# People are not becoming “AIholic”: Questioning the “ChatGPT addiction” construct

Víctor Ciudad-Fernández, Cora von Hammerstein, Joël Billieux

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Víctor Ciudad-Fernández, Cora von Hammerstein, Joël Billieux. People are not becoming “AIholic”: Questioning the “ChatGPT addiction” construct. Addictive Behaviors, 2025, 166, 10.1016/j.addbeh.2025.108325. hal-05104834

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# People are not becoming “Alholic”: Questioning the “ChatGPT addiction” construct

Víctor Ciudad-Fernández<sup>a, b</sup>, Cora von Hammerstein<sup>c, d</sup>, Joël Billieux<sup>e, f, *</sup>

## ARTICLE INFO

### Keywords:

ChatGPT addiction

Generative large language models

Conversational artificial intelligence

Behavioral addictions

## ABSTRACT

Generative artificial intelligence (AI) chatbots such as ChatGPT have rapidly gained popularity in many daily life spheres, even sparking scholarly debate about a potential “ChatGPT addiction.” Throughout history, new technologies have repeatedly been associated with widespread concerns and “moral panics,” especially when their adoption is sudden and involves significant changes in daily functioning. It is thus no surprise that researchers have examined whether intensive use of ChatGPT can be considered an addictive behavior. At least four scales measuring ChatGPT addiction have been developed so far, all framed after substance use disorder criteria. Drawing parallels with previous cases of pathologizing everyday behaviors, we caution against labeling and defining intensive or habitual chatbot use as addictive behavior. To label a behavior as addictive, there must be convincing evidence of negative consequences, impaired control, psychological distress, and functional impairment. However, the existing research on problematic use of ChatGPT or other conversational AI bots fails to provide such robust scientific evidence. Caution is thus warranted to avoid (over)pathologization, inappropriate or unnecessary treatments, and excessive regulation of tools that have many benefits when used in a mindful and regulated manner.

Generative artificial intelligence (AI) chatbots, such as ChatGPT (Chat Generative Pre-Trained Transformer), are tools that have rapidly reshaped how people interact with technology. They offer remarkable utility, from content creation to problem solving (Katz et al., 2023; Orrù et al., 2023). Throughout history, new technologies have repeatedly been associated with widespread concerns and “moral panics,” especially when their adoption is sudden and involves significant changes in daily functioning (see Orben, 2020). It was thus unsurprising when we recently discovered that researchers have examined whether intensive use of ChatGPT<fnref n="1" /> can be considered an addictive behavior, sometimes framing it as “Alholic” (Salah et al., 2024). In fact, multiple scholars have raised concerns about potential chatbot addiction (e.g., Xie et al., 2023; Xie & Pentina, 2022; Yankouskaya et al., 2024). The tendency to

use medical terminology or an addiction framework to conceptualize and assess emerging, poorly understood, technology-mediated problematic or dysregulated behaviors is neither new nor original (e.g., television or radio addiction, Gruenberg, 1935; Meerloo, 1954; Internet addiction, Young, 1998). This approach has been at the center of continuous debates and controversies over the past decade (Satchell et al., 2021; Starcevic et al., 2018).

To our knowledge, four scales have been developed to date to assess problematic or “addictive” use of generative AI chatbots. The development of these scales has followed a circular *confirmatory approach* consisting in reusing items from already existing scales that assess addictive or compulsive online activities, which have themselves been developed by recycling items originally intended to assess substance use disorders

<footnote><sup>a</sup> Department of Personality, Evaluation, and Psychological Treatments, University of Valencia, Valencia, Spain</footnote>

<footnote><sup>b</sup> Polibienestar Institute, University of Valencia, Valencia, Spain</footnote>

<footnote><sup>c</sup> Department of Psychiatry and Addiction Medicine, Fernand Widal Hospital APHP, Paris, France</footnote>

<footnote><sup>d</sup> Paris Cité University, INSERM, therapeutic optimization in neuropharmacology OPTEN U1144, Paris, France</footnote>

<footnote><sup>e</sup> Institute of Psychology, University of Lausanne, Lausanne, Switzerland</footnote>

<footnote><sup>f</sup> Center for Excessive Gambling, Addiction Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland</footnote>

<footnote>* Corresponding author at: Institute of Psychology, University of Lausanne, Lausanne, Switzerland.</footnote>

<footnote>E-mail addresses: victor.ciudad@uv.es (V. Ciudad-Fernández), cora.vonhammerstein@aphp.fr (C. von Hammerstein), joel.billieux@unil.ch (J. Billieux).</footnote>

<footnote n="1">¹ Please note that while substantial part of current research in this topic targeted ChatGPT, the same reasoning could be applied to any kind of generative AI chatbot. In that regard, the present authors predict that in a few months or years, scholars will criticize these initial works with the argument that the focus should be on a specific type of online Application (generative artificial intelligence chatbot) rather than to a specific app (e.g., ChatGPT). Such a step occurred several times in the past, for example in relation to problematic social network use (e.g., Griffiths, 2012).</footnote>
(Billieux et al., 2015). Such scales thus measure problematic use of generative AI chatbots by assessing symptoms such as “tolerance” or “withdrawal,” which are central in substance use and addictive disorders but have, to date, never been empirically studied in the context of problematic or “addictive” use of generative AI chatbots. Relatedly, past research also suggests that criteria such as “tolerance”, “withdrawal”, or “cognitive salience” are not necessarily valid (nor clinically useful) when applied to excessive, addictive, or compulsive involvement in activities such as gaming or social network use (e.g., Castro-Calvo et al., 2021; Fournier et al., 2023; Starcevic, 2016).

