1. Introduction
The aquaculture field is the fastest-growing industry among the food production fields [1] and might be the answer to increased food demand, which is a major rising issue due to climate change and surging expectations of high food quality [2], including seafood [3]. However, it is plagued with many challenges hindering its sustainable growth. Such challenges range from farm escapes—that may threaten the genetic integrity of wild populations [4], needing proper handling to ensure the protection of the environment—to farmed fish diseases [5], which may compromise a sustainable supply.
To better face such modern challenges, we need to employ new and state-of-the-art technologies. For example, a Geographical Information System (G.I.S.), when employed in the field of aquaculture, can greatly aid fish health decision-making by compiling, storing, retrieving, analyzing and displaying mapped spatial data for communicable fish diseases, providing significant potential for its management [6]. In the Mediterranean, such a tool is already being deployed with promising results [7]. Another good example is automating technologies, making use of computer vision and imaging techniques, deployed for various tasks like grading and sorting fish material [8], or for reliable fish counting [9]. Alternatively, new hardware, like transit measuring tunnels incorporated outside growth units, is used to estimate fish biomass through length–weight regressions [10].
The emerging technology of Artificial Intelligence (A.I.) can aid the above cause in innumerable ways, fish health management included. A.I. can be employed in a vast range of tasks, from enhancing the predictive precision from G.I.S.-based disease transmission software [11] to forming the foundation of intelligent systems used for the diagnosis—and subsequently the treatment—of farmed fish [12]. Great examples of A.I. deployment include fish identification, species classification, behavioral analysis [13], fish disease detection [14], feeding behavior identification [15], fish growth measurement (non-invasively) [16], stress state recognition [17], fish egg quality prediction [18], fish mortality detection and alert system [19], real-time monitoring and analysis of water quality [20], and more [21]. Consequentially, the use of A.I. has a major positive socioeconomic impact, since it leads to an increase in fish biomass quality along with an increase in economic sustainability of fish farming systems [22].
To highlight how significant fish health management is, roughly 50% of the overall production loss worldwide is due to the factor of fish disease [23], which also generates immense insurance claims for compensation [24]. A major part of the ongoing situation is due to the fact that most fish farmers simply lack the necessary expertise and experience to identify and treat fish diseases [25], making the use of A.I. in fish health management of paramount importance [26].
In general, Artificial Intelligence refers to the ability we have bestowed upon computers, enabling them to perform complex tasks commonly associated with intelligence [27]. Artificial Intelligence begun gathering more and more support, starting from 1955, in many fields—but most predominantly in medicine—because of the speed and precision that it offered on generating and processing digital data [28]. In recent years, the field of A.I. has experienced impressive development, mainly due to progress in Machine Learning (M.L.) and Deep Learning (D.L.) techniques, which, nurtured from the presence of large datasets—commonly called Big Data (B.D.)—have led to solutions that were previously unavailable using conventional methods. Nowadays, A.I. has become an important source of innovation, serving many purposes [29]. Its development is so dramatic that it has evolved into an integral part of everyday life, regardless of whether its users realize its presence or not [30]. Examples of everyday applications include Google search, Cortana, Alexa and Siri, product recommendation systems in e-shops, ChatGPT, and so on. In academia, numerous studies have illustrated the benefits of A.I. deployment in many scientific fields—medical ones included [31]—with most advocating for A.I. integration into both clinical practice and their curriculum [32].
Still, there is much confusion and controversy when it comes to A.I., both in the public and scientific community, regarding its potential risks and benefits. Many naysayers question the actual practical capabilities of A.I., some are worried about the possibility of A.I. surpassing human intelligence in the future [33], while others fear that A.I. will seriously threaten human jobs in the near future due to its ability to imitate the human process and task-handling abilities, thus replacing the human presence, especially where large amounts of data and work are involved [34]. In the medical field, there are concerns that A.I. will lead to physician deskilling [35] and tamper with the physician–patient relationship [36]. On the other hand, proponents believe that if A.I. is used wisely and appropriately, it will greatly improve patient care and ultimately prove beneficial [37]. As of now, integration of A.I. into medical curricula, with the purpose of better understanding the value of A.I. usage as well as how to maximize its benefits, is still in its infancy [38].
Surveys investigating students’ and professionals’ attitudes towards A.I. are just emerging [39,40]. For example, studies have detected a tendency among students to abstain from certain fields—like radiology—where A.I. is considered a promising future competitor to physicians’ work [41]. Other studies—though still few—have tried to investigate the impact A.I. will have on job insecurity among professionals [42]. Concerning work, it is anticipated that in the next 20 years, about 47% of the jobs, as they exist today, in the U.S., will be at great risk because of the surging computerized and automated systems, resulting in humans competing with A.I. in a large range of cognition-based tasks [43].
Given the above, it is clear that A.I. can potentially revolutionize aquaculture by providing improvements to fish health management practices. However, the successful integration of A.I. in aquaculture depends significantly on the perceptions of this field’s professionals. However, to the best of the authors’ knowledge, no study has ever tested the perception and attitude of aquaculture professionals in the field of fish health management, of any community, towards the usage of A.I. and its applications. Thus, our aim was to understand if variables like age, gender, and education influence their views and attitudes, as well as to enrich the knowledge pool concerning the area of the Mediterranean. We also wanted to determine their understanding and perception of A.I. algorithms and their applications, as well as assess their level of confidence in working side by side with this technology. As such, this study hypothesizes that professionals’ knowledge of A.I. significantly influences their perceptions of this technology and its potential in enhancing fish health management. Through this investigation, we aim to determine whether familiarity with A.I. correlates with more favorable attitudes and increased willingness to adopt A.I. solutions in the aquaculture industry. We believe that this information will prove crucial during the process of decision-making, regarding the future of this field. To this end, we chose the attendees of a major farm fish health conference to be the source of our primary data, in order to acquire not only academia-based input, but mainly input from professionals working on this field, representing companies responsible for more than 70% of the Greek domestic production, thus ensuring a strong and unquestionable representation of the economic sector of this field as well. A questionnaire was deployed, following the quantitative approach, consisting of six modules, aiming to capture demographic information, A.I. knowledge, interest in A.I. usage, perceptions of A.I. potentials, perceptions on how A.I. will impact professional development, and lastly emotional response. As such, this study aspires to shed some first light on the subject, help in the cause of developing strategies for A.I. implementation and briefing, aid in education and training, and become the pilot for more relevant studies.
