Artificial intelligence has become an increasingly advanced technology that is becoming an integral part of everyday life in society (Muttaqin et al., 2023). Its presence has been utilised by humans across various sectors, ranging from industry, healthcare, agriculture and finance to education (Subiyantoro, 2024). As its use becomes more widespread, previous research has also shown that attitudes, experiences, and intentions regarding the use of artificial intelligence can arise in various contexts, such as work, healthcare, and education (Gnambs et al., 2025). This indicates that artificial intelligence is no longer merely a technical technological development, but has become an integral part of human activities in everyday life.

The increasing use of artificial intelligence demonstrates that this technology not only offers benefits but also gives rise to a number of risks. In the field of education, artificial intelligence is seen as having the potential to enhance the learning process, accessibility and student creativity (Gustina et al., 2024). Furthermore, the use of artificial intelligence in study also consider to help build self-confidence and create a more comfortable learning environment, although caution is still required as it involves personal data and user privacy (Arly et al., 2023). Thus, the use of artificial intelligence in learning is not only understood in terms of its benefits but is also accompanied by vigilance regarding certain risks.

On the other hand, in the workplace context, a positive attitude towards artificial intelligence can coexist with scepticism regarding the system’s limitations, the possibility of errors, and potential data bias (Pasolon et al., 2025). A similar pattern is evident among Generation Z in the workplace, who view artificial intelligence as a tool to enhance efficiency, effectiveness, and creativity, yet remain concerned about its negative impacts on their jobs, privacy, and future (Tobing et al., 2025). This situation indicates that attitudes towards artificial intelligence reflect both acceptance of its benefits and vigilance regarding its risks.

These various findings indicate that attitudes towards artificial intelligence do not always follow a single, straightforward trajectory. Generation Z is known to exhibit a more positive attitude towards artificial intelligence and has a stronger intention to continue using it in the future compared to other generations (Susanto et al., 2024). Meanwhile, research on attitudes towards artificial intelligence has also been extensively developed through two main dimensions: positive and negative attitudes towards artificial intelligence (Schepman & Rodway, 2023). Consequently, attitudes towards artificial intelligence cannot be adequately understood solely through the categories of positive or negative, as they also involve considerations of benefits, needs, values, and vigilance regarding risks.

Nevertheless, an approach that treats attitudes as a general evaluation of artificial intelligence—whether positive or negative—has not yet fully accounted for the psychological functions underlying the formation of such attitudes. At a psychological level, the reasons why an individual maintains or changes an attitude are linked to the functions that the attitude serves for that individual (Katz, 1960). Through Functional Attitude Theory, attitudes are understood based on the functions they serve for the individual, namely utilitarian, ego-defensive, value-expressive, and knowledge. These four functions explain attitudes based on perceived benefits, self-protection from threats or risks, the expression of values and self-identity, and the need to understand information. Therefore, this approach allows attitudes towards artificial intelligence to be understood more deeply than merely as positive or negative judgements.

In line with this approach, research into attitudes towards artificial intelligence has also begun to be developed in the context of university students through the ‘Functional Attitudes towards Artificial Intelligence’ framework, which identifies three dimensions: utility-knowledge, value-expressive, and ego-defence (Li & Wang, 2025). However, this research remains focused on university students and places greater emphasis on the development of measurement dimensions. This situation indicates that research into the psychological functions of attitudes towards the use of artificial intelligence as a source of information within user communities closely associated with artificial intelligence issues remains limited.

Given these circumstances, this study focuses on members of the Indonesian AI Forum. This community was selected because it is an open forum that anyone can join without any specific membership requirements. This characteristic makes the Indonesian AI Forum a space that brings together individuals from diverse backgrounds who share an interest in, exposure to, or connection with issues relating to artificial intelligence. In the context of this study, members of this community are considered relevant because they are part of a discussion environment focused on artificial intelligence, making it more likely that they possess experience, perspectives, and practical use of artificial intelligence in the process of information seeking. Thus, the selection of members of the Indonesian AI Forum is not intended to represent the general public, but rather to examine the function of attitudes within an open community that has a close connection to issues surrounding artificial intelligence. Based on the above, this study aims to analyse the level of functional attitudes of members of the Indonesian AI Forum towards the use of artificial intelligence as a source of information based on the perspective of Functional Attitude Theory.