First, Hu et al. (2023) developed the Problematic Use of Conversational Artificial Intelligence scale (PUCAI-6) adapted from an existing problematic social media use measure (Bergen Social Media Addiction Scale, Andreassen et al., 2016). Second, Yu et al. (2024) developed the Problematic ChatGPT Use Scale (PCUS-11), aiming to assess ChatGPT addiction with 11 items based on DSM-5 Internet gaming disorder criteria. Third, Zhou and Zhang (2024) developed the Generative Artificial Intelligence Addiction scale (GAA-3) by using three items from a preexisting measure that assessed WeChat addiction, a social media platform (Cao et al., 2020). Fourth, Zhang et al. (2024) developed an eight-item scale to assess AI chatbot dependence (AICD) from previous questionnaires assessing technology and AI dependence (e.g., Huang et al., 2024) and from in-depth interviews of heavy users of AI (inclusion

criteria: use of AI chatbots almost daily).

As shown in Table 1, and consistent with the confirmatory approach, items in these scales assess classic addiction symptoms such as salience/preoccupation/habit, withdrawal/dependence, tolerance, loss of control/relapse, conflict/negative consequences, or mood modification/escape. Interestingly, Zhang et al. (2024) incorporated qualitative interviews rather than relying solely on existing questionnaires, which can help mitigate overreliance on addiction criteria (Flayelle et al., 2022). Unfortunately, these authors did not provide details that would have allowed readers to understand how qualitative interviews were useful in generating items. Instead, they only listed the retained items, which, unsurprisingly, align with the addiction framework anchoring their study.

To label a behavior as compulsive, dysfunctional, or addictive, there must be convincing evidence of negative consequences, impaired control, psychological distress, or functional impairment (Billieux et al., 2017; Kardefelt-Winther et al., 2017). This is also in line with the central role of functional impairment in the definition and diagnosis of non-substance-related addictive behavior according to the ICD-11 framework (Billieux et al., 2017; Brand et al., 2022). Moreover, any attempt to consider an emerging and potentially problematic behavior as “addictive” requires different types of scientific evidence. This includes evidence from multiple scientific studies showing that the problematic

<caption>**Table 1**</caption>

Items measuring problematic use of conversational artificial intelligence, problematic ChatGPT use, generative artificial intelligence addiction, and AI chatbot dependences.

<table><thead><tr><th>Criteria being targeted</th><th>PUCAI-6 (Hu et al., 2023)</th><th>PCUS-11 (Yu et al., 2024)</th><th>GAA-3 (Zhou & Zhang, 2024)</th><th>AICD (Zhang et al., 2024)</th></tr></thead><tbody><tr><td>Salience, preoccupation, habit</td><td>I spend a lot of time thinking about CAI.</td><td>I constantly have thoughts related to ChatGPT lingering in my mind. I frequently find myself opening ChatGPT even when I had no initial intention to use it.</td><td>—</td><td>Compared with other people or things, I prefer to spend time on AI chatbots. Even if not actively using AI chatbots, I keep them logged in or running in the background. Even when facing tasks or jobs that I could easily complete myself, I tend to seek assistance from AI chatbots.</td></tr><tr><td>Tolerance-like symptoms</td><td>I felt an urge to use CAI more and more.</td><td>I progressively spend more time on ChatGPT.</td><td>—</td><td>I am spending increasingly more time on AI chatbots.</td></tr><tr><td>Mood modification or escape/coping</td><td>I used CAI in order to forget about personal problems.</td><td>I turn to ChatGPT to alleviate feelings of helplessness or anxiety.</td><td>—</td><td>—</td></tr><tr><td>Relapse or loss of control</td><td>I tried to cut down on the use of CAI without success.</td><td>I have attempted to reduce my ChatGPT usage, but without success.</td><td>I find it difficult to control my using generative AI.</td><td>—</td></tr><tr><td>Withdrawal-like symptoms and perceived dependence</td><td>I become restless or troubled if I have been prohibited from using CAI.</td><td>I experience anxiety or irritability when unable to access ChatGPT.</td><td>I feel a bit uneasy when I cannot use generative AI.</td><td>If unable to use AI chatbots, I would feel anxious or uncomfortable.<br>I need to open AI chatbots before starting work or tasks.<br>If unable to use AI chatbots, I would find it difficult to obtain the necessary information.<br>For me, life without AI chatbots would be inconvenient.</td></tr><tr><td>Conflict or negative consequences</td><td>I used CAI so much that it has had a negative impact on my job/studies.</td><td>I lost interest in previously enjoyed activities due to using ChatGPT. My use of ChatGPT causes me to procrastinate and delay completing necessary tasks.</td><td>Using generative AI sometimes interferes with my other activities (such as study and work).</td><td>—</td></tr><tr><td></td><td>—</td><td>I devote excessive time to ChatGPT despite problems.</td><td>—</td><td>—</td></tr><tr><td></td><td>—</td><td>I suffer from sleep deprivation due to excessive use of ChatGPT.</td><td>—</td><td>—</td></tr><tr><td></td><td>—</td><td>I kept the extent of involvement with ChatGPT usage from family, friends, or therapists.</td><td>—</td><td>—</td></tr></tbody></table>