2. Materials and Methods
2.1. Formulated Hypotheses
Table 1 shows the 12 hypotheses that were formulated beforehand to be investigated.
2.2. Study Participants
This study followed the descriptive analytical method and was based on the quantitative approach to collecting primary data. The study was conducted with 73 participants attending a major conference on Mediterranean fish health management, held on 25 May 2023, at the University of Patras in Messolonghi, Greece. The conference (European Maritime Day—
All procedures were performed according to the Helsinki declaration (1964). Participating in the survey was entirely voluntary, and the data collection process was designed to be completely anonymous. Participants were informed about the purpose of the questionnaire both verbally and in the preface section. Since the study did not involve any risk of physical or psychological impact, supervision from an Ethical Review Board was not necessary [44].
2.3. Survey
The digital questionnaire was designed using Google Forms (Google LLC, Mountain View, CA, USA), using well-established methodology [45,46] (Appendix A.1). The survey was divided into six modules and 25 closed-ended questions, following the paradigm of similar works [47,48,49,50], and employed multiple-choice and Likert-scale questions. Likert questions had a 5-point scale, ranging either from “strongly agree” to “strongly disagree”, or from “none” to “very much”, and were used to investigate participants’ agreement with statements across several domains.
The aim of the first module was to collect demographical data, such as age, gender and education level. The second aimed to assess the participants’ knowledge and understanding regarding A.I. as a technology. The third focused on the participants’ interest in A.I. usage. The fourth explored the participants’ perspectives regarding the potential capabilities of A.I. The fifth examined the participants’ perceptions regarding how A.I. will affect their professional field and future. The sixth and final module recorded the participants’ emotional response towards A.I.
The validation process of the questionnaire was conducted by presenting it to a team of aquaculture and A.I. experts and circulating it to a test sample of employees that were not to participate in the sample population. The aim was to reduce bias and increase internal validity [51], while extensively considered questions aided in accurately encoding the survey, guarding against miscommunication [52,53]. The questions were thoroughly formulated to be as direct, short, clear, and to the point, as possible, in order to lead to quantifiable and generalizable responses [54].
The decided protocol of inclusion was that questionnaires with individual unanswered questions were to be included in the analysis, while questionnaires with entire modules of questions unanswered were not to be included in the analysis. After assessment, all questionnaires collected were included in the analysis.
2.4. Processing
After collection, data were categorized and coded using Microsoft Excel (Microsoft Corporation, Redmond, Washington, USA) and then presented and analyzed using Statistical Package for the Social Sciences (SPSS V28, IBM Corporation, Armonk, NY, USA). To be considered statistically significant, the p-value needed to be less than 0.05. The internal reliability of the survey was measured by calculating Cronbach’s alpha. Correlations between variables were calculated, statistical differences between groups were counted, and t-tests were performed to answer the formulated hypotheses.
3. Results
This section contains the end results of processing. It starts with data presentation, followed by descriptive statistics and ending with statistical analysis.
3.1. Module 1: Demographics
A total of 39 men and 34 women participated in the research. In terms of age, the biggest group was between 18 and 25 years old (47.9%), while the second largest age group was between 46 and 55 years old (20.5%). Regarding education level, the majority (60.3%) hold a bachelor’s degree (level 6), while 30.1% also hold a master’s degree (level 7). Additionally, the vast majority (60.3%) are graduates of Fisheries and Aquaculture departments, followed by Agriculture departments (15.1%). In total, 58.9% stated that they specialize in ichthyology (Table 2 and Figure 1).
3.2. Module 2: Knowledge and Understanding
Internet search was the most popular way to obtain information on A.I., followed by the academic/professional environment. A total of 38.4% rated their knowledge of A.I. as low, while only 5.5% characterized their knowledge as high, and none rated it as very high. Additionally, 41.1% stated that they do not know the terms “machine learning” and “deep learning” at all, while 32.9% stated that they do know them but not the difference between them. Regarding the distinction of what constitutes an A.I. application and what does not, 49.3% correctly distinguished 3 out of 5 applications, while no one managed to distinguish them all (read the questionnaire for the applications, Appendix A.1). Furthermore, 87.7% have never been involved in A.I. application development (Table 3 and Figure 2).
3.3. Module 3: Interest in A.I. Usage
A total of 67.1% stated that they were interested in learning about A.I., compared to 12.3% who stated that they were not very interested. Additionally, 38.3% stated that they were interested in using A.I. applications in daily life, while 21.9% stated that they are not very interested. Regarding professional use, 64.4% stated that they are interested in using A.I. applications, while 16.5% stated that they are not very interested (Table 4).
3.4. Module 4: A.I. Potential
In total, 27.3% said they would trust an A.I.’s advice, while 30.2% said they would not. Additionally, 90.4% said they would trust an expert’s advice more than an A.I.’s for some serious matter. A total of 53.5% stated that they believe A.I. will prove to be useful in their professional field, compared to 15.1% who believe that it will not. Regarding the timeline, 41% believe that A.I. will be used extensively in their professional field within the next 5 years, while 57.4% believe that this will happen in more than 5 years. Furthermore, 43.8% believe that A.I. will bring a “revolution” to their professional field, while 19.1% believe that it will not. Finally, 60.3% believe that their curricula should be enriched with A.I. courses, while 16.5% believe they should not (Table 5).
3.5. Module 5: A.I. Effect on Profession
A total of 74% do not believe that A.I. will replace them in their professional field, compared to 23.3% who believe that they will be replaced. Additionally, 52% consider A.I. a partner, compared to 15.1% who consider it a competitor. Meanwhile, 31.5% are eager to witness the contribution of A.I. in their professional field, compared to 27.4% who are not. Regarding professional knowledge, 53.4% believe that A.I. will contribute to it, compared to 16.4% who consider that it will not. Lastly, 49.3% believe that A.I. will contribute to their professional progress, while 12.3% consider that it will not (Table 6).
