# How Effective Are Depictions of AI? Reflections from an Experimental Study in Science Communication

Alberto Romele, Marta Severo

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Alberto Romele, Marta Severo. How Effective Are Depictions of AI? Reflections from an Experimental Study in Science Communication. AI & Society: Knowledge, Culture and Communication, In press, 10.1007/s00146-025-02283-0. hal-04980668

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<figure><img src="image_1.png" /></figure>
Alberto Romele, Institute of Communication and Media, IRMÉCCEN, Sorbonne Nouvelle
University (France)<fnref n="1" />

Marta Severo, Department of Communication, Dicen-Idf, Paris Nanterre University (France)

# How Effective Are Depictions of AI? Reflections from an Experimental Study in Science Communication*

*This is preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in AI & Society, and is available online at https://doi.org/10.1007/s00146-025-02283-0

## Introduction

An emerging body of literature critiques the images used to depict artificial intelligence (AI) in science communication and marketing (Cave & Dihal 2020; Romele 2022; Romele & Severo 2023, b; Vrabič Dežman 2024). Beyond academic literature, there are initiatives where researchers attempt to engage with civil society, such as the irreverent Not My Robot!<fnref n="2" /> account on X (formerly Twitter) and the Better Images of AI<fnref n="3" /> initiative. The proliferation of generative AI systems for producing images from text prompts does not seem to improve the situation. For instance, inputting a neutral prompt like “artificial intelligence, machine learning, algorithm” into ChatGPT results in stereotypical images: white robots, blue backgrounds, screens filled with binary code, and variations of Michelangelo’s *The Creation of Adam* in a human-robot version. These images are indistinguishable from thousands available on stock photo sites like Shutterstock, which are widely used in scientific and institutional contexts. As an example, consider the cover of the *Oxford Handbook of Ethics of AI*—it is particularly notable that a text on AI ethics does not question the ethical implications of such imagery.<fnref n="4" /> In this article, however, we do not aim to reiterate this critique. Instead, we pose a question that critics seem to take for granted: are we really sure that the images used to depict AI are effective? This initial question leads to a second: If this is the case, are we certain that kitsch, stock images are more effective, or at least effective in a different way, compared to other types of AI depiction?

The term “effectiveness” is inherently vague. The review of the literature in the first section of this paper reveals at least three distinct meanings when applied to AI-related images: their explanatory power and ability to convey high-quality knowledge; their potential to inspire action or foster discussion around a specific topic; and their capacity to elicit strong emotional responses, ranging from fear and rejection to enthusiasm and adherence to a particular project or worldview. Rather than privileging one of these interpretations, we draw on Aristotelian rhetoric to conceptualize effectiveness in terms of persuasive force. Following Aristotle, we distinguish three technical *pisteis*—or “means of persuasion”: *logos*, *ethos*, and *pathos*. *Logos* (reasoning) refers to the explanatory power of an image; *ethos* (credibility) pertains to an

<footnote n="1">¹ Corresponding author: alberto.romele@sorbonne-nouvelle.fr</footnote>

<footnote n="2">² https://notmyrobot.home.blog/. Accessed 10 February 2025.</footnote>

<footnote n="3">³ https://betterimagesofai.org/. Accessed 10 February 2025.</footnote>

<footnote n="4">⁴ https://academic.oup.com/edited-volume/34287. Accessed 10 February 2025.</footnote>
image's capacity to incite action or discussion; and *pathos* (engagement) corresponds to an image's ability to evoke specific emotional responses.

In particular, this article aims to explore the effects of images depicting AI on the perception and understanding of AI-related textual content. Specifically, it differentiates between two types of images: those produced by research public entities to disseminate research results to a non-expert audience, and commercial stock images. The former are typically available without copyright (or with an option for a free transfer) from institutional repositories, whereas the latter can be purchased from sites like Shutterstock or Getty Images, which also offer various subscription options. While both types of images are utilized in science communication, commercial stock images appear to dominate both institutional and non-institutional communication. For instance, the majority of images featured on the website of CORDIS—the European Commission's primary portal for results from projects funded by the EU's framework programmes for research and innovation—are sourced from Shutterstock.

The paper is structured into three sections. In the first section, we review the literature concerning the persuasive potential of images in science communication. We highlight the division within the literature between proponents who advocate for the persuasiveness of images and critics who question their efficacy. This analysis particularly focuses on AI imagery, where critical discussions have emerged regarding the risks associated with using specific images, notably stock images, to represent AI. In our view, much of this literature does not sufficiently address the effectiveness of these images in influencing the perception of a non-expert audience. In the second section, we detail our experiment inspired by the methodology of Gruber & Dickerson (2012), who explored the influence of brain imagery. We engaged 79 B.A. communication students based in Paris, France, divided into three groups, each receiving a different version of the same fabricated scientific article: one version without images, one with an image from an institutional repository, and one accompanied by a Shutterstock image. The third section discusses the experimental results. In particular, the following findings are noteworthy: (1) our questionnaire revealed a significant variance in how the three groups engage with the technical and scientific content of the fabricated article. Readers exposed to the stock image generally demonstrated a less thorough understanding of these elements; (2) our questionnaire also indicates that readers of the articles with images were more inclined to engage further compared to those who read the article without images. Additionally, stock images seem to slightly boost engagement. This brings us to conclude that while the existing critical literature correctly acknowledges the persuasive power of AI images, particularly stock images, it should also exercise more caution in condemning their ethical shortcomings.