2.1. Functional Attitude Theory

Functional Attitude Theory explains that attitudes not only reflect an evaluation of an object but are also maintained because they serve a specific psychological function for the individual. Through this theory attitudes are understood in terms of the reasons why a person maintains a particular attitude, so that the explanation does not stop at whether the attitude is positive or negative. In this theory, attitudes are categorised into four main functions: utilitarian, ego-defensive, value-expressive, and knowledge. The utilitarian function relates to the direct benefits felt by the individual; the ego-defensive function relates to self-protection from threats or risks; the value-expressive function relates to values, beliefs, and identity, whilst the knowledge function relates to the need to understand and manage information (Katz, 1960). The functional approach has also been used to explain attitudes towards artificial intelligence in the context of university students. Attitudes towards artificial intelligence are explained through three dimensions: utility-knowledge, value-expressive, and ego-defence (Li & Wang, 2025). This indicates that the functional approach is relevant for understanding attitudes towards artificial intelligence, particularly when the technology is assessed not only in terms of its benefits but also in terms of the values and concerns associated with it.

2.2. Attitude

An attitude is an individual’s evaluative tendency towards an object, reflected through their way of thinking, feeling, and behavioural tendencies. An attitude not only indicates whether a person accepts or rejects an object, but also describes how the individual evaluates that object based on their beliefs, emotions, and behavioural tendencies. In the study of social psychology, attitudes are generally understood through three main components: the cognitive, affective, and conative components. The cognitive component relates to what an individual knows or believes about an object. The affective component relates to the feelings evoked by that object whilst the conative component relates to the tendency to act (Azwar, 2022). Attitudes also do not form suddenly, but develop through experience, social interaction, cultural environment, media, and the influence of parties considered important by the individual. Therefore, attitudes towards an object may differ from one individual to another, depending on the experiences and social context underpinning.

2.3. Artificial Intelligence

Artificial intelligence refers to the ability of a system or machine to imitate the workings of human intelligence. This ability encompasses information gathering, contextual understanding, analysis, decision-making, and learning from past experiences. With these capabilities, artificial intelligence has been developed to assist humans in completing complex tasks more quickly and efficiently (Muttaqin et al., 2023). In its development, artificial intelligence is not only used in the industrial sector but is also becoming increasingly integrated into daily life, including in learning, working, and seeking information. This technology can help users obtain information according to their needs, complete routine tasks, and open up opportunities for efficiency and the development of creativity (Moore, 2019). These conditions indicate that artificial intelligence is understood not merely as advanced technology, but also as a tool that is becoming increasingly relevant in various aspects of society.

2.4. Information

Information is a need that arises when individuals feel they do not yet know, do not yet understand, or still have gaps in their knowledge regarding a particular matter. In such circumstances, individuals are driven to seek information that can help answer questions, reduce uncertainty, and clarify the situation at hand. Therefore, the search for information can be understood as a process of discovering the knowledge or messages required for individuals to understand a matter more effectively (Rifai, 2014). The information-seeking process takes place through several stages, ranging from an awareness of the need for information, the selection of a topic or source, the exploration of information, to the collection and presentation of information deemed relevant. These stages show that information seeking is not merely the activity of finding answers, but also a process of understanding, selecting, and managing information to suit individual needs. In the context of this study, information seeking is relevant because the use of artificial intelligence is directly related to how respondents obtain, understand, and utilise information (Rahmadi et al., 2022).

This study employs a quantitative approach using descriptive methods to describe the facts or characteristics of the population in a systematic and accurate manner (Hardani et al., 2020). The quantitative approach in this study is grounded in the positivist paradigm, which views reality as something concrete, observable, and whose objectivity can be controlled through systematic research procedures (Andini et al., 2023). Through this approach, this study is not aimed at testing the relationship between variables but rather at describing the level of respondents’ attitudes towards the use of artificial intelligence as a source of information.

The population for this study comprises all members of the Indonesian AI Forum group on Telegram, whilst the sample was selected using purposive sampling. This technique was employed because the selection of respondents was based on pre-defined criteria and aligned with the research objectives (Memon et al., 2025). The number of respondents in this study was 73 with the following characteristics: being members of the Forum AI Indonesia group, willing to participate, using artificial intelligence to assist in information retrieval, and utilising it in their work or daily activities. The Indonesian AI Forum was selected because it is an open community that anyone can join, and thus comprises individuals from diverse backgrounds who share an interest in or exposure to issues surrounding artificial intelligence. Consequently, the sample in this study is not intended to represent the general public, but rather to illustrate the role of attitudes within a community that has a close connection to issues surrounding artificial intelligence.