3.6. Module 6: Emotional Response
The entry most picked, regarding feelings towards A.I., was “interest”, followed by “anguish” and “insecurity”. There were 98 positive entries, compared to 42 negative ones (Table 7 and Figure 3).
3.7. Cronbach’s Alpha
Cronbach’s alpha had an adequate value (>0.7), both in every module and altogether, resulting in an internally consistent questionnaire (Table 8).
3.8. Descriptive Statistics
Table 9 and Table 10 present the descriptive statistics of the Likert-scale questions, ranging from 1 (very low) to 5 (very high). Table 11 shows that the p-values of all variables are clearly below the threshold of 0.05, indicating that their data are not normally distributed. Consequently, to analyze the correlations between those variables we need to use a non-parametric method. To this end, we use the Spearman correlation coefficient to assess the linear relationship between variables (Table 12, Table 13 and Table 14).
3.9. Correlations
Calculating Spearman’s correlation coefficients, using the formula taking into account rank ties as well, revealed what we consider as mild (>0.3 and <0.5) (Table 12), moderate (>0.5 and <0.7) (Table 13) and strong (>0.7) (Table 14) correlations between variables, which all had an accepted p-value. See Figure 4.
3.10. Significant Differences
Table 15, Table 16, Table 17 and Table 18 illustrate the statistically significant differences between Likert-scale questions among the examined demographical categories of gender, age groups, education level and specialty. To achieve better group size balance, age was divided into two groups: 18–25 (as is) and 26–65 (all other groups combined). The same approach was followed for education level, with one group being bachelor (as is) and the other being master and doctorate (the other two groups combined). Since data are not normally distributed, hence they require a nonparametric approach, and since there are cases where the number of available groups is more than 2, we employed the Kruskal–Wallis’s test as the most appropriate, accepting entries with a p-value of less than 0.05.
As depicted, viewing A.I. as a partner rather than a competitor and being interested in being briefed about A.I. had the highest statistical difference for gender. For age groups, most variables had high and comparable scores, with interest in being briefed about A.I. being the highest again. For education level, integration into curricula had the highest statistical difference, followed by interest in being briefed about A.I. and A.I. contribution to knowledge. Lastly, for specialty, A.I. contribution to development, followed by viewing A.I. as a partner rather than a competitor and A.I. contribution to knowledge, had the highest statistical difference.
3.11. Hypotheses Answers
T-tests were performed to test the formulated hypotheses (Table 19, Table 20 and Table 21). Bonferroni’s corrections have been employed (leading to greater/stricter p-values), in order to guard against type I errors.
Interest in being briefed about A.I. is greater among males than females.
Levene: p = 0.224 > 0.05, t-test: p = 0.026 < 0.05, therefore it is valid.
Trust, in A.I. estimates, is greater among males than females.
Levene: p = 0.278 > 0.05, t-test: p = 0.118 > 0.05, therefore it is discarded.
Viewing A.I. as a partner rather than a competitor is greater among males than females.
Levene: p = 0.664 > 0.05, t-test: p = 0.016 < 0.05, therefore it is valid.
Believing that A.I. will contribute to professional development is greater among males than females.
Levene: p = 0.021 < 0.05, t-test: p = 0.184 > 0.05, therefore it is discarded.
Interest in being briefed about A.I. is greater among younger professionals than their older colleagues.
Levene: p = 0.330 > 0.05, t-test: p = 0.008 < 0.05, therefore it is valid.
Trust, in A.I. estimates, is greater among younger professionals than their older colleagues.
Levene: p = 0.900 > 0.05, t-test: p = 0.022 < 0.05, therefore it is valid.
Viewing A.I. as a partner rather than a competitor is greater among younger professionals than their older colleagues.
Levene: p = 0.572 > 0.05, t-test: p = 0.020 < 0.05, therefore it is valid.
Believing that A.I. will contribute to professional development is greater among younger professionals than their older colleagues.
Levene: p = 0.735 > 0.05, t-test: p = 0.014 < 0.05, therefore it is valid.
Interest in being briefed about A.I. is greater among professionals with a higher education level.
Levene: p = 0.751 > 0.05, t-test: p = 0.420 > 0.05, therefore it is discarded.
Trust, in A.I. estimates, is greater among professionals with a higher education level.
Levene: p = 0.129 > 0.05, t-test: p = 0.918 > 0.05, therefore it is discarded.
Viewing A.I. as a partner rather than a competitor is greater among professionals with a higher education level.
Levene: p = 0.212 > 0.05, t-test: p = 1.323 > 0.05, therefore it is discarded.
Believing that A.I. will contribute to professional development is greater among professionals with a higher education level.
Levene: p = 0.354 > 0.05, t-test: p = 0.759 > 0.05, therefore it is discarded.
4. Discussion
This research aimed to record and examine aquaculture professionals’ perspectives on, attitudes towards and understanding of the field of fish health management, focusing on Mediterranean farmed fish, regarding A.I. Part of the goal was also to shed light on the factors that affect its adoption.
The respondents’ gender was found to affect their interest in being briefed about A.I. and whether they view A.I. more as a partner than a competitor. Meanwhile, age was found to affect both factors, as well as trust in A.I. estimates and belief in A.I.’s contribution to professional development. This finding agrees with our systematic literature review regarding factors that affect adoption [55]. Specifically, age has been found to affect adoption, with younger ages being more interested and positive both on learning and using new technologies, than older ones [56]. Interestingly, in our research education level did not appear to affect any of these factors despite having been found to affect adoption positively in others [57,58,59,60].
It is noteworthy that 64.4% stated that they know nothing or almost nothing about A.I., while approximately the same percentage, 67.1%, stated that they are either interested or very interested in receiving a briefing on this technology. Most participants, 75.2%, believe that A.I. will be widely used in the field in the next 10 years, which proves the importance of research on this subject, given that 43.8% believe that it will literally revolutionize it, in comparison with 19.1% that do not believe it will. This is further demonstrated by the majority’s opinion, 60.3%, stating that A.I. should be included in curricula, against 16.5%, who do not acknowledge a need for that. The majority, 52%, believe A.I. to be more of a partner than a competitor, with similar percentages having the opinion that A.I. will help them expand their professional knowledge, 53.4%, and contribute to their professional progress and development, 49.3%. The way professionals view A.I. is also illustrated through their feelings towards it, with 98 being positive and 42 being negative, roughly a 2:1 ratio.
