=Paper=
{{Paper
|id=Vol-3408/short-s2-07
|storemode=property
|title=Proomting is Computational Thinking
|pdfUrl=https://ceur-ws.org/Vol-3408/short-s2-07.pdf
|volume=Vol-3408
|authors=Alexander Repenning,Susan Grabowski
|dblpUrl=https://dblp.org/rec/conf/iseud/RepenningG23
}}
==Proomting is Computational Thinking==
https://ceur-ws.org/Vol-3408/short-s2-07.pdf
Proompting is Computational Thinking
Alexander Repenning1, Susan Grabowski2
1
PH FHNW, School of Education, Bahnhofstrasse 6, 5210 Windisch, Switzerland
2
EPFL, AVP-E LEARN, Station 9, 1015 Lausanne, Switzerland
Abstract
Proompting, a term informally introduced into the English language to describe the iterative process of
writing or modifying a prompt to an AI system such as ChatGPT or Midjourney, could be considered a
new kind of Computational Thinking. Computational Thinking describes a process of convergence
combining human ability with computer affordances. In essence, Computational Thinking is an iterative
process of humans engaging in problem-solving by thinking with computers. In the current literature
Computational Thinking is often associated with programming. However, as we are trying to demonstrate
here, the perspective that “Proompting is Computational Thinking” can help to elevate the notion of
Computational Thinking to become a useful framework to promote the convergence of Human Intelligence
and Artificial Intelligence.
Keywords
Computational Thinking, programming, convergence of human and artificial intelligence, ChatGPT
1. Introduction
The invention of machines offering functions assumed to be expressions of human intelligence has a long
history. For instance, the ability to perform arithmetic operations was assumed to be an expression of human
intelligence. The invention of the world's first automatic calculating machine [5] by Blaise Pascal, known as
the Pascaline, had a significant impact on society during the 17th century. While the exact reception varied
among different segments of society, the overall response was positive and marked an important milestone in
the development of mechanical calculators. The term Artificial Intelligence, of course, did not exist at that time
but could be described retrospectively to capture the nature of that innovation at the time. The Pascaline did not
immediately revolutionize society or replace human calculators, as its cost and limited functionality restricted
its widespread adoption. Technology evolved and gradually the performance of machines exceeded that of
humans. Machines started to beat humans in Chess [3], ten years later Go [11] and many other human abilities.
As the performance of computers is increasing exponentially and neural nets are being trained on previously
unimaginable large data sets, Artificial Intelligence is evolving at an alarming speed. Ray Kurzweil called the
point at which Artificial Intelligence would exceed Human Intelligence the Singularity [6].
The Singularity may not be here just yet but developments such ChatGPT make it quite clear that the impact of
technology on society is much more profound and that technology is moving at previously unimaginable
speeds. Unlike with Blaise Pascal’s calculating machine this technology is affordable and not just relevant to
the intellectual elite. In other words, it is extremely likely for AI to transform the very fabric of society. Some
time ago, in his book “Program or be Programmed” Rushkopf [9], defined a critical ultimatum underlining the
urgency of Digital fluency [2]. Essentially, if one is not willing to take control of technology, i.e., “to program”,
then the machines will take over and “program” humans. While his position may be extreme it illustrates
possible consequences of ignoring technological development. How can society coexist with seemingly
superior technology where machine intelligence exceeds human intelligence?
Going back to the examples of games we can observe two fundamentally opposite strategies to deal with the
looming Singularity: capitulation and convergence. Lee Sedol, after losing against Google’s AlphaGo decided
to capitulate. He saw no more value in further pursuing this career as a Go player. After all, in the context of
IS-EUD 2023: 9th International Symposium on End-User Development, 6-8 June 2023, Cagliari, Italy
EMAIL: alexander.repenning@fhnw.ch (A. 1); susanne.grabowski@epfl.ch (A. 2)
ORCID: 0000-0002-2165-7533 (A. 1)
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Go machine intelligence had already eclipsed human intelligence. As machine learning further progressed this
gap could only widen. Gary Kasparow, in contrast, after losing against IBM’s Deep Blue was even further
motivated to play Chess. Continuing to play against Deep Blue he claimed to have significantly advanced his
Chess playing skills. This convergence model seeks to synergistically combine human intelligence with
Artificial Intelligence into a total intelligence exceeding human abilities as well as computer affordances.