Note. PUCAI-6 was developed by Hu et al. (2023), PCUS-11 by Yu et al. (2024), and GAA-3 by Zhou and Zhang (2024). GAA-3 assesses only relapse/loss of control, withdrawal, and conflict criteria. AICD was developed by Zhang et al. (2024). The list of criteria and their correspondence with the items of the four scales considered is proposed by the authors of this paper and is not necessarily presented as such in the research papers describing these scales. AI = artificial intelligence; CAI = conversational artificial intelligence.
behavior is clinically relevant (e.g., studies conducted with treatment-seeking cases or persons experiencing concrete negative consequences or functional impairment), or evidence suggesting that psychological or neurobiological mechanisms involved in established addictive disorders are also valid for the candidate condition (Brand et al., 2022). However, the existing research on problematic ChatGPT or conversational AI use fails to provide such robust scientific evidence.

Addictive use of generative AI chatbots, as assessed by these scales, has been linked to various psychological constructs. Yu et al. (2024) reported moderate correlations between problematic ChatGPT use and weekly usage time and depression, but no significant relationships were found with perceived chatbot accuracy, depth/breadth of answers, or satisfaction. Notably, two items of the scale they used (PCUS-11, see Table 1) explicitly refer to negative affect (anxiety, irritability, or helplessness), which may artificially inflate the correlation found with depressive symptoms (for a similar phenomenon in problem gaming, see Ballou & Van Rooij, 2021). Hu et al. (2023) reported a positive correlation between problematic use of conversational AI and social anxiety, loneliness, and rumination. However, when several predictors were considered in a multivariate analysis, loneliness was no longer a significant predictor. Zhou and Zhang (2024) found a positive association between generative AI “addiction” and both flow experience and attachment to AI, which may simply reflect adaptive and non-problematic use of AI chatbot applications.

Current conceptualizations of behavioral addiction posit that clinically relevant functional impairment is central to these disorders, which concern common behaviors and daily life activities (Billieux et al., 2017; Brand et al., 2022; Kardefelt-Winther et al., 2017). Yet, evidence linking intensive AI chatbot use to concrete negative consequences is extremely limited. For example, Xie et al. (2023) proposed vague and anecdotal interpretations of heavy users' statements (e.g., “It's a necessity to me,” “It's like my best friend,” or “I would feel empty without it”), and Lin and Chien (2024) described a clinical case in which a person configured ChatGPT to emulate a female idol he admired, including physical attributes, personality, way of speaking, and manner. Following the confirmatory approach described by Billieux et al. (2015), these authors elaborate on what they consider the “addictive potential” and “side effects” of ChatGPT, but an alternative yet plausible hypothesis is that the AI application simply serves as a medium through which this person can satisfy his obsession with the idol. Along the same lines, a recent study conducted by Huang et al. (2024) revealed that depression and anxiety are longitudinally and positively associated with dependence on AI chatbots but not the other way around, despite all three variables being associated cross-sectionally. These results suggest that people experiencing mental health issues might use AI chatbots like ChatGPT more intensively (even potentially as a coping or compensatory strategy) but contradict the view that such applications might cause mental health issues. Finally, Zhang et al. (2024) investigated negative consequences through open-ended questions on ChatGPT dependence among university students. The most frequent complaints reported included increased laziness, decreased creativity, reduced critical thinking, and encountering more inaccuracies when writing. Although these aspects can indeed be viewed as downsides of generative AI chatbots, they hardly fit the classic profile of life-altering or clinically significant harm required to qualify as a genuine addictive disorder.

In summary, we are not saying that problematic use of AI chatbots does not exist, but rather that existing evidence provides very weak evidence to potentially consider intensive AI chatbot use as an addictive behavior. Although hazardous use of technology is a relevant public health issue (World Health Organization, 2015), (over)pathologization of intensive technology endorsement is likely to promote stigmatization of users, inappropriate or unnecessary treatments, and excessive regulation of tools that have many benefits when used in a mindful and regulated manner.

## CRediT authorship contribution statement

**Víctor Ciudad-Fernández:** Writing – original draft, Conceptualization. **Cora von Hammerstein:** Writing – review & editing, Conceptualization. **Joël Billieux:** Writing – original draft, Conceptualization.

## Funding

VCF was supported by the grant FPU21/00527 funded by the Ministry of Science, Innovation and Universities, Spain.

## Declaration of competing interest

Joël Billieux serves as an editorial board member for Addictive Behaviors. Please note, however, that he was not involved at any stage during the editorial process. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

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