A mild positive correlation was found between the respondent’s stated knowledge on the topic and their interest in being briefed about A.I., as well as its usage—concerning both their everyday life and their professional field. Additionally, there was also a mild positive correlation with their Trust in A.I.’s assessments and their eagerness to see its contribution professionally. Those findings agree with the literature. Namely, the more information is available to a stakeholder [61], and the more familiar the stakeholder is with said technology [62], the greater the chance of adoption [55].
Furthermore, a moderate positive correlation was found between the belief that A.I. will revolutionize the respondent’s professional field and being supportive in favor of integrating relevant courses into curricula, as well as eagerness to see the contribution of A.I. in the professional field. Those findings are also in agreement with the literature—namely, good expectations of performance of a new technology [63], belief that said technology will improve work efficiency and achieve professional goals [64], and belief that said technology will prove useful [65] and operate as intended [66] greatly enhances its adoption [55].
Additionally, a strong positive correlation was found between the interest in being briefed about A.I. and the interest in using A.I. both in everyday life and professionally. There was also a strong positive correlation with trust in A.I.’s estimates, being supportive in favor of integrating relevant courses into curricula, viewing A.I. as more of a partner than a competitor, eagerness to see its contribution professionally, and conviction that A.I. will contribute both to the professional’s knowledge and progress.
After considering the above, an interesting insight emerges regarding the target population: An individual who is interested in using A.I. in everyday life will be very likely to be interested in using it in their professional field as well, and vice versa. Additionally, they will be more likely to trust A.I. estimates and consider A.I. more as a partner than a competitor. At the same time, daily and professional usage was found to be correlated with interest in being briefed about this technology, which in turn was found to be correlated with the respondent’s stated knowledge on the topic, which further fuels their interest in being briefed about this technology. Consequently, thoroughly informing the target group about this technology, alone, triggers subsequent interest and desire to employ this technology. An online briefing would be suitable, given that the Internet was the most popular chosen source of information, and potentially combining it with the usage of social media, as well.
It is also notable that farm managers are not afraid at all (0%) of being replaced in the future by A.I. in their professional field—the percentage for the whole sample was 23.3%.
4.1. Research Limitations
The first limitation is that the study concerns the Greek aquaculture community. The second limitation is that A.I. is a brand-new technology and most professionals have not used it, nor know much about it, in order to have personal experience to express, which is crucial, since stakeholders witnessing the operation and end results of a new technology have been found to have a much greater chance of accepting it and adopting it [67]. Thus, the same research should be repeated after proper training and acclimatization, along with a test of causality to check the cause-and-effect relationship between the detected correlations. Low experience at this point is to be expected since new technologies have been found to be adopted progressively, beginning with the most innovative members of a certain field, and then gradually by more, encouraged by peer-influence as well [55].
4.2. Practical Applications
The authors have provided insights that could potentially guide strategic and informed decision-making in the field of aquaculture by stakeholders. For example, such findings could be used to attract the attention and interest of aquaculture professionals, companies, organizations and communities. Furthermore, the findings of the current paper can be used as the basis to guide and strengthen future research on the topic while also enriching the current literature, which is extremely limited.
Conceptualization, J.A.T.; methodology, D.C.G., V.P.G. and J.A.T.; software, V.P.G.; validation, D.C.G.; formal analysis, V.P.G.; investigation, V.P.G.; resources, J.A.T. and D.C.G.; data curation, V.P.G.; writing―original draft preparation, V.P.G.; writing―review and editing, V.P.G., D.C.G. and J.A.T.; visualization, V.P.G.; supervision, D.C.G. and J.A.T.; project administration, J.A.T.; funding acquisition, J.A.T. All authors have read and agreed to the published version of the manuscript.
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions from the funding program.
The publication fees of this manuscript have been financed by the Research Council of the University of Patras.
The authors declare no conflicts of interest.
Footnotes
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Hypotheses.
| No. | Hypothesis |
|---|---|
| H1 | Interest in being briefed about A.I. is greater among males than females. |
| H2 | Trust, in A.I. estimates, is greater among males than females. |
| H3 | Viewing A.I. as a partner rather than a competitor is greater among males than females. |
| H4 | Believing that A.I. will contribute to professional development is greater among males than females. |
| H5 | Interest in being briefed about A.I. is greater among younger professionals than their older colleagues. |
| H6 | Trust, in A.I. estimates, is greater among younger professionals than their older colleagues. |
| H7 | Viewing A.I. as a partner rather than a competitor is greater among younger professionals than their older colleagues. |
| H8 | Believing that A.I. will contribute to professional development is greater among younger professionals than their older colleagues. |
| H9 | Interest in being briefed about A.I. is greater among professionals with a higher education level. |
| H10 | Trust, in A.I. estimates, is greater among professionals with a higher education level. |
| H11 | Viewing A.I. as a partner rather than a competitor is greater among professionals with a higher education level. |
| H12 | Believing that A.I. will contribute to professional development is greater among professionals with a higher education level. |
Module 1: Demographics.
| Variable | Choices | Frequency | Percent |
|---|---|---|---|
| Gender | Male | 39 | 53.4 |
| Female | 34 | 46.6 | |
| Total | 73 | 100.0 | |
| Age Groups | 18–25 | 35 | 47.9 |
| 26–35 | 8 | 11.0 | |
| 36–45 | 10 | 13.7 | |
| 46–55 | 15 | 20.5 | |
| 56–65 | 5 | 6.8 | |
| Total | 73 | 100.0 | |
| Education Level | Bachelor | 44 | 60.3 |
| Master | 22 | 30.1 | |
| Doctorate | 5 | 6.8 | |
| Unanswered | 2 | 2.7 | |
| Total | 73 | 100.0 | |
| Undergraduate Studies | Biology | 5 | 6.8 |
| Veterinary | 2 | 2.7 | |
| Agriculture | 11 | 15.1 | |
| Aquaculture | 44 | 60.3 | |
| AnimalScience | 4 | 5.5 | |
| Other | 3 | 4.1 | |
| Unanswered | 4 | 5.5 | |
| Total | 73 | 100.0 | |
| Specialty | Ichthyologist | 43 | 58.9 |
| Ichthyopathologist | 7 | 9.6 | |
| FarmManager | 17 | 23.3 | |
| Unanswered | 6 | 8.2 | |
| Total | 73 | 100.0 |
Module 2: Knowledge and understanding.
