Professionally Ethical Ways to Harness an Art-Making Generative AI to Support Innovative Instructional Design Work

Professionally Ethical Ways to Harness an Art-Making Generative AI to Support Innovative Instructional Design Work

Shalin Hai-Jew
Copyright: © 2024 |Pages: 35
DOI: 10.4018/979-8-3693-0074-9.ch010
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Instructional designers often pride themselves on using the most cutting-edge commercial authoring and other tools available to achieve their work. Their creations have to meet high technical standards in order to function in a digital environment, in learning management systems, content management systems, on social media, on digital content platforms, and others. In the present moment, generative AI tools enable the making of novel texts and digital visuals, among others. A major extant question is how best to harness generative art-making AIs in instructional design work. In this case, this work explores professionally ethical (and legal) ways to use a generative art-making AIs for ID work, as an innovative approach based on a review of the literature, a year of using several free web-facing art-making generative AIs (CrAIyon, Deep Dream Generator, and others) in open or public beta, and learning from applied instructional design work (over several decades).
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2. Review Of The Literature

Artificial intelligence (AI) has been a long-term objective in computer science. There have been three phases of AI development from the 1950s: “Foundation (1950s – 1970s, methods and algorithms), Symbolic (1970s – 1990s, model driven, knowledge-based systems), and ML/DL (1990s – present, data-driven” (Maslic & Kim, 2021, p. 277), with “ML” referring to “machine learning” and “DL” referring to “data learning”. A large category of computer programs, in the modern age, emulates human intelligence and are known as “artificial intelligence” (as in non-human and non-animal intelligence, rather that of computational machines). “General” or “strong” AI aims to emulate and build on full human intelligence; “narrow” or “weak” AI aims to focus on limited and dedicated applications of machine intelligence for defined specific aims. Work into AI has been ongoing for decades, from the mid-1950s, at least. AI is a “paradigm” for thinking about a category of technologies (Striuk, Kondratenko, Sidenko, & Vorobyova, Nov. 2020, p. 368). For all the progress, this moment is “at the dawn of AI” even as AI is “acting, almost always invisibly, in nearly all fields of human activity” (Santaella, 2022, p. 43).

Key Terms in this Chapter

Deep Fake: An image, sound recording, video, or other digital object that depicts a false or misrepresentational context, often using AI and others.

General AI: A set of artificial intelligence technologies that emulate and build on human general intelligence.

Generative Adversarial Network (GAN): A type of AI program that is comprised of a generator and a discriminator, which work in adversarial and competitive ways.

Intellectual Property: Rights related to an original work that may be protected by patent, copyright, trademark, and other elements.

Post-Humanism: A theory of “after” or “beyond” humanism as a central concern to extend focus on additional issues beyond human concerns (decentering of human concerns as the foremost concern).

Generative Computational Creativity: A class of creative systems that can autonomously (without human input) generate text (in various genres), games, designs, analogies, artworks, music, and other contents.

Weak AI: An artificial intelligence program designed for a dedicated purpose to solve particular dedicated challenges (vs. AI that focuses on the emulation of general human intelligence).

Generative AI: An artificial intelligence tool or program that creates some content autonomously or with inputs from people or other external sources.

Anthropocentrism: Human-centeredness in terms of a worldview.

Generative Design: An AI capability of proposing various designs with particular parameters.

Instructional Design: The systematic design of teaching and learning to optimize the process (based on theory, empirical research, data, target learners, discipline content areas, and other inputs).

Visual Prompt: The uses of uploaded digital imagery (photos, drawings, sketches, icons, logos, and / or others) to elicit a response (such as from a generative AI).

Aesthetics: What is considered beautiful or artfully pleasing.

Copyright: A legal right owned by the originator of a work to use it in particular ways.

Strong AI: Artificial intelligence that aims to replicate and / or extend general intelligence.

Techno-Moral: The consideration of morality in a technological context or tool.

Techno-Social: The consideration of the social in a technological context or tool.

Creativity: The ability to conceptualize and execute on an original idea and / or creation.

Text Prompt: The uses of words, phrases, textual symbols, or other symbols in combination to elicit a response (such as from a generative AI).

Innovation: The creation of a novel process or object, such as to a marketplace.

Socio-Technical System: The understanding of particular technologies as comprising both social and technical aspects.

Non-Fungible Token (NFT): Assets (often digital) tokenized on a permanent blockchain.

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