This slideshow offers some early ideas about how art-making generative AIs may be used in instructional design while maintaining professional ethics.

1. Art-Making
Generative AI
and
Instructional Design
Work
An Early Brainstorm

2. 2

3. 3

4. 4

5. 5

6. About Art-Making Generative AIs
6

7. Generative AIs
• Generative AIs are modeled out in different ways:
1. neural with a deep learning model to train / learn on samples from
a training dataset and from that learning to generate new ones
based on a statistical data distribution (with machine learning /
computational data pattern finding across a range of complex
dimensions)*
2. symbolic or algorithmic, with a programmer setting the parameters
and an autonomous system generating samples within those
parameters (Aggarwal & Parikh, 2020)
There are combined “neuro-symbolic” approaches and additional
approaches outside these categories, too.
7

8. Generative Adversarial Networks (GANs)
• A common type currently is a Generative Adversarial Network (GAN).
The program is trained on (textual/visual/gameplay/music/sound,
etc.) data which is curated and often large-scale (human-generated).
• The datasets are using of one modality, although cross-modal ones would
make sense, too.
• The program “learns” about details of the visuals from the training
dataset / imageset, often using neural networks, which are a tool in
the artificial intelligence (AI) toolkit.
8

9. Generative Adversarial Networks(cont.)
• The GAN is generally comprised of a generator and a discriminator.
The generator (G) creates (textual/visual/gameplay/music/sound,
etc.) contents, which are judged by the discriminator (D) against
certain standards (established by the training data) for fidelity.
• With each work, both the generator and the discriminator become
more efficacious (so there is a cooperative aspect to this adversarial-
ness…in the sense that competition makes entities stronger).
• This is modeled after a two-person zero-sum game…a minimax
context…in game theory.
9

10. Generative Adversarial Networks(cont.)
• A GAN may have additional rules written into it, for issues such as
compositing, lighting, and other aesthetic aspects.
• A GAN may have additional rules written into it to keep it from
generating offensive images, from communicating stereotypes (from
the training data), and so on.
• Creative Adversarial Networks (CANs) is sometimes used to refer to
such AIs that use these technologies to generate text, visuals, games,
music, sound…and other dedicated contents.
10

11. Machine Art-Making
• A subset of generative AIs are designed to make digital visuals, which
may be photorealistic, artful (with certain styles), and so on, made up
out of whole cloth.
• Some of these take inputs from outside (open systems), such as text
prompts (and / or with visual prompts) and various parameters to
generate the desired image.
• Many are available on the Web (based on different terms): Deep Dream
Generator, CrAIyon (formerly WALL-E), Midjourney, Stable Diffusion, and
others.
11

12. Some Controversies about Art-Making
Generative AI Tools
• Some were built using copyrighted contents (and without the
permission of those whose works were captured and used as training
data).
• Some create likenesses of recognizable people (faces, physiques,
voices, and such, separately…and in combination) without their
permission.
• Some have no guardrails and may show adult content.
• Some amplify stereotypes.
• Some amplify biases.
• Some amplify hate speech.
12

13. 13

14. 14

15. Some Controversies in the
Academic Space
15

16. Some Controversies in the Academic Space
• How can art-making generative AIs be cited when they are used for
the creation of a refined visual used for teaching and learning? What
if the image is only used partially? What if the image is used only as a
reference or a concept or an inspiration? What if the image is used to
seed another AI image?
• How can an artificial image be described to learners to not
misrepresent visual contents? What if such an image is no longer
behind an LMS or CMS log-in and “escapes” into the wild? (“LMS”
refers to a “learning management system,” and “CMS” refers to a
content management system.)
16

17. Some Controversies in the Academic Space
(cont.)
• How can AI-generated works not be confused with the authentic
thing in publishing? In student work?
• Current forensics do not yet enable accurate identification of such works as
machine-generated at-scale (with speed and accuracy). (There are systems to
authenticate “real” works…and reverse image searches and other tools keep a
memory of online images…but “deep fakes” cannot be fully identified as-yet.
“Shallow fakes” either.)
• What are the implications of AI-generated art on professional human
artists? On potential human de-skilling?
17

