. (2024) describe model collapse as a training-dynamics effect that can be mitigated by data curation, not as an observed degradation of a persistent digital ecosystem. To support 'digital microplastics' and the claim that GenAI content creates 'enduring layers of synthetic content,' the paper must either provide evidence of irreversible accumulation or explicitly reframe the metaphor as a partial, deliberately heuristic device rather than an empirical equivalence.
[Abstract and Conclusion] The paper claims that the digital plastic metaphor 'provides a theoretical foundation' for CAIL and that multiliteracies 'must integrate' CAIL, but the argumentative link between the metaphor and specific competencies is missing. The text does not derive any concrete CAIL sub-skills from the metaphor (e.g., what it means to notice plasticity, to assess toxicity, or to respond to persistence) nor does it propose any teaching activities or assessment types that follow from the analogy. Without this derivation, the metaphor is illustrative rather than foundational, and the recommendation to integrate CAIL stands on its own premises about GenAI harms rather than on the plastic analogy. The authors should specify the pedagogical entailments of the metaphor and explain how it changes what learners should be taught.
[Exploring GenAI Content as Digital Plastic (opening)] The paper asserts that 'plastic is a more apt interpretation' than the 'monoculture' metaphor (Messeri & Crockett, 2024) but provides no explicit criteria for evaluating competing metaphors. The monoculture metaphor captures homogeneity and fragility, while plastic adds persistence and toxicity; these have different pedagogical and normative implications. Since the paper's novelty rests on the superiority of digital plastic, the comparison needs to be argued rather than asserted. The authors should state the criteria (e.g., salience, generative power, alignment with multiliteracies goals) and show how the plastic metaphor scores higher, or acknowledge that the two metaphors are complementary and should be used together.

minor comments (6)

[Introduction, third paragraph] The sentence 'as AI text detection has been shown to be fallible and unreliable ... and with the ever-increasing technological improvements of these mod' is truncated mid-word; 'mod' should be 'models' and the sentence should be completed.
[References and text] The name 'Nicolleti, L., & Bass, D.' appears in both the text and the reference list; the correct spelling is 'Nicoletti'.
[References, Bozkurt et al. (2023)] The reference list entry for Bozkurt et al. (2023) contains a duplicated author 'Stewart, B.'; the duplicate should be removed.
[Exploring GenAI Content as Digital Plastic (sentence on Kalantzis & Cope)] The sentence 'Kalantzis and Cope (2024, p.16) . argue that' contains a stray period before 'argue'; this should be corrected to 'Kalantzis and Cope (2024, p.16) argue that'.
[Throughout] The terms 'AI-generated content,' 'synthetic media,' and 'GenAI content' are used interchangeably; the paper would benefit from a precise definition of the target domain of the metaphor at the outset.
[Conclusion] The paper uses 'digital ecosystem' in the abstract and conclusion but never defines what counts as a digital ecosystem; a working definition would help delineate the scope of the claimed analogy.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper proposes a conceptual metaphor and a normative educational recommendation; no prediction or fitted quantity is derived from its own inputs.

full rationale

The paper contains no equations, fitted parameters, or empirical predictions, so the core circularity patterns (self-definitional equations, fitted input called prediction, uniqueness theorem chains) do not apply. The central proposal is a conceptual metaphor: GenAI content is framed as 'digital plastic' using analogical properties (synthetic, ubiquitous, useful, potentially toxic, persistent). This is an act of metaphor construction under Conceptual Metaphor Theory, not a derivation whose conclusion is already contained in the premises. The recommendation that multiliteracies should integrate CAIL follows from the stated premises about GenAI's pervasiveness and harms, plus the definition of CAIL given in the paper itself ('the ability to understand, appraise, and evaluate synthetic, AI-produced text and media'). That definition is independent of the conclusion that CAIL should be taught. Self-citations appear (e.g., Roe, Furze, and Perkins, 2024, for the CAIL term and metaphor examples; Furze et al., 2024, for assessment frameworks), but they are used to name prior work and provide context, not as the sole load-bearing evidence for the paper's central claim. The persistence analogy, which equates GenAI traces with microplastics and invokes model collapse, is an evidential and analogical-validity concern, not circularity: the paper does not define 'digital plastic' in terms of 'CAIL' or vice versa, and it does not present the analogy's conclusion as a fitted output. The self-referential declaration that GenAI tools were used in drafting is a transparency note and does not make the argument circular. Overall, the derivation chain is self-contained in the required sense: the metaphor and CAIL recommendation could be unconvincing, but they are not true by construction or by self-citation.