# 1. Are Images (of AI) in science communication persuasive? A literature review

For a long time, science communication has been concerned with the effectiveness of images in conveying scientific information. In contrast, the emerging literature on images depicting AI tends to take this effectiveness for granted. Hence, the goal of this first section is to link the literature on the visual representations of AI with the established literature on the importance of visual images to science and science communication. In regard to the latter, two divergent opinions are evident. On one hand, some scholars argue in favor of the persuasive power of images within science and science communication. Landau et al. (2009: 326) cite studies on the role that scientific illustration has historically played in constructing scientific knowledge, such as those by Baigrie (1996). They also reference a body of research focused on the effects
of verbal and visual images, particularly in mass media, on people's perception and general understanding of science, exemplified by Doorman (1989), Keller (2003), and Giarelli (2006).

Additional, more recent examples of this extensive literature include: León & Erviti (2015), who address the visual representations of climate change (CC) in Spanish television news, suggesting a correlation between the scarcity of CC imagery and the topic's marginalization in Spanish TV news; Bucchi & Saracino (2016), who measure visual science literacy in Italy through surveys conducted between 2014 and 2016, finding that respondents more readily recognize scientific images than respond to text-based questions, thus affirming the powerful role of images in science communication; Wang et al. (2017), who analyze the cover stories of journals such as *Nature*, *Science*, and *Cell*, and establish a positive correlation between the presence of images in cover story papers and the citations received, as well as with the journals' impact factors; Feldman & Hart (2018), who suggest that CC images emphasizing climate action can foster hope; and Schultz et al. (2018), who examine images related to stormwater management, noting that images highly congruent with the topic tend to evoke negative emotions, and vice versa (Schultz et al. 2018: 219). Lastly, Heley et al. (2022) define visual misinformation—a phenomenon they consider still overlooked—as “visual content that, typically used alongside text or audio, leads to false and inaccurate presentations of information” (Heley et al. 2022: 516).

Conversely, a smaller segment of literature contests the persuasive power of images, or posits that it cannot be empirically determined with certainty. Gruber & Dickerson (2012), for instance, conclude their empirical analysis of visual representations of the brain—including fMRI scans, science fiction images, and artistic depictions—by stating that “the persuasiveness of different kinds of images remains relatively uniform and shows no statistically significant deviations” (Gruber & Dickerson 2012: 940). They ultimately assert that “images of any sort do not appear, at least in this study, to have an impact on readers’ evaluations of popular science news” (Gruber & Dickerson 2012: 944). Locke (2013) emphasizes the importance of the context in which images are employed. Specifically, the author examines two opposing perspectives on scientific images: those that are “black-boxed” by the meanings assigned by the scientific community, and those considered polysemic, with meanings contingent upon the context of presentation and use. For Locke, black-boxing is not an intrinsic attribute of images; rather, it is contingent on the context and is a process undertaken by both scientists and non-scientists: “Black-boxing should be seen not as a phenomenon solely accomplished by the scientific community in a definitive manner, but as an ongoing process by both scientists and non-scientists in the activity of constructing alternative meanings in different contexts” (Locke 2013: 306). More recently, Duan et al. (2021) explore the impact of both concrete and abstract images of CC, concluding that responses vary more due to the political orientation of the respondents than the images themselves: “findings indicate that individuals with differing political orientations react to climate change images in varied ways [...] concrete images significantly diminished the perceived psychological distance to climate change among liberals, but did not have the same effect on conservatives” (Duan et al. 2021: 380).

Before delving into the literature on images depicting AI, it is necessary to note that there is a growing interest in three types of images: those used to train AI systems, those produced by generative AI, and those that visually represent AI in science communication, marketing, and the media at large. In contrast, there seems to be less emphasis on the role of images in developing new knowledge within the field of AI. This could be attributed to the fact that scientific papers on AI, and AI research in general, are not as reliant on images as other scientific and technological research, such as in nanotechnology. While it is beyond the scope of this paper to empirically demonstrate this, we propose that the scarcity of “scientific” images
of AI may have enabled the proliferation of other types of images, such as the imaginative depictions of AI found in science-fiction movies and stock images.