18. Some Current Professional Ethics
• No plagiarism
• No fraud
• No misrepresentations
• No defamation of others (egos
or entities)
• No untruths
• No slander
• No defamation of others
• No threats
• No crying fire in a theatre
18

19. Some Current Professional Ethics(cont.)
• Control against negative learning
(or misapprehensions)
• Stringent accuracy
• Inclusiveness in the teaching and
learning
• Respect for others in the
teaching and learning (and just
being a person in the world)
• Accessibility of all learning
contents (digital and analog)
• Usability for all learners (such as
based on “universal design”)
• And others…
19

20. 20

21. About Instructional Design Work
21

22. Instructional Design Work
• Instructional design work involves a complex skillset and plenty of
collaboration on cross-functional teams.
• The skills include the following: research, writing, grant-writing, data
analysis, analytics, learner modeling, learning design, learning
development, learning deployment, adhering to legal and technical
standards, intercommunications, documentation, coding / scripting,
photography, alpha testing, beta testing, and others.
22

23. ID Preferences for Content
• 1) Self-created contents or those from known entities are preferred
because of the clear sourcing and provenance. There are the raw
files…and the legal releases…and other contents, with clear
documentation.
• 2) Free contents from the U.S. government, with legal releases, clear
provenance, professional archival, and long-term availability.
• 3) Free contents from open-source repositories…but with the
limitation of unclear provenance. (Non-stock-image-sharing
corporations can have very restrictive releases on content…which
require constant renewal.)
• 4) Stock images (free and commercial) for decorative applications.
23

24. 24

25. Some Professional Ethics in Using
Art-Making Generative AIs in
Instructional Design Work
Early Thinking
25

26. Citations When?
• The presenter created a continuum between “Ephemeral /
Immaterial” on one pole and “Realized / Material” on the other in
terms of the visuals.
• If a generated image is only used conceptually (1), stylistically (2), as rough
drafts (3), as references (4), perhaps the citation is not needed, since the
world is full of visuals and inspirations.
• If an AI-generated work is “partially finished” (5) or “refined finished” (6),
attribution is necessary.
26

27. Ephemerality to Materiality Continuum for Harnessing
Art-Making Generative AI for Instructional Design
Ephemeral /
Immaterial
Realized /
Material
Conceptual (1) Style (2) Roughs (3) Reference (4) Partially
Finished (5)
Refined /
Finished (6)
Inspiration
Ideas
Concepts
Brainstorming
Motivation
Exemplars
Style extraction
Style application
/ style transfer
Draft
Wireframe
Digital outline
Storyboard
Reference visual
Trace from
Used as a
seeding visual
(to create other
AI-based visuals)
Usable in part
Usable as a part
(requires
extraction;
requires
compositing)
Layout
Refined work
Publishable
Usable (without
much editing or
revision or
transcoding)
27

28. Training Instructional Designers
• Any training should help instructional designers understand
appropriate uses of art-making generative AIs.
• It should include knowledge of how these systems were created and
how they function.
28

29. 29

30. In a Time of Change
30

31. Changing Technological Affordances
• This work is a first draft.
• The technologies are changing in real time.
• Many other art-making generative AIs have come to the fore.
• It looks like multimodal generative AIs are forthcoming soon, if they
haven’t already emerged. (Those that create videos are multimodal
machine creatives.)
31

32. References
• Aggarwal, G., & Parikh, D. (2020). Neuro-symbolic generative art: a
preliminary study. arXiv preprint arXiv:2007.02171.
32

33. Communications and Contact
• Dr. Shalin Hai-Jew
• ITS
• Kansas State University
• 785-532-5262
• shalin@ksu.edu
• This work is based on a draft chapter titled “Professionally Ethical Ways to
Harness Art-Making Generative AI to Support Innovative Instructional Design
Work.” This work is being considered for publication at the moment of this
sharing.
• All the visuals in this work were created using an art-making generative AI but
were also edited to look like illustrations (down to the pixel level).
33