| Variable | Choices | Frequency | Percent |
|---|---|---|---|
| Rate your knowledge on the subject of A.I.? | None | 19 | 26.0 |
| Low | 28 | 38.4 | |
| Moderate | 21 | 28.8 | |
| High | 4 | 5.5 | |
| Very High | 0 | 0.0 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| How do you learn and stay informed about the subject of A.I.? | Media | 10 | |
| Social Media | 18 | ||
| Academic/Professional | 21 | ||
| Social Environment | 12 | ||
| Internet Search | 38 | ||
| Do you know the terms “Machine Learning” and “Deep Learning”? | I don’t know any of the terms | 30 | 41.1 |
| I only know one term | 10 | 13.7 | |
| I know both but not the difference | 24 | 32.9 | |
| I know both and the difference | 9 | 12.3 | |
| Total | 73 | 100.0 | |
| Correct answers to A.I. related questions (To determine actual knowledge) | 0 | 10 | 13.7 |
| 1 | 12 | 16.4 | |
| 2 | 9 | 12.3 | |
| 3 | 36 | 49.3 | |
| 4 | 6 | 8.2 | |
| 5 | 0 | 0.0 | |
| Total | 73 | 100.0 | |
| Have you ever been involved in the development of A.I. applications? | No | 64 | 87.7 |
| Yes | 9 | 12.3 | |
| Total | 73 | 100.0 |
Module 3: Interest in A.I. usage.
| Variable | Choices | Frequency | Percent |
|---|---|---|---|
| How interested are you in learning about the subject of A.I.? | None | 3 | 4.1 |
| Low | 6 | 8.2 | |
| Moderate | 15 | 20.5 | |
| High | 20 | 27.4 | |
| Very High | 29 | 39.7 | |
| Total | 73 | 100.0 | |
| How interested are you in the use of A.I. applications in your everyday life? | None | 4 | 5.5 |
| Low | 12 | 16.4 | |
| Moderate | 28 | 38.4 | |
| High | 12 | 16.4 | |
| Very High | 16 | 21.9 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| How interested are you in the use of A.I. applications in your work? | None | 4 | 5.5 |
| Low | 8 | 11.0 | |
| Moderate | 13 | 17.8 | |
| High | 28 | 38.4 | |
| Very High | 19 | 26.0 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 |
Module 4: A.I. potential.
| Variable | Choices | Frequency | Percent |
|---|---|---|---|
| Would you trust an A.I. application advice on a serious matter? | Strongly Disagree | 4 | 5.5 |
| Disagree | 18 | 24.7 | |
| Neither | 30 | 41.1 | |
| Agree | 15 | 20.5 | |
| Strongly Agree | 5 | 6.8 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| Which source of information would you trust more on a serious matter? | A.I. | 3 | 4.1 |
| Human Specialist | 66 | 90.4 | |
| Neither | 2 | 2.7 | |
| Unanswered | 2 | 2.7 | |
| Total | 73 | 100.0 | |
| How useful do you think the use of A.I. will prove to be in your professional field? | None | 3 | 4.1 |
| Low | 8 | 11.0 | |
| Moderate | 21 | 28.8 | |
| High | 28 | 38.4 | |
| Very High | 11 | 15.1 | |
| Unanswered | 2 | 2.7 | |
| Total | 71 | 100.0 | |
| In how many years do you think A.I. applications will be widely used in your field? | Already being used | 12 | 16.4 |
| Next 2 | 5 | 6.8 | |
| Next 5 | 13 | 17.8 | |
| Next 10 | 25 | 34.2 | |
| Next 15 | 12 | 16.4 | |
| More than 15 | 5 | 6.8 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| A.I. will revolutionize my field | Strongly Disagree | 2 | 2.7 |
| Disagree | 12 | 16.4 | |
| Neither | 26 | 35.6 | |
| Agree | 20 | 27.4 | |
| Strongly Agree | 12 | 16.4 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| A.I. training should be included in my undergraduate curricula | Strongly Disagree | 1 | 1.4 |
| Disagree | 11 | 15.1 | |
| Neither | 16 | 21.9 | |
| Agree | 28 | 38.4 | |
| Strongly Agree | 16 | 21.9 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 |
Module 5: A.I. effect on profession.
| Variable | Choices | Frequency | Percent |
|---|---|---|---|
| Do you think that A.I. will replace you in your professional field? | No | 54 | 74.0 |
| Yes | 17 | 23.3 | |
| Unanswered | 2 | 2.7 | |
| Total | 73 | 100.0 | |
| I view A.I. as a partner than a competitor | Strongly Disagree | 3 | 4.1 |
| Disagree | 8 | 11.0 | |
| Neither | 23 | 31.5 | |
| Agree | 19 | 26.0 | |
| Strongly Agree | 19 | 26.0 | |
| Unanswered | 1 | 1.4 | |
| Total | 73 | 100.0 | |
| I’m eager for the contribution of A.I. in my professional field | Strongly Disagree | 2 | 2.7 |
| Disagree | 18 | 24.7 | |
| Neither | 30 | 41.1 | |
| Agree | 12 | 16.4 | |
| Strongly Agree | 11 | 15.1 | |
| Total | 73 | 100.0 | |
| A.I. will help me expand my professional knowledge | Strongly Disagree | 2 | 2.7 |
| Disagree | 10 | 13.7 | |
| Neither | 22 | 30.1 | |
| Agree | 23 | 31.5 | |
| Strongly Agree | 16 | 21.9 | |
| Total | 73 | 100.0 | |
| A.I. will contribute to my professional progress and development | Strongly Disagree | 0 | 0.0 |
| Disagree | 9 | 12.3 | |
| Neither | 28 | 38.4 | |
| Agree | 19 | 26.0 | |
| Strongly Agree | 17 | 23.3 | |
| Total | 73 | 100.0 |
Module 6: Emotional response.