Data collection was carried out using a questionnaire with a five-point Likert scale. The questionnaire was used as it is a data collection tool consisting of a series of written questions designed to obtain relevant data from respondents in line with the research topic (Abdullah et al., 2022). This study using 1 to 5 scale.

Table 1. Respondent Characteristics

In Table 1, the research instrument was developed based on Functional Attitude Theory, which encompasses four functions of attitude, namely utilitarian, ego-defensive, value-expressive, and knowledge. In this study, these four functions were used as dimensions in the development of the research instrument. The initial instrument consisted of 22 items covering 11 indicators. Following instrument validation, one item within the value-expressive function were deemed invalid and removed, leaving the final instrument comprising 21 items: 6 utilitarian items, 6 ego-defensive items, 5 value-expressive items, and 4 knowledge items.

Validity and reliability tests were conducted on 30 respondents outside the main sample who had similar characteristics to the target population. The use of a pilot sample similar to the target population is in line with the recommendation that instruments should be tested on a small pilot sample before being used in the main study (Kalkbrenner, 2021). Validity testing utilised Pearson’s product-moment correlation, whilst reliability testing employed Cronbach’s alpha with the aid of SPSS. The test results indicated that one item within the value-expressive function was invalid, with a calculated r value (0.179) lower than the table r (0.361), and was therefore excluded from the instrument. Following the revisions, the reliability coefficients for each function were 0.853 for utilitarian, 0.821 for ego-defensive, 0.811 for value-expressive, and 0.901 for knowledge. Consequently, the research instrument was deemed suitable for use in this study.

The data were analysed using descriptive statistics, including frequency, percentage and mean, to describe the distribution of respondents’ answers for each attitude function. Descriptive analysis was used to describe the data collected, and the results can be presented in the form of frequency distributions and measures of central tendency such as the mean (Abdullah et al., 2022). Furthermore, respondents’ scores were also categorised to examine the overall level of attitude functions as well as that of each individual function. Thus, the research findings not only reveal the average trends in respondents’ answers but also illustrate the distribution of attitude function levels among the study’s respondents.

Before presenting the research findings regarding the attitudes of members of the Indonesian AI Forum towards the use of artificial intelligence as a source of information, we will first outline the characteristics of the respondents included in this study.  These characteristics include gender, age, and the frequency of artificial intelligence use, as shown in Table 2.

Table 2. Respondent Characteristics

Source: Primary Data Processed (2026)

Based on Table 2, this study involved a higher proportion of female respondents (65.8%) than male respondents (34.2%). These data indicate that female respondents constituted the majority of participants in this study. This suggests that female respondents are more likely to participate in research. In terms of age characteristics, respondents were grouped into the early adolescent category (12–16 years), late adolescent (17–25 years), early adult (26–35 years), and late adult (36–45 years). Ministry of Health of the Republic of Indonesia (2009) findings indicate that the majority of respondents were in the late adolescence group (79.5%). Meanwhile, respondents in the early adulthood group accounted for 15.1%, whilst those in the early adolescence and late adulthood groups each accounted for 2.7%. This indicates that the respondents in this study were predominantly from younger age groups. The dominance of younger age groups in this study is also consistent with previous research indicating that Generation Z is a generation that tends to be more adaptable to technological developments and has a higher tendency to continue using artificial intelligence compared to other generations (Susanto et al., 2024). Thus, the age characteristics of the respondents in this study align with a group closely associated with developments in digital technology.

Meanwhile, regarding the frequency of artificial intelligence use, the majority of respondents used artificial intelligence every day (45.2%), followed by use five days a week (19.2%). This indicates that the use of artificial intelligence is becoming increasingly inseparable from daily activities, both in personal and professional contexts, meaning that its use tends to occur on a routine basis. The results of this study are presented through two forms of analysis. Firstly, mean value analysis was used to examine the general trends in respondents’ answers for each attitude function. Secondly, scores were also grouped into five categories: very weak, weak, moderately formed, strong, and very strong. This categorisation is based on the mean and standard deviation of the scores for each function, so that the research results not only show average trends but also indicate the distribution of attitude function levels among respondents. Consequently, the discussion of each attitude function in this study is based on two aspects: the trend of average scores and the distribution of their levels of development.