This paper introduces the idea that proompting is Computational Thinking, explains how Computational
Thinking helps with the convergence of human abilities and computer affordances, provides some examples of
Proompting and finally suggests some early principles of proompting.
2. Proompting versus Computational Thinking
Computational Thinking (CT) is an iterative process synergizing human abilities with computer affordances.
The field of Computer Science education research [10] is still debating what exactly CT really is [7] and how
it is different from Computer Science and programming. However, there are some common characteristics
describing the CT process. Figure 1 shows the original three “A” CT process [8]consisting of the abstraction,
automation and analysis stages. This figure is being shared widely, including in Wikipedia.
1. Abstraction: Problem Formulation: Problem formulation attempts to conceptualize a problem
verbally, e.g., by trying to formulate a question such as “How does a mudslide work?,” or through
visual thinking, e.g., by drawing a diagram identifying objects and relationships.
2. Automation: Solution Expression: The solution needs to be expressed in a non-ambiguous way so that
the computer can carry it out. Computer programming enables this expression. The rule in Figure 1
expresses a simple model of gravity: if there is nothing below a mud particle it will drop down.
3. Analysis: Execution & Evaluation. The solution gets executed by the computer in ways that show the
direct consequences of one’s own thinking. Visualizations, for instance the representation of pressure
values in the mudslide as colors, support the evaluation of solutions.
Figure 1: The Computational Thinking Process
The Yin and Yang in the center of the Computational Thinking process (Figure 1) describe the two
complementary forces which make up the synergy between human abilities and computer affordances. There
are CT models such CT unplugged [1] exploring Computational Thinking without the use of computers.
However, for our purposes to explore the role of AI we are exclusively interested in use cases where we do
have computers.
The Computational Thinking process can now be reconceptualized as proompting (according to the Urban
Dictionary this term has been introduced informally into the English language). By proompting we refer to the
process of writing or modifying a prompt to an AI system such as ChatGPT or Midjourney. Lets now revisit
the 3 stages of CT from the Proompting angle with an explicit eye on the role of humans and computers:
1. Abstraction: Problem Formulation: again, the human drives the CT process by starting with a question.
We use the same question from before “How does a mudslide work?”
2. Automation: Solution Expression: Unlike with our previous example the user is not planning to
program a simulation of a mudslide but is planning to use one or more AI systems to create some kind
of output. Users may have different formats of output in mind. Do they want text, images, videos,
audio, code or perhaps even some combination? The user requirements combined with pragmatic
concerns such as previous experience will determine which AI system users will use and how they will
formulate a promising prompt. The initiative here is still human. Users need to select, and combine AI
systems. They need to express a prompt.
3. Analysis: Execution & Evaluation. An AI system may create one or more responses which users will
have to evaluate. The analysis stage may be quite simple such as picking an interesting looking picture
produced by an AI system such as DALL-E, or it may be quite intricate such as reading and perhaps
even running code produced. The connection between input (prompt) and output may be difficult to
understand and in some cases even unpredictable. For this stage to be productive humans will need to
have some understanding of the various affordances of the different AI systems.
A single iteration is unlikely to produce the desired result. Users may have to adjust their problem formulation
(abstraction), change the expression of their prompt (automation), or further process the output of the AI
(analysis). That is, they will engage in proompting. In the next sections we present some proompting examples.
3. 3. Proompting Experiments
Here are some basic examples of proompting.
3.1 Mudslide
Abstraction: The user is an elementary school teacher trying to teach the concept of mudslides to 4th graders
in a STEM class. She starts the CT process by formulating the original question from Figure 1: “How does a
mudslide work?”.
Automation: Moving on to automation she picks ChatGPT 3.5 and uses the main question 1:1 as prompt “How
does a mudslide work?”
Analysis: The output is elaborate text introducing the general notion of a mudslide
A mudslide, also known as a debris flow or a landslide, occurs when a mass of soil, rock, and water rapidly
moves down a slope. It is typically triggered by heavy rainfall, snowmelt, earthquakes, or volcanic activity.