In this context, our focus is on images depicting AI in science communication, marketing, and media in general. These sources include not only science-fiction movies but also repositories of stock images from agencies like Getty Images and Shutterstock. Indeed, stock imahgery plays a significant role in shaping the current visual imagery of AI, as noted by Romele (2022). Additionally, artists' impressions and open-access repositories from research institutions, such as the media library of the French Institute for Research in Computer Science and Automation (INRIA), are also considered. The interest in these images is increasing. For example, Singler (2020) analyzes 79 variations of the “AI creation meme,” namely adaptations of Michelangelo’s *The Creation of Adam* featuring a human-robot interaction. Cave & Dihal (2020) discuss how AI is predominantly portrayed as white, in terms of both color and ethnicity. Romele (2022) addresses the ethics of science communication, as discussed by Dahlstrom & Ho (2012) and Medvecky & Leach (2019), and the use of stock images of AI. They contend that such images have an “anaesthetic” effect—a term that combines “aesthetics” and “anesthetic”—on the public’s understanding and engagement with AI. In other words, the term suggests that the aesthetics of these images is more likely to pacify rather than stimulate public debate and engagement. This pacification implies that the public may be more inclined to accept the processes of AI innovation and implementation within our societies without critical examination. Romele (2022) employs the notion of “pensive images,” introduced by philosopher Jacques Rancière, as an antidote to the anesthetizing effects of stock images of AI. Meanwhile, Romele & Severo (2023) argue that there is an intrinsic visual representability problem with AI: in fact, no type of image—whether institutional images showing computer scientists at work or algorithms, images representing a technology in which AI is embedded, such as a drone or a robot, or imaginative images—can truly represent AI as it is. Faced with this impossibility of reference, there are two options: either propose an iconoclasm of AI, which the authors reject, or disregard the reference and judge—ethically, aesthetically, and ontologically—the depictions of AI based on their capacity to generate debate. In particular, the authors invoke Chantal Mouffe’s concept of agonism. Romele & Severo (2023) focus also on the process of algorithmization in the production of stock images depicting AI. This process culminates in generative AI creating images of itself, thereby establishing its own symbolic conditions of possibility. Similarly, Vrabič Dežman (2024: np) asserts that generative images depicting AI “have further reinforced the ossification of the familiar blue visuals” typical of stock imagery.<fnref n="5" />

Alongside the academic literature, initiatives aiming to promote public engagement in science and technology, such as Better Images of AI and Not My Robot!, have emerged. These initiatives critique common visual representations of AI, such as intelligent robots, binary code floating in space, and body parts fragmented into particles, pixels, or voxels. Many authors advocate for more ‘honest’ representations of AI, ones that more accurately reflect the reality of AI systems and their creators. They also suggest that visual representations of AI can influence public expectations and the societal acceptability of AI.

This literature on AI’s visual representations is part of a broader body of work that deals with AI communication. Fast & Horvitz (2017), for instance, analyze perspectives on AI presented in the *New York Times* over a 30-year period. They examine the prominence of AI discussions, the optimistic or pessimistic tones in news coverage, the types of ideas associated with AI, and how public hopes and concerns about AI have evolved. Cave & Dihal (2019) investigate the

<footnote n="5">⁵ On the blue visual of stock imagery of AI, see https://blog.betterimagesofai.org/nel-blu-dipinto-di-blu-or-the-anaesthetics-of-stock-images-of-ai/. Accessed 10 February 2025.</footnote>
fears and hopes surrounding “intelligent machines” through an analysis of 300 fictional and non-fictional texts, identifying four polarizations: immortality versus inhumanity, ease versus obsolescence, gratification versus alienation, and dominance versus uprising. Obozintsev (2018) and Chuan et al. (2019) utilize framing theory. Natale & Ballatore (2020) trace the rise of the “thinking machine” myth through content analysis of articles published in *Scientific American* and *New Scientist* between the 1950s and the 1970s. Hermann (2023) warns of the risks associated with using science fiction in AI science communication, although he notes that the issue is not with science fiction per se, which often uses science and technology as a backdrop for exploring human conditions. The concern arises when science fiction is used for different purposes, such as explaining AI from a scientific and technical perspective. Cave & Dihal (2023) examine how AI is portrayed across different cultural contexts, from Europe to East Asia.

Beyond academic papers and books, public reports also contribute to the discourse. Cave et al. (2018) suggest the need to reshape AI narratives, arguing that certain images, particularly Terminator-style humanoid representations, dominate public discussions. They advocate for alternative models for discussing AI that could be more influential in shaping public discourse. Feng et al. (2023) offer lessons from AI communication, highlighting the importance of showcasing the people behind the technology and investing in image libraries: “The prevalence of misleading images of AI contributes to bias in public debate... Using more accurate images for technology communication is crucial in today’s visually oriented media landscape” (Feng et al. 2023: 7).

This review of the literature suggests a strong consensus on the effectiveness of visual communication in shaping public perception and expectations of AI. The majority of this research employs content analysis of texts and images or is grounded in the personal beliefs of researchers and stakeholders. Nevertheless, there is a noticeable gap regarding the audience's perception. Bingaman et al. (2021) stand out in this respect, referencing three national surveys that reveal the U.S. public's ambivalent feelings toward AI, influenced by factors such as demographics, political orientation, and religiosity: Northeastern University & Gallup (2018), West (2018), and Zhang & Dafoe (2019). Bingaman et al. diverge by centering on media and messaging, specifically the impact of images, rather than the broader social and cultural context. Drawing on framing theory and public opinion research, they conduct a survey with four textual frames—neutral, social progress, Pandora's box, or a combination—and four image treatments including no image, virtual assistants, personal robots, or menacing film AIs. Their findings suggest that while textual framing alone can influence attitudes toward AI, the combination of text and imagery is more effective than images alone (Bingaman et al. 2021: 390). They also note that, contrary to previous research demonstrating the influence of visual framing on scientific issues, image treatments alone did not significantly affect opinions about AI, indicating that the effect of visual representation may be conditional (Bingaman et al. 2021: 396). In sum, this study, unlike much of the existing literature on AI imagery, did not identify empirical evidence of AI images alone significantly affecting public beliefs and opinions about AI.