| Variable | Choices | Frequency |
|---|---|---|
| How does A.I. make you feel? | Security | 9 |
| Interest | 62 | |
| Joy | 13 | |
| Other, positive | 14 | |
| Anguish | 18 | |
| Fear | 2 | |
| Insecurity | 18 | |
| Other, negative | 4 |
Cronbach’s Alpha values per module.
| Module | Cronbach’s Alpha |
|---|---|
| 3 | 0.875 |
| 4 | 0.784 |
| 5 | 0.883 |
| Altogether | 0.925 |
Descriptive statistics.
| Variable | Mean | Median | Mode | Std. Dev. | Min | Max |
|---|---|---|---|---|---|---|
| Rate your A.I. knowledge | 2.14 | 2.00 | 2 | 0.877 | 1 | 4 |
| Correct answers regarding A.I. | 2.22 | 3.00 | 3 | 1.228 | 0 | 4 |
| Interest in being briefed about A.I. | 3.90 | 4.00 | 5 | 1.145 | 1 | 5 |
| Interest in using A.I. in everyday life | 3.33 | 3.00 | 3 | 1.163 | 1 | 5 |
| Interest in using A.I. professionally | 3.69 | 4.00 | 4 | 1.146 | 1 | 5 |
| Trust in A.I. estimates | 2.99 | 3.00 | 3 | 0.986 | 1 | 5 |
| A.I. professional usefulness | 3.51 | 4.00 | 4 | 1.026 | 1 | 5 |
| Revolutionize profession | 3.39 | 3.00 | 3 | 1.042 | 1 | 5 |
| Integration into curricula | 3.65 | 4.00 | 4 | 1.037 | 1 | 5 |
| A.I.: partner than competitor | 3.60 | 4.00 | 3 | 1.22 | 1 | 5 |
| Eagerness for A.I. contribution | 3.16 | 3.00 | 3 | 1.054 | 1 | 5 |
| A.I. contribution to knowledge | 3.56 | 4.00 | 4 | 1.067 | 1 | 5 |
| A.I. contribution to development | 3.60 | 3.00 | 3 | 0.982 | 2 | 5 |
Descriptive statistics.
| Variable | Perc 25% | Perc 50% | Perc 75% |
|---|---|---|---|
| Rate your A.I. knowledge | 1.00 | 2.00 | 3.00 |
| Correct answers regarding A.I. | 1.00 | 3.00 | 3.00 |
| Interest in being briefed about A.I. | 3.00 | 4.00 | 5.00 |
| Interest in using A.I. in everyday life | 3.00 | 3.00 | 4.00 |
| Interest in using A.I. professionally | 3.00 | 4.00 | 5.00 |
| Trust in A.I. estimates | 2.00 | 3.00 | 4.00 |
| A.I. professional usefulness | 3.00 | 4.00 | 4.00 |
| Revolutionize profession | 3.00 | 3.00 | 4.00 |
| Integration into curricula | 3.00 | 4.00 | 4.00 |
| A.I.: partner than competitor | 3.00 | 4.00 | 5.00 |
| Eagerness for A.I. contribution | 2.00 | 3.00 | 4.00 |
| A.I. contribution to knowledge | 3.00 | 4.00 | 4.00 |
| A.I. contribution to development | 3.00 | 3.00 | 4.00 |
Shapiro–Wilk test for normality.
| Variable | Statistic | df | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | 0.862 | 70 | <0.001 |
| Correct answers regarding A.I. | 0.829 | 70 | <0.001 |
| Interest in being briefed about A.I. | 0.843 | 70 | <0.001 |
| Interest in using A.I. in everyday life | 0.890 | 70 | <0.001 |
| Interest in using A.I. professionally | 0.867 | 70 | <0.001 |
| Trust in A.I. estimates | 0.907 | 70 | <0.001 |
| A.I. professional usefulness | 0.892 | 70 | <0.001 |
| Revolutionize profession | 0.905 | 70 | <0.001 |
| Integration into curricula | 0.881 | 70 | <0.001 |
| A.I.: partner than competitor | 0.886 | 70 | <0.001 |
| Eagerness for A.I. contribution | 0.890 | 70 | <0.001 |
| A.I. contribution to knowledge | 0.898 | 70 | <0.001 |
| A.I. contribution to development | 0.871 | 70 | <0.001 |
Mild correlations.
| Variable | Correlated Variables | Spearman’s Coefficient | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | Difference between M.L. and D.L. | 0.408 | <0.001 |
| Interest in being briefed about A.I. | 0.387 | <0.001 | |
| Interest in using A.I. in everyday life | 0.433 | <0.001 | |
| Interest in using A.I. professionally | 0.303 | 0.010 | |
| Trust in A.I. estimates | 0.347 | 0.003 | |
| Eagerness for A.I. contribution | 0.317 | 0.007 | |
| Interest in being briefed about A.I. | Trust in A.I. estimates | 0.416 | <0.001 |
| A.I.: partner than competitor | 0.397 | 0.001 | |
| A.I. contribution to development | 0.415 | <0.001 | |
| Interest in using A.I. in everyday life | Revolutionize profession | 0.404 | <0.001 |
| Integration into curricula | 0.494 | <0.001 | |
| A.I.: partner than competitor | 0.311 | 0.008 | |
| Interest in using A.I. professionally | Revolutionize profession | 0.447 | <0.001 |
| A.I.: partner than competitor | 0.393 | 0.001 | |
| Trust in A.I. estimates | A.I. professional usefulness | 0.383 | 0.001 |
| Revolutionize profession | 0.330 | 0.005 | |
| Integration into curricula | 0.431 | <0.001 | |
| A.I. contribution to knowledge | 0.485 | <0.001 | |
| Difference between M.L. and D.L. | Eagerness for A.I. contribution | 0.386 | 0.001 |
| Correct answers in distinguishing A.I. applications | A.I. contribution to knowledge | 0.308 | 0.008 |
| A.I. contribution to development | 0.343 | 0.003 | |
| A.I. professional usefulness | A.I.: partner than competitor | 0.409 | <0.001 |
| A.I. contribution to knowledge | 0.476 | <0.001 | |
| A.I. contribution to development | 0.485 | <0.001 | |
| Revolutionize profession | Eagerness for A.I. contribution | 0.450 | <0.001 |
| A.I. contribution to development | 0.369 | 0.001 | |
| Integration into curricula | A.I. contribution to knowledge | 0.375 | 0.001 |
| A.I. contribution to development | 0.452 | <0.001 |
Moderate correlations.