Here's a general explanation of how a mudslide works:
A bullets list with 6 points including “Triggering event”, “Saturation and liquefaction”, “Failure of slope
material”, “Movement”, “Expansion and destruction”, and “Deposition” follows. After a brief analysis, the
teacher decides that the output is too complex for 4th graders.
Automation 2: She keeps the abstraction but expands the prompt: “please simplify for an 8 year old and be
brief”
Sure! A mudslide is like a big, heavy pile of wet dirt and rocks that suddenly moves down a hill very quickly.
It happens when there is a lot of rain or water, and the ground gets too weak and slippery. The dirt and rocks
can destroy things in their path like trees, houses, and roads. After it stops moving, it leaves behind a big mess.
It's important to be careful and stay away from areas where mudslides can happen.
Automation 3: This is a good start but to get a more hands on experience for her students the teacher would
like to see a simulation. She proompts: “write a particle simulation of a mudslide using JavaScript”
She got a short JavaScript program from ChatGPT including some explanation of what it does “The particles
are randomly generated with different velocities and sizes, and they fall from the top of the canvas to the bottom,
simulating the movement of a mudslide.” She knows how to copy the program into the browser. The code
actually runs but there is no evidence of a mudslide. The teacher does know a little bit of JavaScript but is not
able nor does she have the time to fix the code.
Somewhat frustrated, the teacher wishes to at least draw a picture of a mudslide. Using DALL-E via the BING
interface she enters the “mudslide” prompt only to be greeted by a “This prompt has been blocked. Our system
flagged this prompt because it may conflict with our content policy” BING message.
3.2 DynaBook
Alan Kay’s 50+ year old vision of educational computing devices for children was called DynaBook [4]
depicting Jimmy and Beth using hand held devices including networking and programming: “Two nine-year-
olds were lying on the grass of a park near their home, their DynaBooks hooked together.”
Figure 2. DynaBook: left drawing by Allan Kay 1972; right: AI DALL-E (via MS Bing) 2023
With more than 20 iterations within the Computational Thinking process (Figure 1), Proompting for the image
to the right was quite involved. The prompt used for Figure 2 right was “two 9 year old kids collaborating with
each other using iPads, sitting outside in the grass, cartoon style.” Without the “cartoon style” part DALL-E
(via MS Bing) flat out refused to create an image with the warning “Unsafe image content detected.” The same
was not true when using DALL-E directly. It is not clear why this image was considered unsafe.
The challenge suggested by this example is a high degree of unpredictability inherent to proompting. That is,
the precise mapping from step 2 in Figure 1 (automation) to step 3 (analysis) can be difficult to comprehend
because of the unforeseeable nature of many of the AI tools. Especially for the unexperienced users the result
can be quite a surprise.
3.3 Modern Art
The third experiment is trying to use DALL-E to replicate the work of OP-Art artist Bridget Riley, who created
the work "Shiver" in 1964 (Figure 3). In this experiment proompting consist of abstracting the geometrical
principles of the original picture (Figure 1, step 1), automating the image by formulating prompts (step 2),
analyzing the image generated (step 3) and repeating the CT process (steps 1 – 3) as many times as necessary.
The work consists of a simple matrix of 31 x 31 grid squares. In each white field is a black triangle. From row
to row, the triangles are arranged and twisted slightly differently. The first and last rows are identical
horizontally and vertically.
Figure 3: Bridget Riley: Shiver 1964
Provided the relative scarcity of online materials by Bridget Riley we assumed DALL-E would only have
limited training data. To simplify the task, the matrix was reduced in size to 20 x 20 cells.
Prompt 1: “Generate a 20 x 20 grid. Each grid-contains a black triangle. Triangles sit-in a horizontal row are
the same position. Vertically, the triangles shift minimally.”
The AI generates a matrix containing triangles. However, it does not follow the more specific description.
Figure 4: Four results of using DALL-E with prompt 1
Prompt 2: “Generate a 20 x 20 grid. Each grid contains a triangle filled with black, pointing downwards. Within
a horizontal row, the triangles are in the same position. Vertically, the triangles in the rows are shifted minimally
to the right or left.”