The primary aim of this paper is to further investigate this issue. Our interest lies not only in the overall effectiveness of AI images but also in the impact of various types of AI images. We concentrate on two categories: images from open-access research institution repositories that depict the “people behind the technology”—echoing the communications strategies suggested by Feng et al. (2023) and reflecting the general belief that such images are the most authentic, as they show AI “in the making”—and images from stock images agencies that represent AI in highly imaginative ways, which are often criticized for their unrealistic portrayals of AI.
Before proceeding, it is essential to unpack the concept of effectiveness as applied to images of AI, as it can be somewhat vague. The review of the literature here above reveals different meanings attributed to the term. It can refer to their explanatory power and ability to convey high-quality knowledge (Wang et. al. 2017); their potential to inspire action or foster discussion around a specific topic (Feldman & Hart 2018); and their capacity to elicit strong emotional responses, ranging from fear and rejection to enthusiasm and adherence to a particular project or worldview (Schultz et. al. 2018). Rather than privileging one of these interpretations, this paper draws on Aristotelian rhetoric to conceptualize effectiveness in terms of persuasive force.

In this regard, we follow Gruber & Dickerson (2012), who define effectiveness specifically in terms of rhetorical persuasiveness. However, their analysis is limited to two of the three means of persuasion identified by Aristotle, namely logos and ethos. By contrast, we aim to examine all three. Following Aristotle, we distinguish three technical pisteis—or means of persuasion: logos, ethos, and pathos. Logos (reasoning) refers to the explanatory power of an image; ethos (credibility) pertains to an image's capacity to incite action or discussion; and pathos (engagement) corresponds to an image's ability to evoke specific emotional responses. In Aristotelian rhetoric, logos pertains to the argument itself, ethos to the speaker, and pathos to the audience. In our perspective, however, we are more interested in the logical, ethical, and emotional effects that an image can have on its viewer—particularly when the act of viewing is accompanied by the reading of a corresponding text.

According to Aristotle, persuasion operates strictly within these three dimensions: “for all people who are casting judgments are persuaded either because they themselves are affected, or because they assume that the speakers are a certain kind of person, or because something has been proven” (Rhetoric III.1, 1403b12–13). In our approach, however, we do not rule out the existence of additional dimensions. That said, we argue that these three remain central, as they correspond to three fundamental characteristics of a technical object: its empirical and material nature, the social consequences of its implementation, and the broader atmosphere of meaning and sensibility that shapes collective attitudes toward it.

## 2. Empirical case study

### 2.1. Objectives and research questions

This paper sets out to achieve two principal objectives: (1) to evaluate the persuasive power of AI images in science communication and (2) to compare the influence of two types of AI images—images sourced from open-access repositories of research institutions, and images from stock agencies. Aligned with these objectives, our study is guided by the following research questions:

1. Do AI images exert any influence on readers of a science communication article about AI, and if so, can this influence be characterized as persuasive?

2. Does the nature of the AI images (open-access “realistic” vs. stock “imaginative”) affect their impact on the readers of a science communication article about AI?

3. How do AI images impact readers’ engagement, and does this engagement vary depending on the type of images used?
## 2.2. Methods

The study closely followed the methodology of Gruber & Dickerson (2012), which itself was based on McCabe & Castel (2008), focusing on the influence of brain images. We introduced two slight modifications. First, we extended the scope of inquiry to include engagement alongside reasoning and credibility, aligning with the rhetorical tradition's *logos* and *ethos*, and incorporating *pathos*. Our aim was to clarify the concept of images' effectiveness, which can otherwise be vague. We posited that an AI depictions might be logically sound and credible yet not engage the audience, such as by provoking a desire to learn more about the topic. Second, we adapted one open-ended question to better assess engagement. The survey instrument featured eight Likert-scale items with four response options each and two open-ended questions.

The experiment involved three versions of a fabricated science communication article about AI, each with identical text but different visual presentations. One group of students received the article without any images (group 1), another group received the article with an image from an open-access scientific database (group 2), and the final group received the article with an image from a stock agency database (group 3). Participants were B.A. communication students from a university in Paris. Participation was voluntary, with no compensation, and the study adhered to GDPR standards by not collecting personal data, maintaining participant anonymity, and informing them of their rights and the study's purpose. The study had 79 participants: 27 in group 1, 26 in group 2, and 26 in group 3. Participants who failed to respond to at least one open-ended question were excluded, as incomplete responses indicated low engagement.

Each group was presented with a fabricated news story titled "An article written by ChatGPT has been accepted for publication by the journal *Nature Machine Intelligence*," claiming publication in *Nature*'s news and views section on January 30, 2024, and translated from English to French by one of the authors. This fictional scenario, including a fabricated tweet by the editor-in-chief, Liesbeth Venema, and made-up statistics about ChatGPT's usage by U.S. university students, was designed to eliminate prior exposure. The article also featured statements from a non-existent Prof. Strauss from UC Berkeley, who allegedly submitted the paper as a scientific test. It reported on an experiment where 25 instructors assessed essays, purportedly including one written by ChatGPT, which received a low likelihood-to-be-AI score. The narrative ended with a faux statement from Venema, inviting a skeptical view of the content.