| Variable | Correlated Variables | Spearman’s Coefficient | p-Value |
|---|---|---|---|
| Interest in being briefed about A.I. | Interest in using A.I. in everyday life | 0.645 | <0.001 |
| Interest in using A.I. professionally | 0.644 | <0.001 | |
| A.I. professional usefulness | 0.526 | <0.001 | |
| Integration into curricula | 0.637 | <0.001 | |
| Eagerness for A.I. contribution | 0.552 | <0.001 | |
| A.I. contribution to knowledge | 0.541 | <0.001 | |
| Interest in using A.I. in everyday life | Trust in A.I. estimates | 0.600 | <0.001 |
| A.I. professional usefulness | 0.661 | <0.001 | |
| Eagerness for A.I. contribution | 0.591 | <0.001 | |
| A.I. contribution to knowledge | 0.535 | <0.001 | |
| A.I. contribution to development | 0.514 | <0.001 | |
| Interest in using A.I. professionally | Trust in A.I. estimates | 0.506 | <0.001 |
| A.I. professional usefulness | 0.581 | <0.001 | |
| Integration into curricula | 0.596 | <0.001 | |
| A.I. contribution to knowledge | 0.536 | <0.001 | |
| A.I. contribution to development | 0.600 | <0.001 | |
| A.I. professional usefulness | Revolutionize profession | 0.543 | <0.001 |
| Integration into curricula | 0.521 | <0.001 | |
| Eagerness for A.I. contribution | 0.510 | <0.001 | |
| Trust in A.I. estimates | Eagerness for A.I. contribution | 0.536 | <0.001 |
| A.I. contribution to development | 0.531 | <0.001 | |
| Revolutionize profession | Integration into curricula | 0.519 | <0.001 |
| Integration into curricula | Eagerness for A.I. contribution | 0.534 | <0.001 |
| A.I.: partner than competitor | Eagerness for A.I. contribution | 0.537 | <0.001 |
| A.I. contribution to development | 0.567 | <0.001 |
Strong correlations.
| Variable | Correlated Variables | Spearman’s Coefficient | p-Value |
|---|---|---|---|
| Interest in using A.I. in everyday life | Interest in using A.I. professionally | 0.744 | <0.001 |
| Interest in using A.I. professionally | Eagerness for A.I. contribution | 0.720 | <0.001 |
| Eagerness for A.I. contribution | A.I. contribution to knowledge | 0.707 | <0.001 |
| A.I. contribution to development | 0.789 | <0.001 | |
| A.I. contribution to knowledge | A.I. contribution to development | 0.740 | <0.001 |
Kruskal–Wallis’ test for gender.
| Variable | Kruskal–Wallis | df | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | 0.617 | 1 | 0.432 |
| Correct answers regarding A.I. | 1.669 | 1 | 0.196 |
| Interest in being briefed about A.I. | 4.967 | 1 | 0.026 |
| Interest in using A.I. in everyday life | 0.067 | 1 | 0.796 |
| Interest in using A.I. professionally | 0.142 | 1 | 0.706 |
| Trust in A.I. estimates | 3.452 | 1 | 0.063 |
| A.I. professional usefulness | 0.365 | 1 | 0.546 |
| Revolutionize profession | 0.027 | 1 | 0.869 |
| Integration into curricula | 0.390 | 1 | 0.532 |
| A.I.: partner than competitor | 6.387 | 1 | 0.011 |
| Eagerness for A.I. contribution | 0.996 | 1 | 0.318 |
| A.I. contribution to knowledge | 1.438 | 1 | 0.230 |
| A.I. contribution to development | 1.728 | 1 | 0.189 |
Kruskal–Wallis’ test for age group.
| Variable | Kruskal–Wallis | df | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | 1.588 | 1 | 0.208 |
| Correct answers regarding A.I. | 4.715 | 1 | 0.030 |
| Interest in being briefed about A.I. | 7.514 | 1 | 0.006 |
| Interest in using A.I. in everyday life | 4.367 | 1 | 0.037 |
| Interest in using A.I. professionally | 6.589 | 1 | 0.010 |
| Trust in A.I. estimates | 4.997 | 1 | 0.025 |
| A.I. professional usefulness | 0.123 | 1 | 0.726 |
| Revolutionize profession | 0.859 | 1 | 0.354 |
| Integration into curricula | 5.461 | 1 | 0.019 |
| A.I.: partner than competitor | 6.521 | 1 | 0.011 |
| Eagerness for A.I. contribution | 5.318 | 1 | 0.021 |
| A.I. contribution to knowledge | 6.156 | 1 | 0.013 |
| A.I. contribution to development | 5.983 | 1 | 0.014 |
Kruskal–Wallis’ test for education level.
| Variable | Kruskal–Wallis | df | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | 0.027 | 1 | 0.869 |
| Correct answers regarding A.I. | 0.078 | 1 | 0.780 |
| Interest in being briefed about A.I. | 1.674 | 1 | 0.196 |
| Interest in using A.I. in everyday life | 0.109 | 1 | 0.741 |
| Interest in using A.I. professionally | 0.349 | 1 | 0.555 |
| Trust in A.I. estimates | 0.472 | 1 | 0.492 |
| A.I. professional usefulness | 0.000 | 1 | 0.990 |
| Revolutionize profession | 0.251 | 1 | 0.617 |
| Integration into curricula | 5.737 | 1 | 0.017 |
| A.I.: partner than competitor | 0.224 | 1 | 0.636 |
| Eagerness for A.I. contribution | 0.957 | 1 | 0.328 |
| A.I. contribution to knowledge | 1.528 | 1 | 0.216 |
| A.I. contribution to development | 0.705 | 1 | 0.401 |
Kruskal–Wallis’ test for specialty.