Table 3. Distribution of Responses to the Utilitarian Function

In Table 3, the utilitarian function illustrates how respondents perceive artificial intelligence in relation to the direct benefits they experience. Based on Table 3, the utilitarian function falls into the ‘very strong’ category, with a total mean of 4.41 and an actual total score of 26.46. The highest mean value is found in Item P.4 (4.64), followed by P.3 (4.58), whilst the lowest mean score was for P.6 (4.12). These results indicate that respondents rated highly those aspects relating to the direct benefits of using artificial intelligence, particularly in helping to meet and simplifying the information search process.

These findings suggest that respondents’ attitudes towards artificial intelligence as a source of information are largely driven by the practical benefits they experience directly. Artificial intelligence is viewed as a tool that helps obtain information according to needs, speeds up the search process, and reduces barriers to obtaining and organising information. Within the framework of Functional Attitude Theory, these results align with the utilitarian function, namely when an attitude is formed because an object is perceived to provide direct utility to the individual. In the context of this study, this assessment indicates that artificial intelligence is positioned as a useful and relevant tool in the respondents’ information-seeking activities.

When viewed by category distribution, the response was ‘fairly developed’ (54.8%), followed by ‘strong’ (26.0%), ‘weak’ (12.3%), and ‘very weak’ (6.8%). For this function, no respondents fell into the ‘very strong’ category. This pattern indicates that although, on average, the utilitarian function falls within the 'very strong' category, the level of formation among respondents is predominantly in the 'fairly formed' to 'strong' range. Thus, the practical benefits of artificial intelligence appear quite clear in shaping respondents’ attitudes, although the intensity is not yet fully consistent across all respondents.

The high utilitarian function indicates that respondents’ attitudes towards artificial intelligence as a source of information is largely shaped by the practical benefits experienced directly. In the context of this study, artificial intelligence is understood as a tool that helps obtain information as needed, speeds up the search process, and facilitates information processing. This finding is consistent with Functional Attitude Theory, which views utilitarian function as the basis of attitude when an object is assessed as providing direct utility to the individual. Artificial intelligence can also enhance accessibility, aid the learning process, and support user creativity (Gustina et al., 2024). Furthermore, the utilisation of artificial intelligence across various fields demonstrates that this technology is increasingly understood in terms of its practical benefits (Subiyantoro, 2024).

Source: Primary Data Processed (2026)

Based on Table 4, unlike the utilitarian function, which is more closely related to direct benefits, the ego-defensive function reveals the respondents’ caution regarding the use of artificial intelligence. Based on Table 4, the ego-defensive function falls into the ‘moderately developed’ category, with a total mean of 3.383 and an actual total score of 20.298. The highest mean score was found in item P.1 (3.99), followed by P.2 (3.95), whilst the lowest was P.5 (2.60). These results indicate that respondents still demonstrate a degree of caution regarding the use of artificial intelligence, although this is not as strong as the functions related to practical benefits and information comprehension.

The findings indicate that respondents’ attitudes towards artificial intelligence as a source of information are shaped not only by perceived benefits, but also by efforts to anticipate potential risks. Respondents’ concerns were primarily focused on the potential for hoaxes or misinformation, a decline in creativity, and breaches of personal data security. However, at the same time, respondents did not entirely view artificial intelligence as a direct threat to their daily activities. Within the framework of Functional Attitude Theory, these results align with the ego-defensive function, namely when attitudes relate to self-protection from perceived threats or risks. Thus, the use of artificial intelligence in this study is accompanied by caution, but this caution has not yet become the strongest basis for the formation of respondents’ attitudes

Judging by the distribution of categories, it is fairly well-developed (47.9%), followed by strong (27.4%), weak (12.3%), very weak (9.6%), and very strong (2.7%). This pattern indicates that the ego-defensive function is indeed present in the respondents, but the degree of its development tends to be at a moderate level. This means that risk considerations remain part of the attitude towards artificial intelligence, but do not appear with the same intensity across all respondents.

The moderate development of the ego-defensive function indicates that respondents’ attitudes towards artificial intelligence as a source of information are still accompanied by vigilance regarding risks, although this function is not the strongest underlying factor. From the perspective of Functional Attitude Theory, this aligns with the ego-defensive function, namely when attitudes relate to efforts to protect oneself from perceived threats or risks. The use of artificial intelligence can coexist with doubts regarding the system’s limitations, potential errors, biases, and the loss of human control (Pasolon et al., 2025). Furthermore, views on artificial intelligence can also be ambivalent, meaning it is seen as beneficial yet still raises concerns about its negative impacts (Tobing et al., 2025).