The research design incorporated three distinct groups to assess the influence of visual elements on the evaluation of an article about artificial intelligence. Group 1 was presented with the article devoid of any imagery. Group 2 received the same article accompanied by a photograph from the French National Center for Scientific Research's (CNRS) online media library. This image showcased Edouard Pauwels, a computer science and mathematics professor at the Institute for Research in Computer Science in Toulouse, as he elucidated scientific findings to a student, set against the backdrop of a whiteboard inscribed with mathematical formulas.<fnref n="6" /> Group 3 appraised the article paired with a Shutterstock vector graphic image. This depiction featured a female profile, constructed from electronic circuitry, binary code of zeros and ones, and luminous particles, with a progressive blue-toned backdrop suggesting the digital essence. The image's description on Shutterstock articulated it as symbolizing "Big Data and the notion of artificial intelligence, machine learning, and the

<footnote n="6">⁶ https://images.cnrs.fr/reportage-photo/rep001073. Accessed 10 February 2025.</footnote>
concept of a cybernetic mind domination, represented through a woman's face outlined by a circuit board with binary code flowing across a blue background.”<fnref n="7" />

## 2.3. Hypotheses

After reading the article, participants were asked to complete a survey consisting of eight four-point Likert-scale questions and two open-ended questions, expanding on the five-question survey used by Gruber & Dickerson (2012). Each question aimed to provide insights related to the research questions and test the following hypotheses:

For research question 1 (Do AI images have a persuasive effect on readers of a science communication article about AI? If so, can this effect be considered persuasive?), our hypothesis was:

H1: AI images have a persuasive effect. Readers tend to perceive articles with AI images as more logically structured, clear, and effective than articles without images.

The first six questions of the survey examined the persuasive power of the article, with three questions exploring reasoning: (1) The article is well written; (2) The title is a good summary of results; (3) The article's scientific reasoning is logical, and two questions assessing credibility: (4) The article contains credible information; (5) Article is authoritative; (6) I understand the technical and scientific aspects mentioned in the article).

To test H1, we compared responses to these six questions between the groups with images (groups 2 and 3) and the group without images (group 1). These responses showed good coherence, with a Cronbach's alpha coefficient of 0.631, slightly below the acceptable threshold of 0.7. It is important to note that we did not choose these questions but replicated questions from previous studies. Additionally, we analyzed the open-ended response to question 9 to validate the Likert scale results.

Regarding research question 2 (Does the type of AI images (open access “realistic” vs. stock “unrealistic AI”) influence the impact on readers of a science communication article about AI?), our hypothesis was as follows:

H2: Unrealistic AI images sourced from stock databases tend to have a more persuasive impact than realistic AI images from scientific databases.

To test this hypothesis, we applied a similar method as used for H1, but this time we compared the two groups that read the article with an image: group 2 (open access image) and group 3 (stock image). Similarly, we compared the results with responses to the open question 9.

For research question 3 (Do AI images influence readers' engagement? Does readers' engagement vary based on the type of images?), we formulated two hypotheses:

<footnote n="7">⁷ https://www.shutterstock.com/fr/image-vector/big-data-artificial-intelligence-concept-machine-1214787442. Accessed 10 February 2025.</footnote>
H3: Articles with AI images generate more reader engagement than articles without images.

H4: Articles with unrealistic AI images from stock databases tend to generate weaker engagement compared to those with realistic AI images from scientific databases. In other words, “unrealistic” images tend to have an “anaesthetic” effect.

To test these hypotheses, we considered responses to two Likert-scale questions about engagement: (7) The discoveries mentioned in the article are relevant to me; (8) The article makes me want to know more about the subject. It is worth noting that the Cronbach's alpha coefficient for these questions was relatively weak at 0.5. Therefore, our discussion primarily focuses on the textual analysis of the corresponding open question (question 10: “I find that the article makes me want to know more about the subject because:”). To assess H3, we compared the responses of groups with images (2 and 3) with those of the group without an image (group 1). For H4, we compared group 2 (open access image) with group 3 (stock image).

<figure><img src="image_2.png" /><figcaption>Figure 1. Responses to the first 6 questions of the questionnaire.</figcaption></figure>

## 3. Discussion

To enhance clarity, our discussion will be divided into two parts. First, we will delve into research questions 1 and 2, which pertain to the persuasive power of images. Second, we will explore readers' engagement.
3.1. The persuasive power of images

This paper's primary aim was to assess the influence of AI's depictions in science communication. Specifically, our hypotheses (H1 and H2) suggest that articles with images are more persuasive than those without images (H1), and, in particular, unrealistic images sourced from stock databases have a greater impact than realistic images from scientific databases (H2). The discussion of H1 and H2 is intertwined as both are based on the analysis of the same questions.

Overall, we cannot identify clear evidence that distinctly separates the three groups. Survey results indicate slight differences in perceptions among the three groups, but they do not consistently point in the same direction. Nevertheless, when analyzing each question separately, we can highlight some intriguing insights related to our research questions Q1 and Q2 and their corresponding hypotheses.

All three articles are generally perceived as logical. Articles with images are deemed slightly more logical than those without images, although it is noteworthy that 15% of readers of the article without images do not find the text entirely logical. Moreover, the article with a realistic image is perceived as slightly more logical than the one with a stock image.

In terms of being well-written, respondents generally view the articles positively, although a significant portion partially disagrees or disagrees with this assertion. Articles with images are considered better written than those without images, implying that the absence of images may draw readers' attention more to the text itself. The article with the open-access institutional image is perceived as slightly better written than the one with a stock image.

The titles of the articles are generally seen as effective in summarizing the results, although approximately 10% of respondents across all three articles disagree with this statement. However, in the case of the article with a stock image, the title is perceived as less effective.

