Making Sense

Cope, Bill; Kalantzis, Mary

doi:10.1017/9781316459645

Cited by 30 publications

(14 citation statements)

References 151 publications

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“…Building on this work, we have mapped a theory of meaning that analyzes shared functions (reference, agency, structure, context, and interest) across all its forms (text, image, space, object, body, sound, and speech). We have called this a “transpositional grammar,” focusing on the constant and insistent movement across forms and functions (Cope and Kalantzis, 2020; Kalantzis and Cope, 2020). Like Kress, we note the profound differences between the forms of speech and written text, so radical in fact as to render the concept “language” problematic, as if text were a straightforward transliteration of speech.…”

Section: Grammar and Anti-grammarmentioning

confidence: 99%

“…Or that’s what it appears to be because it’s even less, because before the words appear on our screens they have been reduced to tokens. Then there is a reduction to Unicode, the universal scripting system of digital text (Cope and Kalantzis, 2020: 23-25). What then follows is a reduction to binary notation in order to give a long and humanly unreadable name to what is by now a humanly unintelligible abstraction.…”

Section: Grammar and Anti-grammarmentioning

confidence: 99%

“…In grammars of language, such subtleties are captured in transitivity (Halliday, 1968), case (Fillmore, 1968), or what Charles Fillmore calls “frame semantics” (Fillmore, 1976). Analysis of the range of such action types we have classified as “transactivity,” one of the dimensions of agency in our transpositional grammar (Cope and Kalantzis, 2020: 198-202).…”

Section: Grammar and Anti-grammarmentioning

confidence: 99%

“…We define written text as that which can be represented in Unicode, the universal character set for the connection of digital devices and the rendering of text in a wide range of media. Unicode is typographical compendium of graphemes in every human language and the most widely used symbologies (Cope and Kalantzis, 2020: 23-25). Math and code are written in Unicode in the same way as all other written text.…”

Section: Grammar and Anti-grammarmentioning

confidence: 99%

“…Some have argued that Generative AI is a “stochastic parrot” of sorts, as if it just repeats textual formulae that it has been taught (Bender et al, 2021). But it is much more than a parrot, because it meets Kress and our requirements of “design”—the formulation of new, meaningful, and well-formed text with a selection and ordering of words that mostly has never have been made before (Cope and Kalantzis, 2020: 68-72, 301-303, Kress, 2000). This is a remarkable technical achievement, and with it come new risks as well as new opportunities (Bommasani and others, 2022).…”

Section: Grammar and Anti-grammarmentioning

confidence: 99%

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A multimodal grammar of artificial intelligence: Measuring the gains and losses in generative AI

Cope,

Kalantzis

2023

Multimodality & Society

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This paper analyzes the scope of Artificial Intelligence (AI) from the perspective of a multimodal grammar. Its focal point is Generative AI, a technology that puts so-called Large Language Models to work. The first part of the paper analyzes Generative AI, based as it is on the statistical probability of one token (a word or part of a word) following another. If the relation of tokens is meaningful, this is circumstantial and no more, because its mechanisms of statistical analysis eschew any theory of meaning. This is the case not only for the written text that Generative AI leverages, but by extension image and multimodal forms of meaning that it can generate. The AI can only work with non-textual forms of meaning after applying language labels, and to that extent is captive not only to the limits of probabilistic statistics but the limits of written language as well. While acknowledging gains arising from the brute statistical power of Generative AI, in its second part the paper goes on to map what is lost in its statistical and text-bound approaches to multimodal meaning-making. Our measure of these gains and losses is guided by the concept of grammar, defined here as a theory of the elemental patterns of meaning in the world—not just written text and speech, but also image, space, object, body, and sound. Ironically, a good deal of what is lost by Generative AI is computable. The third and final part of the paper briefly discusses educational applications of Generative AI. Given both its power and intrinsic limitations, we have been experimenting with the application of Generative AI in educational settings and the ways it might be put to pedagogical use. How does a grammatical analysis help us to identify the scope of worthwhile application? Finally, if more of human experience is computable than can be captured in text-bound AI, how might it be possible at the level of code to create a synthesis in which grammatical and multimodal approaches complement Generative AI?

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Section: Grammar and Anti-grammarmentioning

confidence: 99%

Section: Grammar and Anti-grammarmentioning

confidence: 99%

Section: Grammar and Anti-grammarmentioning

confidence: 99%

Section: Grammar and Anti-grammarmentioning

confidence: 99%

Section: Grammar and Anti-grammarmentioning

confidence: 99%

See 3 more Smart Citations

A multimodal grammar of artificial intelligence: Measuring the gains and losses in generative AI

Cope,

Kalantzis

2023

Multimodality & Society

Self Cite

View full text Add to dashboard Cite

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Experiences of Multimodal Teaching Through a Serious Game: Meanings, Practices and Discourses

Lameras

Papageorgiou

2020

Technology Supported Innovations in School Education

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The aim of this chapter is to report on school teachers' perceptions of and approaches to multimodality using a serious game. STEAM is a game designed for helping schoolteachers to gain awareness of how multimodality may be enacted in the classroom for enhancing the student learning experience. The game embraces the notion of multimodal teaching and learning, as a way to present multiple representations of content such as text, images, video, audio and pervasive media, by augmenting modes with tools, teaching strategies and locations as means to create ideational, interpersonal and textual meanings. A semi-structured questionnaire was employed to school teachers (n=54) for understanding how multimodality was experienced through using the game as:(1) stipulating diversity and increasing knowledge retention, (2) developing senses for attaining deeper understanding of the subject topic, (3) involving students into learning design and (4) supporting student's autonomy and selfdirection. The findings revealed an explicit connection between theory and practice as experienced through the game's semiotic domain whilst contemplating on attempts to transcend experiences of in-game multimodality to lived classrooms.

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Maps of Medical Reason: Applying Knowledge Graphs and Artificial Intelligence in Medical Education and Practice

Cope

Kalantzis

Zhai

et al. 2022

Postdigital Science and Education

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Making Sense

Cited by 30 publications

References 151 publications

A multimodal grammar of artificial intelligence: Measuring the gains and losses in generative AI

A multimodal grammar of artificial intelligence: Measuring the gains and losses in generative AI

Experiences of Multimodal Teaching Through a Serious Game: Meanings, Practices and Discourses

Maps of Medical Reason: Applying Knowledge Graphs and Artificial Intelligence in Medical Education and Practice

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