Table 5. Distribution of Responses to the Value-expression

Source: Primary Data Processed (2026)

In Table 5, in addition to perceived benefits and caution, respondents’ attitudes towards artificial intelligence are also linked to their values and beliefs. As shown in Table 5, the value-expressive function falls into the ‘strong’ category, with a total mean of 3.752 and an actual total score of 18.76. The highest mean score was found in item P.4 (4.27), followed by P.5 (4.22), whilst the lowest was P.1 (3.26). These results indicate that respondents gave fairly high ratings to aspects relating to openness to technological developments and belief in the positive impact of artificial intelligence.

The findings indicate that respondents’ attitudes towards artificial intelligence as a source of information are also linked to how they express their values, beliefs and self-image. Artificial intelligence is viewed not only as a practical tool, but also as an expression of openness to technological developments and a positive outlook on technological progress. Within the framework of Functional Attitude Theory, these results align with the value-expressive function, whereby attitudes are used to display values, beliefs, and identities deemed important by the individual. Nevertheless, this function does not appear as strong as those related to practical benefits and information comprehension; consequently, the use of artificial intelligence in this study has not yet been fully positioned as the primary reflection of the respondents’ values or self-identity.

When viewed in terms of category distribution, ‘fairly established’ (32.9%) was the most common response, followed by ‘strong’ (31.5%), ‘weak’ (21.9%), ‘very weak’ (8.2%), and ‘very strong’ (5.5%). This pattern suggests that the formation of the value-expressive function tends to be more varied than that of other functions. This means that, for some respondents, artificial intelligence is linked to their values and beliefs, whilst for others this function has not yet been firmly established. Thus, the value-expressive function is present in respondents’ attitudes, but the degree to which it is formed is not uniform across all respondents.

The presence of the value-expressive function indicates that respondents’ attitudes towards artificial intelligence as a source of information are also linked to their values, beliefs, and self-image. From the perspective of Functional Attitude Theory, this aligns with the value-expressive function, wherein attitudes are used to express values and identities deemed important by the individual. Attitudes towards artificial intelligence can also be explained through the value-expressive dimension, which suggests that the use of this technology is not only related to its utility but also to its alignment with an individual’s values and beliefs (Li & Wang, 2025). Consequently, the use of artificial intelligence among respondents is not merely understood in terms of its benefits but also in terms of the extent to which the technology is perceived as consistent with the values they hold.

Table 6. Distribution of Responses to Knowledge

In Table 6, the context of using artificial intelligence as a source of information, the ‘knowledge’ function illustrates how artificial intelligence is understood in relation to the process of comprehending information. As shown in Table 6, the ‘knowledge’ function falls into the ‘very strong’ category, with a total mean of 4.4425 and an actual total score of 17.77. The highest mean score was recorded for item P.2 (4.53), whilst the lowest was for P.4 (4.36). Nevertheless, all items in this function showed high and relatively consistent mean values. These results indicate that respondents gave very high ratings to aspects related to the role of artificial intelligence in helping to understand, clarify and process information.

The findings indicate that respondents’ attitudes towards artificial intelligence as a source of information are closely linked to its role in facilitating the process of understanding information. Artificial intelligence is not only used to obtain answers, but also to clarify the information received, assist in understanding the required information, and provide more detailed explanations. Within the framework of Functional Attitude Theory, these results align with the ‘knowledge’ function, where attitudes relate to an individual’s need to understand, interpret, and organise information. In the context of this study, the results indicate that artificial intelligence is positioned as a tool that helps respondents process information in a clearer and more easily understandable manner.

In terms of the categorical distribution, the results were as follows: strong (42.5%), followed by fairly well-developed (31.5%), weak (17.8%), and very weak (8.2%). In this function, there were no respondents in the very strong category. This pattern indicates that although, on average, the knowledge function falls into the very strong category, the level of development among respondents is predominantly in the strong to fairly developed range. Thus, the knowledge function appears to play a significant role in shaping respondents’ attitudes, although its strength is not yet fully consistent across all respondents.