Responses to the question about the credibility of the information in the articles are relatively similar among the three groups. Respondents generally find the information credible. Therefore, it appears that images have a weak influence on the perceived credibility of the articles, with only a few respondents finding the articles with images less credible than the one without images.

All three articles are considered moderately authoritative, although the scores for this question are lower than those for the previous one, especially for the article with a stock image.

The question about the technical and scientific aspects in the article stands out with notably different answers, corroborated by the Nova test, which is significant only for this question (0.06). In particular, when comparing the article without images and the one with a stock image, the difference is even more pronounced (0.03). Respondents found that they could better comprehend the technical and scientific aspects mentioned in the article without images compared to the one with stock images—although nobody answered: “strongly disagree.”
<figure><img src="image_3.png" /><figcaption>Figure 2. Nova test for question 6 of the questionnaire.</figcaption></figure>

To support our findings concerning this last question, we analyzed the responses to the open question “I find that the article gives me a more or less clear understanding of the technical and scientific aspects of the subject because.” By examining word frequency, we identified common vocabulary among the three groups, including words like “article,” “ChatGPT,” and “good.” Nevertheless, significant differences emerged for the article with stock images. The term “clear” appeared less frequently compared to the other two groups, and certain terms such as “concrete,” “precise,” “data,” and “world” were entirely absent. Conversely, words like “clear” and “scientific” were frequently used for the article with the realistic image.

<table><thead><tr><th>Without images</th><th>frequency</th><th>Stock image</th><th>frequency</th><th>OA image</th><th>frequency</th></tr></thead><tbody><tr><td>article</td><td>10</td><td>article</td><td>6</td><td>clair</td><td>8</td></tr><tr><td>bien</td><td>10</td><td>chatgpt</td><td>6</td><td>scientifique</td><td>8</td></tr><tr><td>chatgpt</td><td>8</td><td>bien</td><td>5</td><td>aspect</td><td>7</td></tr><tr><td>clair</td><td>6</td><td>expliquer</td><td>5</td><td>bien</td><td>7</td></tr><tr><td>comprendre</td><td>6</td><td>scientifique</td><td>5</td><td>technique</td><td>7</td></tr><tr><td>scientifique</td><td>6</td><td>technique</td><td>5</td><td>article</td><td>6</td></tr><tr><td>expliquer</td><td>5</td><td>clair</td><td>4</td><td>chatgpt</td><td>6</td></tr><tr><td>ia</td><td>5</td><td>aspects</td><td>3</td><td>expliquer</td><td>6</td></tr><tr><td>technique</td><td>5</td><td>assez</td><td>3</td><td>comprendre</td><td>5</td></tr></tbody></table>
<table><caption>Table 1. Word frequency of the responses to the open question “I find that the article gives me a more or less clear understanding of the technical and scientific aspects of the subject because.”</caption><tr><td>concret</td><td>4</td><td>fait</td><td>3</td><td>argument</td><td>3</td></tr><tr><td>mettre</td><td>4</td><td>simples</td><td>3</td><td>donnée</td><td>3</td></tr><tr><td>monde</td><td>4</td><td>sujet</td><td>3</td><td>précis</td><td>3</td></tr><tr><td>recherche</td><td>4</td><td>utilisé</td><td>3</td><td>sujet</td><td>3</td></tr><tr><td>sujet</td><td>4</td><td></td><td></td><td>terme</td><td>3</td></tr></table>

By examining the verbatim responses from the three groups, we can highlight certain excerpts to elucidate their differing perceptions.

For group 3, presented with an unrealistic image, the respondents' remarks were generally broad: “It is quite well explained”; “It is clear and well written!”; “The information provided is clear.” In contrast, group 2, exposed to a realistic image, offered responses that delved into more detailed and technical aspects referenced in the paper. Their comments included: “It evokes the different scientific points of view on the question of the limits for the use of ChatGPT”; “The article makes me understand the technical and scientific aspects of the subject because it gives a clear definition of what ChatGPT is and also how this software functions in a scientific context”; “Yes, I find that it makes me better understand the technical aspects because this article used precise survey/experiment results data to represent the appearance, functions, and look of ChatGPT.” Notably, many members of group 2 emphasized the illustrative effectiveness of the article and the relevance of examples in their responses, such as: “It explains what ChatGPT is and what its uses are”; “That it is clear and concise and the structure of the arguments is easily identifiable. Moreover, the examples from the experiments are very relevant”; “The technical aspects are later illustrated by examples or terms that allow me to visualize these aspects in real life.” The absence of similar responses in the other two groups suggests a distinctive impact of the realistic image. The feedback from group 1, which viewed no images, supports this theory. Although their responses also focused on specifics similar to group 2, they tended to emphasize data and tangible outcomes over illustrative content: “A study with the results is mentioned. This makes things more concrete”; “The numerical data are explained”; “It places ChatGPT in a concrete context.”

In summary, our analysis of the responses to the previous questions allows us to draw some conclusions about Hypotheses 1 and 2. Overall, no significant differences were identified between the three versions of the article, indicating that the presence of an image does not substantially affect reader comprehension. However, the questionnaire revealed a notable variance in how the three groups engage with the technical and scientific content of the article. Readers exposed to the stock image generally demonstrated a less thorough understanding of these elements. In contrast, those who viewed the article with the open-access institutional image displayed a deeper appreciation for the technical details. The verbatim responses to Question 9 corroborate these findings.
## 3.2. The readers' engagement

Similar to the previous section, Hypotheses 3 and 4, which concern the impact of images on reader engagement in science communication, are discussed together as they are based on the same survey questions.