Assumptions & free parameters 0 free parameters · 5 assumptions · 2 invented entities

This is a conceptual paper with no free parameters or empirical fits. Its burden is carried by theoretical axioms (CMT, multiliteracies, equity) and by the central analogical assumption that AI content behaves like plastic. The two invented entities are the metaphor and its microplastics extension; neither has independent empirical evidence beyond the cited phenomena they repackage.

assumptions (5)

domain assumption Metaphors are constitutive of thought and social reality, not merely stylistic devices (Conceptual Metaphor Theory).
Invoked in 'Creating New Metaphors' via Lakoff and Johnson (2003); the whole framework depends on metaphors shaping understanding.
domain assumption Multiliteracies and educational justice are the appropriate evaluative lens for GenAI's impact on learning.
The paper's normative frame comes from the New London Group and is not defended against alternative frameworks.
ad hoc to paper GenAI content is persistent, accumulates in training data, and degrades future models in ways analogous to plastic pollution.
The central analogy is asserted in 'Exploring GenAI Content as Digital Plastic' and relies on Shumailov et al.'s model collapse; the analogy's pedagogical value is not empirically established.
domain assumption Widespread access to GenAI tools will continue to be unequal across geographies and income levels.
Used to motivate the equity argument; supported by cited works (Miao and Holmes, Chandel and Lim) but taken as background.
domain assumption Students need critical AI literacy to navigate synthetic media, and such literacy can be taught.
The paper's central prescription presupposes that CAIL is teachable and beneficial; no intervention evidence is provided.

invented entities (2)

digital plastic (conceptual metaphor)
purpose: Frames GenAI content as synthetic, useful, persistent, and potentially toxic to guide teaching of critical AI literacy.
The paper introduces this metaphor as its central contribution; it has no falsifiable prediction outside the paper and is evaluated by heuristic usefulness.
digital microplastics (conceptual extension)
purpose: Describes fragments of AI-generated content accumulating in training data and degrading future models, linking to model collapse.
The term is coined in this paper as an extension of the plastic metaphor; the underlying model-collapse phenomenon is cited from Shumailov et al., but the entity itself is rhetorical, not measured.

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Cite this review

Pith. "Pith review of GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy." pith.science (2026). https://pith.science/paper/XNYLGQKQ

@misc{pith2026250208249,
  author       = {Pith},
  title        = {Pith review of: GenAI as Digital Plastic: Understanding Synthetic Media Through Critical AI Literacy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XNYLGQKQ}},
  note         = {Machine review of arXiv:2502.08249}
}

read the original abstract

This paper introduces the conceptual metaphor of 'digital plastic' as a framework for understanding the implications of Generative Artificial Intelligence (GenAI) content through a multiliteracies lens, drawing parallels with the properties of physical plastic. Similar to its physical counterpart, GenAI content offers possibilities for content creation and accessibility while potentially contributing to digital pollution and ecosystem degradation. Drawing on multiliteracies theory and Conceptual Metaphor Theory, we argue that Critical Artificial Intelligence Literacy (CAIL) must be integrated into educational frameworks to help learners navigate this synthetic media landscape. We examine how GenAI can simultaneously lower the barriers to creative and academic production while threatening to degrade digital ecosystems through misinformation, bias, and algorithmic homogenization. The digital plastic metaphor provides a theoretical foundation for understanding both the affordances and challenges of GenAI, particularly in educational contexts, where issues of equity and access remain paramount. Our analysis concludes that cultivating CAIL through a multiliteracies lens is vital for ensuring the equitable development of critical competencies across geographical and cultural contexts, especially for those disproportionately vulnerable to GenAI's increasingly disruptive effects worldwide.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

8 extracted references · 6 canonical work pages

[1]

Learning Outcomes

Alser, M., & Waisberg, E. (2023). Concerns with the Usage of ChatGPT in Academia and Medicine: A Viewpoint. American Journal of Medicine Open, 9, 100036. https://doi.org/10.1016/j.ajmo.2023.100036 Barthes, R. (1957). Mythologies. Éditions du Seuil. Batten, A. J. (2012). Metaphors We Teach By: The Language of “Learning Outcomes.” Teaching Theology & Religi...

work page arXiv 2023
[4]

contamination

https://doi.org/10.14742/ajet.9434 Garcia, A., Luke, A., & Seglem, R. (2018). Looking at the Next 20 Years of Multiliteracies: A Discussion with Allan Luke. Theory Into Practice, 57(1), 72–78. https://doi.org/10.1080/00405841.2017.1390330 Gray, A. (2024). ChatGPT “contamination”: Estimating the prevalence of LLMs in the scholarly literature (arXiv:2403.16...

work page arXiv 2018
[6]

https://doi.org/10.53761/q3azde36 Perkins, M., & Roe, J. (2024). Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies (arXiv:2408.06872). arXiv. https://doi.org/10.48550/arXiv.2408.06872 Perkins, M., Roe, J., & Furze, L. (2024). The AI Assessment Scale Revisited: A Framework for Ed...

arXiv doi:10.48550/arxiv.2408.06872 2024
[7]

V., & Querol-Julián, M

https://doi.org/10.3389/fcomm.2022.830613 Lim, F. V., & Querol-Julián, M. (2024). Learning with technologies in the digital age: Now and the future. In Designing Learning with Digital Technologies. Routledge. Liu, D., & Bridgeman, A. (2023, July 12). What to do about assessments if we can’t out- design or out-run AI? What to Do about Assessments If We Can...