The high level of the knowledge function indicates that respondents’ attitudes towards artificial intelligence as a source of information are more closely related to its role in helping to understand and clarify information. From the perspective of Functional Attitude Theory, this aligns with the knowledge function, where attitudes relate to an individual’s need to understand and organise information. Attitudes towards artificial intelligence can also be explained through the utility-knowledge dimension, which suggests that the use of this technology is not only about utility but also about its ability to help individuals understand information (Li & Wang, 2025). Furthermore, the use of artificial intelligence in various contexts also indicates that this technology is increasingly understood as a tool that helps individuals address information needs in various situations (Gnambs et al., 2025). Thus, artificial intelligence is understood by respondents primarily as a means to both obtain and understand information.

Overall, the research findings indicate that respondents’ attitudes towards the use of artificial intelligence as a source of information are formed to varying degrees. The utilitarian and knowledge functions fall into the ‘very strong’ category, the value-expressive function into the ‘strong’ category, whilst the ego-defensive function falls into the ‘moderately developed’ category. These findings indicate that respondents’ attitudes towards artificial intelligence as a source of information are more closely related to practical benefits and its role in helping to understand information, whilst risk considerations and value expression remain present, albeit at different levels. Furthermore, respondents’ total scores were grouped to examine their overall attitude functions towards the use of artificial intelligence as a source of information. The results of this grouping are presented in Table 7.

Table 7. Category Levels of Attitudinal Functioning Among Respondents

From the perspective of Functional Attitude Theory, attitudes are understood in terms of the psychological functions they serve for the individual. Therefore, the overall categorisation in this study is used to provide a general overview of the extent to which respondents’ attitudes towards artificial intelligence as a source of information have been formed. This categorisation is based on the results of four attitude functions—namely utilitarian, ego-defensive, value-expressive, and knowledge—thereby illustrating the general picture of respondents’ attitudes following the analysis of each function. Consequently, the categories ranging from very weak to very strong do not merely describe a single specific function but indicate the overall level of attitude formation among respondents.

According to Table 7, the responses were predominantly ‘moderate’ (50.68%), followed by ‘strong’ (26.03%), ‘weak’ (12.33%), ‘very weak’ (8.22%), and ‘very strong’ (2.74%). These findings indicate that, in general, the psychological functions underpinning respondents’ attitudes towards the use of artificial intelligence as a source of information range from moderate to strong. This indicates that respondents generally already possess a fairly clear attitude towards artificial intelligence, particularly regarding its practical benefits and its role in helping to understand information, although the strength of these attitudes is not yet entirely consistent. Consequently, the use of artificial intelligence as a source of information among respondents is no longer at the initial response stage but has shown the formation of a relatively clear attitude, although it has not yet fully reached the category of strong and stable.

Overall, respondents’ attitudes towards the use of artificial intelligence as a source of information were predominantly in the ‘fairly established’ category. This indicates that the psychological functions underpinning respondents’ attitudes have been established, although they are not yet entirely strong or consistent. Attitudes towards artificial intelligence can also be explained through a functional approach that places psychological needs at the foundation of understanding individuals’ attitudes towards this technology (Li & Wang, 2025). Thus, the results in Table 6 show that respondents’ attitudes towards artificial intelligence as a source of information have generally been formed, with utilitarian and knowledge functions appearing more prominent than value-expressive and ego-defensive functions.

Based on these criteria, the grouping of respondents according to their attitudes towards the use of artificial intelligence in the information-seeking process is shown in Table 7.

The attitude functions of members of the Indonesian AI Forum regarding the use of artificial intelligence as a source of information are formed to varying degrees. The utilitarian and knowledge functions fall into the ‘very strong’ category, the value-expressive function falls into the ‘strong’ category, whilst the ego-defensive function falls into the ‘moderately formed’ category. Overall, the majority of respondents’ attitude functions fall into the category of moderately developed. These findings indicate that respondents’ attitudes towards artificial intelligence as a source of information are more closely related to the practical benefits experienced directly, as well as its role in helping to understand and clarify information. Meanwhile, the value-expressive and ego-defensive functions remain present in the formation of attitudes, but are not as strong as the utilitarian and knowledge functions

Generally speaking, artificial intelligence is largely understood as a useful tool for meeting information needs whilst supporting the process of information comprehension. Nevertheless, the psychological functions underpinning attitudes towards artificial intelligence have not yet been fully and firmly established. This finding enriches the study of attitudes towards artificial intelligence through the Functional Attitude Theory, particularly in the context of utilising artificial intelligence as a source of information. Further research is recommended to be conducted on different respondent groups or within more specific contexts of artificial intelligence usage so that the picture regarding the functions of attitudes towards artificial intelligence can be understood more broadly and deeply.