Generally, respondents express interest in the discoveries presented in the article. Readers of the article with the open-access institutional image show greater interest than those of the article with the stock image, while readers of the article without images exhibit the least interest.

<figure><img src="image_4.png" /><figcaption>Figure 3. Responses to questions 7 and 8 of the questionnaire.</figcaption></figure>

Respondents generally displayed an inclination to learn more about the subject. Those who read the articles with images showed a greater eagerness to engage further than those who read the article without images. Although there are no significant differences in willingness to learn between the two types of images, stock images seem to slightly enhance engagement.

To substantiate these observations, we analyzed responses to the open-ended question: “I find that the article makes me want to know more about the topic because.” Across all groups, the frequent use of verbs like “pouvoir” (can), “faire” (do), “concerner” (concern), “savoir” (know), and “être” (be) reveals varied levels of engagement with the subject. Notably, the prominence of “être” and “concerner,” alongside mentions of “AI” and “étudiant” (student) in responses to the article with the stock image, indicates a notably higher level of engagement compared to the responses to the article without images, where more passive verbs such as “savoir” (know) are more common. The vocabulary used in responses to the article with the open-access reflects an intermediate level of engagement.

<table><thead><tr><th>Without image</th><th>frequency</th><th>OA image</th><th>frequency</th><th>Stock image</th><th>frequency</th></tr></thead><tbody><tr><td>topic</td><td>14</td><td>article</td><td>11</td><td>topic</td><td>11</td></tr><tr><td>can</td><td>9</td><td>topic</td><td>10</td><td>AI</td><td>9</td></tr><tr><td>chatgpt</td><td>8</td><td>chatgpt</td><td>9</td><td>to be</td><td>8</td></tr><tr><td>know</td><td>8</td><td>do</td><td>9</td><td>concern</td><td>5</td></tr><tr><td>article</td><td>7</td><td>to be</td><td>7</td><td>can</td><td>5</td></tr><tr><td>AI</td><td>6</td><td>AI</td><td>7</td><td>student</td><td>4</td></tr></tbody></table>
<table><caption>Table 2. Word frequency of the responses to the open question "I find that the article makes me want to know more about the topic because."</caption><tr><td>interesting</td><td>6</td><td>know</td><td>7</td><td>academic</td><td>4</td></tr><tr><td>want</td><td>5</td><td>want</td><td>6</td><td>article</td><td>3</td></tr><tr><td>current</td><td>4</td><td>argument</td><td>4</td><td>field</td><td>3</td></tr><tr><td>to be</td><td>4</td><td>interesting</td><td>4</td><td>interesting</td><td>3</td></tr><tr><td>technology</td><td>4</td><td>have</td><td>3</td><td>research</td><td>3</td></tr><tr><td>give</td><td>3</td><td>good</td><td>3</td><td></td><td></td></tr><tr><td>information</td><td>3</td><td>concern</td><td>3</td><td></td><td></td></tr><tr><td>research</td><td>3</td><td>give</td><td>3</td><td></td><td></td></tr><tr><td></td><td></td><td>think</td><td>3</td><td></td><td></td></tr><tr><td></td><td></td><td>can</td><td>3</td><td></td><td></td></tr><tr><td></td><td></td><td>time</td><td>3</td><td></td><td></td></tr></table>

A close reading of all comments reveals that for group 3 (stock image), all responses except one demonstrate robust engagement with the subject. Notably, several respondents articulated their personal stakes as students in statements like: "I am a student and this directly concerns me in scenarios such as university research or thesis writing"; "I am a student and as ChatGPT continues to evolve, I believe we will inevitably need to engage with it"; "Because the article directly concerns me"; "This topic affects all of us and could indirectly impact my life"; "Moreover, as a student, this issue concerns me."

In summary, while the differences among the three groups are not statistically significant, two notable trends are apparent. Firstly, images seem to bolster reader engagement. Across the two questions analyzed, respondents in the group without images displayed less engagement compared to the groups with images. Secondly, an analysis of the open-ended responses suggests that stock images elicit greater engagement than other types.

Conclusion

The objective of this article was to explore the persuasive power of two distinct types of images depicting AI—open-access institutional images, and stock images—and to assess their impact on reader engagement. Our analysis has yielded several key findings and insights.
In our experiment, articles enriched with images generally garnered more favorable responses. Readers tended to perceive such articles as more logical and well-written. Interestingly, when we compared the impact of realistic images to stock images, we observed that while realistic images had a slight advantage, the differences were not statistically significant. This implies that the presence of any image, regardless of its source or content, plays a role in enhancing perceived logical structure and writing quality.

Moreover, in our experiment, we found that images had a limited effect on the perceived credibility of the articles. Readers generally considered all versions of the articles as credible sources of information. However, when it came to understanding the technical and scientific aspects of the subject matter, the presence or absence of images did influence perception. Readers of articles without images and those featuring open-access institutional images felt they had a better grasp of these aspects, while stock images had a slightly dampening effect on comprehension. The analysis of responses to the open-ended questions further substantiated these trends, as readers exposed to articles with realistic images provided more detailed insights into the technical content.