work page arXiv 2024
[8]

https://doi.org/10.1007/s40979-023-00146-z Zhgenti, S., & Holmes, W. (2023). Generative AI and Education: Adopting a Critical Approach. https://discovery.ucl.ac.uk/id/eprint/10177781/1/Zhgenti%20and%20Holmes%20- %202023%20- %20Generative%20AI%20and%20Education%20Adopting%20a%20Critical%20A.p df

work page arXiv 2023
[10]

https://doi.org/10.53761/2y2np178 Roe, J., Perkins, M., & Tregubova, Y. (2024). The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes (arXiv:2408.01075). arXiv. https://doi.org/10.48550/arXiv.2408.01075 Saban, A. (2006). Functions of Metaphor in Teaching and Teacher Education: A review essay. Teaching Education, 17(4), 299–315. ...

work page Pith review arXiv doi:10.48550/arxiv.2408.01075 2024
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https://doi.org/10.1186/s41239-024-00487-w Roe, J. (2024). AI and the Anthropological Imagination: Rethinking Education in the Digital Age. Open Anthropology Research Repository. https://openanthroresearch.org/index.php/oarr/preprint/view/399 Roe, J., Furze, L., & Perkins, M. (2024). Funhouse Mirror or Echo Chamber? A Methodological Approach to Teaching C...

arXiv doi:10.48550/arxiv.2411.14730 2024
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https://doi.org/10.1038/s41586-024-07566-y Singh Chawla, D. (2024). Is ChatGPT corrupting peer review? Telltale words hint at AI use. Nature, 628(8008), 483–484. https://doi.org/10.1038/d41586-024-01051-2 Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/...

work page arXiv 2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

Learning Outcomes

Alser, M., & Waisberg, E. (2023). Concerns with the Usage of ChatGPT in Academia and Medicine: A Viewpoint. American Journal of Medicine Open, 9, 100036. https://doi.org/10.1016/j.ajmo.2023.100036 Barthes, R. (1957). Mythologies. Éditions du Seuil. Batten, A. J. (2012). Metaphors We Teach By: The Language of “Learning Outcomes.” Teaching Theology & Religi...

work page arXiv 2023

[2] [4]

contamination

https://doi.org/10.14742/ajet.9434 Garcia, A., Luke, A., & Seglem, R. (2018). Looking at the Next 20 Years of Multiliteracies: A Discussion with Allan Luke. Theory Into Practice, 57(1), 72–78. https://doi.org/10.1080/00405841.2017.1390330 Gray, A. (2024). ChatGPT “contamination”: Estimating the prevalence of LLMs in the scholarly literature (arXiv:2403.16...

work page arXiv 2018

[3] [6]

https://doi.org/10.53761/q3azde36 Perkins, M., & Roe, J. (2024). Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies (arXiv:2408.06872). arXiv. https://doi.org/10.48550/arXiv.2408.06872 Perkins, M., Roe, J., & Furze, L. (2024). The AI Assessment Scale Revisited: A Framework for Ed...

arXiv doi:10.48550/arxiv.2408.06872 2024

[4] [7]

V., & Querol-Julián, M

https://doi.org/10.3389/fcomm.2022.830613 Lim, F. V., & Querol-Julián, M. (2024). Learning with technologies in the digital age: Now and the future. In Designing Learning with Digital Technologies. Routledge. Liu, D., & Bridgeman, A. (2023, July 12). What to do about assessments if we can’t out- design or out-run AI? What to Do about Assessments If We Can...

work page arXiv 2024

[5] [8]

https://doi.org/10.1007/s40979-023-00146-z Zhgenti, S., & Holmes, W. (2023). Generative AI and Education: Adopting a Critical Approach. https://discovery.ucl.ac.uk/id/eprint/10177781/1/Zhgenti%20and%20Holmes%20- %202023%20- %20Generative%20AI%20and%20Education%20Adopting%20a%20Critical%20A.p df

work page arXiv 2023

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https://doi.org/10.53761/2y2np178 Roe, J., Perkins, M., & Tregubova, Y. (2024). The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes (arXiv:2408.01075). arXiv. https://doi.org/10.48550/arXiv.2408.01075 Saban, A. (2006). Functions of Metaphor in Teaching and Teacher Education: A review essay. Teaching Education, 17(4), 299–315. ...

work page Pith review arXiv doi:10.48550/arxiv.2408.01075 2024

[7] [53]

https://doi.org/10.1186/s41239-024-00487-w Roe, J. (2024). AI and the Anthropological Imagination: Rethinking Education in the Digital Age. Open Anthropology Research Repository. https://openanthroresearch.org/index.php/oarr/preprint/view/399 Roe, J., Furze, L., & Perkins, M. (2024). Funhouse Mirror or Echo Chamber? A Methodological Approach to Teaching C...

arXiv doi:10.48550/arxiv.2411.14730 2024

[8] [759]

https://doi.org/10.1038/s41586-024-07566-y Singh Chawla, D. (2024). Is ChatGPT corrupting peer review? Telltale words hint at AI use. Nature, 628(8008), 483–484. https://doi.org/10.1038/d41586-024-01051-2 Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/...

work page arXiv 2024