Figure 5: Four results of using DALL-E with prompt 2
The program thus generates a better matrix by additionally specifying that the first triangles point downwards
and the rotation can be to the right or to the left. The first result is somewhat better, but far from satisfactory.
The other results move away from the target again and are partly very questionable. In the last image, for
example, numbers and letters appear. Maybe we have to tell the AI to stay as close as possible to our description.
4. Conclusions
Overall our experiences show that AI holds great promise but, in its current state, can also be the source of
great frustration. At a time where AI as an end-user technology is exploding it will become nearly impossible
for users to track the rampant evolution of AI tools. It will take AI tools to be able to use AI tools. Already
extensions to AI tools enable the proliferation of next-generation meta-AI tools bundling up several AI tools
into new services. The mere speed of this process is raising big concerns on multiple levels. Education will be
profoundly transformed in good and bad ways. In this kind of world with so many quickly moving parts the
idea that “Proompting is Computational Thinking” may provide some calm by offering some resilient
principles. It will be necessary to establish common principles to all these systems. Here are some early
principles that we have noticed:
1. Proompting is Computational Thinking is a universal framework offering some resilient principles
that are valid across different AI tools.
2. Proompting is a social process: The opaqueness of most AI tools makes it difficult to predict the output
resulting from a prompt. The community of users showcasing examples and sharing suggestions is
essential to work productively with AI tools. Some AI systems such as Midjourney include this kind
of social embedding.
3. The unpredictable nature of prompting may require reverse Computational Thinking. Some AI tools
offer early support to reverse the Computational Thinking process. For instance, users can provide
existing images to make Midjourney describe that image as a prompt. In other words, these tools make
the proompting processes more transparent by providing a path back from Analysis (step 3) to
Automation (step 2).
4. Competence models need to shift from answering questions to posing them. Traditionally the
competency of individuals is judged by their abilities to answer questions. For instance, can you label
the various parts of the human body in pictures of anatomy? Emerging competency models will be
about the skills to pose questions and to modify them. In other words, new models will be about
proompting.
5. References
[1] Bell, T., F. Rosamond, and N. Casey, "Computer science unplugged and related projects in math and computer
science popularization," in The Multivariate Algorithmic Revolution and Beyond, L. B. Hans, D. Rod, V. F. Fedor,
and M. niel, Eds., ed: Springer-Verlag, 2012, pp. 398-456.
[2] DiSessa, A. A., Changing minds: Computers, learning, and literacy: Mit Press, 2000.
[3] Hsu, F.-h., "IBM's deep blue chess grandmaster chips," IEEE micro, vol. 19, pp. 70-81, 1999.
[4] Kay, A. C., "A personal computer for children of all ages," in Proceedings of the ACM National Conference, 1972.
[5] Kistermann, F. W., "Blaise Pascal's adding machine: new findings and conclusions," IEEE Annals of the History of
Computing, vol. 20, pp. 69-76, 1998.
[6] Kurzweil, R., The singularity is near: Springer, 2014.
[7] Repenning, A. and A. Basawapatna, "Explicative programming," Communications of the ACM, vol. 64, pp. 30-33,
2021.
[8] Repenning, A., A. Basawapatna, and N. Escherle, "Computational Thinking Tools," presented at the IEEE
Symposium on Visual Languages and Human-Centric Computing, Cambridge, UK, 2016.
[9] Rushkoff, D., Program or be programmed: Ten commands for a digital age: Or Books, 2010.
[10] Saqr, M., K. Ng, S. S. Oyelere, and M. Tedre, "People, Ideas, Milestones: A Scientometric Study of Computational
Thinking," ACM Transactions on Computing Education (TOCE), vol. 21, pp. 1-17, 2021.
[11] Wang, F.-Y., J. J. Zhang, X. Zheng, X. Wang, Y. Yuan, X. Dai, J. Zhang, and L. Yang, "Where does AlphaGo go:
From church-turing thesis to AlphaGo thesis and beyond," IEEE/CAA Journal of Automatica Sinica, vol. 3, pp. 113-
120, 2016.