Another significant finding from our experiment was the positive impact of images on reader engagement. Articles with images generated greater interest among readers in the discoveries presented and encouraged them to seek more information on the subject. While the type of image—realistic or stock—did not statistically affect reader engagement, the analysis of the responses to the open-ended questions showed that stock images seem to slightly boost engagement.

Before proceeding, an important caveat is necessary. AI images are inherently multistable. The concept of multistability was introduced within postphenomenology to refer, among other things, to the variety of ways in which the same technological object can be appropriated in different social and cultural contexts (De Boer 2023). One could say that AI images are multistable in terms of their effectiveness because the same image can have different effects in different contexts, depending on variations in reception contexts, audiences, and media environments. An analysis of the effects of these images should therefore be complemented by empirical and ethnographic studies of a techno-semiotic and semio-pragmatic nature, for instance. In truth, this multistability is a characteristic of any image and, more broadly, any text—in the semiotic sense of the term. However, one could argue that this is even more the case for AI images, and particularly for stock images that depict AI. Indeed, precisely because of the intrinsic difficulty—or rather, the intrinsic impossibility—of representing AI “as it truly is,” these images leave open the gap between what is said and unsaid, visible and invisible, sign and referent. And it is precisely within this gap that a great variety of interpretations and effects can emerge.

From our observations, we can draw two conclusions. Firstly, it is evident that AI images impact the perception of textual content. While this does not affect the credibility attributed to the article, it certainly impacts the perception of the logical structure and writing quality, the impression of understanding the scientific and technical aspects of the subject matter, and the engagement. Existing critical literature is therefore correct in stating that science communication about AI must pay more attention to how AI is depicted. AI images have a persuasive power, and thus, it can be assumed that there is a need for an ethical framework to regulate their use. Secondly, it is clear that there are differences between open-access institutional images (in particular, those representing “the people behind the technology”) and more imaginative commercial stock images. However, these differences are not as significant as the critical literature might suggest: they exclusively concern the understanding of technical
and scientific aspects, and the general engagement of the readers. In the first case, realistic images lead to a greater impression of understanding, while in the second, imaginative images provoke a bit more of engagement.

In this regard, two observations can be made: (1) contrary to what the literature suggests, stock images do not have an “anaesthetic” effect. Rather, they make the reader feel personally concerned; (2) however, one could argue that the critical literature is correct in asserting that, even if there is engagement, it remains superficial, as it does not promote a real understanding of the technological and scientific implications. Robert (2012) introduced the concept of “computational unthought” (*impensé informatique*) to account for the effects of the discourses a society produces about its own information and communication technologies. According to Robert, the primary effect is to prevent any concrete understanding and, with it, any critical stance. The term “unthought” refers to the fact that these discourses do not offer frameworks for understanding digital technologies. Instead, they serve another function: “drowning us in a river of discourse [...] that occupies space and thus prevents the emergence of relevant political or ethical questions (except in the intellectually sterile form of the fear of ‘being all tracked’)” (Robert 2012: 221).

Yet, one could also formulate a different hypothesis, which we will only briefly outline here. One might argue that the understanding of scientific and technical aspects promoted by institutional and realistic images is merely an illusion, and that such images do not truly provide access to this understanding. If this were true, then the criticism of stock images of AI could be turned on its head. After all, stock images depicting AI are relatively harmless in that their kitschy and highly stereotyped nature makes them easily recognizable (they are now part of our consumer culture), allowing us to critique or take them with irony—just as the X profile Not My Robot! does. A similar argument about stock images in general was made by Frosh (2020). Far more dangerous are the images that aim to be authentic and realistic, such as those in the CreativeInsights collection “AI: Visualizing the Invisible” by Getty Images.<fnref n="8" /> If we were to go even further, we might say that the problem with these realistic images is that they trivialize AI and our relationship with it. In contrast, imaginative images constantly magnify AI, and it is precisely this magnification that provokes our wonder and, consequently, our engagement.

In this sense, the repetitiveness of subjects, forms, and colors in these images, and the near-anonymity of their creators, should not be seen as signs of their ethical, aesthetical, and ontological inferiority. Much like Byzantine icons, it might be said that the purpose of these images is not to provide access to the thing represented; nor is it to exalt the perspectivism (i.e., the subjective point of view) of their authors. As Florensky argued in his essay on reverse perspective (Florensky 2002: 197-272), these are merely the assumptions of Western painting. The purpose of icons, through their repetitiveness and lack of perspective, was to act as a bridge between the visible and the invisible, without the latter being exhausted by the former.<fnref n="9" />

<footnote n="8">⁸ https://creativeinsights.gettyimages.com/en/trends/technology/ai-visualising-the-invisible. Accessed 10 February 2025.</footnote>

<footnote n="9">⁹ This last consideration opens up a range of reflections on the critique of critique—an avenue we neither intend nor are able to explore here. Beyond the question raised above about whether AI critique—in this case, the critique of AI images—is truly targeting the right subject (for instance, focusing on stock images rather than custom-made visuals that trivialize AI’s presence in society), one might also ask whether critique itself is, in some way, feeding off the hype surrounding AI discourse—and, ultimately, amplifying it. Of course, this paper is not exempt from such meta-critique. On this topic, see, for example, Vinsel (2019).</footnote>
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