| Variable | Kruskal–Wallis | df | p-Value |
|---|---|---|---|
| Rate your A.I. knowledge | 4.400 | 2 | 0.111 |
| Correct answers regarding A.I. | 2.912 | 2 | 0.233 |
| Interest in being briefed about A.I. | 0.737 | 2 | 0.692 |
| Interest in using A.I. in everyday life | 4.757 | 2 | 0.093 |
| Interest in using A.I. professionally | 2.299 | 2 | 0.317 |
| Trust in A.I. estimates | 4.325 | 2 | 0.115 |
| A.I. professional usefulness | 1.686 | 2 | 0.430 |
| Revolutionize profession | 0.353 | 2 | 0.838 |
| Integration into curricula | 0.164 | 2 | 0.921 |
| A.I.: partner than competitor | 7.322 | 2 | 0.026 |
| Eagerness for A.I. contribution | 5.789 | 2 | 0.055 |
| A.I. contribution to knowledge | 7.157 | 2 | 0.028 |
| A.I. contribution to development | 7.442 | 2 | 0.024 |
T-test with grouping variable: gender.
| Variable | Variances | Levene’s p | One-Sided p | Two-Sided p |
|---|---|---|---|---|
| Interest in being briefed | Equal variances | 0.224 | 0.026 | 0.054 |
| Trust in estimates | Equal variances | 0.278 | 0.118 | 0.238 |
| Partner than competitor | Equal variances | 0.664 | 0.016 | 0.032 |
| Contribution to development | Equal variances | 0.021 | 0.192 | 0.382 |
T-test with grouping variable: age groups.
| Variable | Variances | Levene’s p | One-Sided p | Two-Sided p |
|---|---|---|---|---|
| Interest in being briefed | Equal variances | 0.330 | 0.008 | 0.018 |
| Trust in estimates | Equal variances | 0.900 | 0.022 | 0.044 |
| Partner than competitor | Equal variances | 0.572 | 0.020 | 0.042 |
| Contribution to development | Equal variances | 0.735 | 0.014 | 0.030 |
T-test with grouping variable: education level.
| Variable | Variances | Levene’s p | One-Sided p | Two-Sided p |
|---|---|---|---|---|
| Interest in being briefed | Equal variances | 0.751 | 0.420 | 0.837 |
| Trust in estimates | Equal variances | 0.129 | 0.918 | 1.833 |
| Partner than competitor | Equal variances | 0.212 | 1.323 | 2.649 |
| Contribution to development | Equal variances | 0.354 | 0.759 | 1.518 |
Appendix A
Appendix A.1. Questionnaire
-
Gender:
-
Age:
-
What’s your education level?
(Choose only one)
Bachelor Master Doctorate ☐ ☐ ☐ -
What’s your undergraduate studies?
(Choose only one)
Biology Veterinary Agriculture Aquaculture Animal Sc. Other ☐ ☐ ☐ ☐ ☐ ☐ -
What’s your specialty?
(Choose only one)
Ichthyologist Ichthyopathologist Farm Manager ☐ ☐ ☐ -
Rate your knowledge on the subject of A.I.
(Choose only one)
None Low Moderate High Very High ☐ ☐ ☐ ☐ ☐ -
How do you learn and stay informed about the subject of A.I.?
(May choose more than one)
Media Social Media Academic/Professional Social Environment Internet Search ☐ ☐ ☐ ☐ ☐ -
Do you know the terms “Machine Learning” and “Deep Learning”?
(Choose only one)
I don’t know any of the terms I only know one term I know both terms but not their difference I know both terms and their difference ☐ ☐ ☐ ☐ -
Which of the following applications use A.I.?
(May choose more than one)
Spam detection in email services Check spelling and typographical mistakes Shopping suggestions in e-shops Voice navigation assistant Search Engines (Google, Bing, etc.) ☐ ☐ ☐ ☐ ☐ -
Have you ever been involved in the development of A.I. applications?
(Choose only one)
Yes No ☐ ☐ -
How interested are you in learning about the subject of A.I.?
(Choose only one)
None Low Moderate High Very High ☐ ☐ ☐ ☐ ☐ -
How interested are you in the use of A.I. applications in your everyday life?
(Choose only one)
None Low Moderate High Very High ☐ ☐ ☐ ☐ ☐ -
How interested are you in the use of A.I. applications in your work?
(Choose only one)
None Low Moderate High Very High ☐ ☐ ☐ ☐ ☐ -
Would you trust an A.I. application advice on a serious matter?
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
Which source of information would you trust more on a serious matter?
(Choose only one)
A.I. Human Specialist Neither ☐ ☐ ☐ -
How useful do you think the use of A.I. will prove to be in your professional field?
(Choose only one)
None Low Moderate High Very High ☐ ☐ ☐ ☐ ☐ -
In how many years do you think A.I. applications will be widely used in your field?
(Choose only one)
Already being used Next 2 Next 5 Next 10 Next 15 More than 15 ☐ ☐ ☐ ☐ ☐ ☐ -
A.I. will revolutionize my field.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
A.I. training should be included in my undergraduate curricula.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
Do you think that A.I. will replace you in your professional field?
(Choose only one)
Yes No ☐ ☐ -
I view A.I. as a partner than a competitor.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
I’m eager for the contribution of A.I. in my professional field.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
A.I. will help me expand my professional knowledge.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
A.I. will contribute to my professional progress and development.
(Choose only one)
Strongly Disagree Disagree Neither Agree Strongly Agree ☐ ☐ ☐ ☐ ☐ -
How does A.I. make you feel?
(May choose more than one)
Security Interest Joy Other, positive Anguish Fear Insecurity Other, negative ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
References
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