The impact of using AI tools in enhancing the analytical skills of mass communication students: a quasi-experimental study

This study seeks to understand the effects of using artificial intelligence (AI) tools by mass communication students in developing their analytical skills of media content through a quasi-experimental study. The students were trained to use several AI tools, notably YouTube Scripter, Reader GPT, and NoteGPT, to extract written transcripts from videos, to use ChatGPT to discuss and analyze the written transcript or text, and to use GPT to read and interpret the video directly. Most of previous related studies focused on the use of AI tools in education from the descriptive perspective of attitudes or general applications. These studies lacked empirical investigation of the effects of the use of AI tools on the development of analytical skills specifically among mass communication students. Therefore, the present quasi-experimental study sets out to address this research gap by evaluating the impact of AI tools on the improvement of analytical skills among mass communication students at “October 6” University in Egypt. The fieldwork, conducted in December 2024, deployed a questionnaire using the time series method. A group of fourth-year students at the “October 6 University” in Egypt constituted the informants or subjects. Subsequently, the differences in the students’ responses, before and after using the AI tools to analyze the content of two news bulletins aired on DW and BBC channels, were measured. The results showed that using AI tools enhanced the students’ ability to analyze media content. The study recommended further research on leveraging AI tools in mass communication university education to enhance the students’ analytical skills for various types of media content, for example, the persuasive media content, and to equip media students with various skills that will make them suitable for optimal professionalism after graduation from the media school.

Introduction

The Fourth Industrial Revolution and AI, along with their multidimensional utilities, are envisaged to have a significant impact on the job market in the future. Some jobs are likely to disappear, and new ones will emerge. To do this, universities need to contemplate the future and prepare adequately for it through learning, gaining experience, and talking about it all the time (Ullah et al., 2025; Abdelmagid et al., 2024; Oosthuizen, 2022). So, universities need to work harder to ensure that university degrees maintain their desired worth (Cao et al., 2023). To stay relevant, universities need to update their courses with AI applications curriculum in view of the contemporary requirements of the job market. This means that all departments, whether of STEM fields or not, are expected to integrate AI-related courses to prepare their students for the interdisciplinary needs of AI-driven industries (Zekanović-Korona et al., 2025; Ma and Siau, 2019).

At the same time, it is widely acknowledged that the greatest shortcoming today with AI technologies offering personalized learning is the inadequacy of good learning resources, in terms of technology and usage (Trisnawati et al., 2023; Hirankerd and Kittisunthonphisarn, 2020). Addressing these challenges is therefore essential to optimize the use of AI in education.

Regarding mass communication field, it is worth mentioning that the use of AI tools in media content analysis has radically transformed education, especially media and journalism studies (Ehigie, 2025). Students are now able to use AI tools, such as ChatGPT along with natural language processing (NLP) and machine learning (ML) algorithms to efficiently process a vast amounts of content, enhancing their analytical capabilities and expanding their knowledge of critical media. Moreover, these tools provide students in media colleges with the opportunity to develop excellent analytical skills by acquiring experience in automating data collection and classification, as well as extracting key ideas from diverse sources (Siahaan et al., 2025; Arbab, 2025; Harahap, 2024). For this reason, many professors have begun to recognize the integration of AI in media education as a key approach to help students improve their skills and prepare for the job market. AI plays multiple roles in education, including supporting students’ technological literacy, critical thinking, and problem solving. These skills are important for job mobility in today’s complicated, AI-driven job markets (Satone et al., 2024; Samigova, 2023).

In this regard, many literatures have highlighted the employment of AI tools in media content analysis, resulting in the enhancement of analytical skills among media students. Various studies have shown that AI, especially ChatGPT-3, improves critical thinking and journalistic writing skills, develops in-depth analysis skills and promotes collaboration in educational settings, especially media colleges (Irfan et al., 2023). Moreover, other studies have shown that AI-based social media applications improve students’ academic performance and critical thinking skills, especially when creating specialized content (Bashiri and Kowsari, 2023).

In general, integrating AI tools into media education has multiple benefits. It does not only enhance the students’ analytical skills but it also equips them to deal with challenges and to exploit opportunities resulting from an AI-enriched media environment. This underscores the importance of applying a multidisciplinary curriculum that enables students to be aware of the technical components of media studies and ethical practices of AI tools, in developing critical thinking skills (Dinçer, 2024; Verma and Rohman, 2024; Chiang et al., 2022). In addition, it is worth noting that the use of AI in media and press raises concerns about ethical challenges, news biases, and job displacements. These concerns should be adequately addressed to guarantee the practice of responsible and impartial journalism (Aissani et al., 2023; Valtonen et al., 2019).

Chiu et al. (2022) posits that AI has three interwoven jobs: to help teachers improve their skills, to help them teach better, and to give them flexible teaching strategies in the classroom. Furthermore, the function of AI in media education is largely regarded as revolutionary, providing customized learning experiences and promoting creativity; however, it necessitates meticulous integration to tackle ethical issues and guarantee successful learning outcomes (Jha and Singh, 2024; Luo and Zhu, 2025). In general, AI has a lot of potential to improve analytical skills in media education, but its success depends a lot on the teaching methods, teacher mediation, and the educational culture that values critical thinking over just getting things done (Anita et al., 2025; Irfan et al., 2023).

Therefore, in the light of previous studies and the limited number of these studies that have focused on the use of AI in enhancing the capabilities of media students, specifically with regard to content analysis, it can be argued that the field of mass communication education is rapidly evolving due to the rise of AI applications, especially the tools that can quickly and accurately analyze media texts, find semantic patterns, and understand frames and media discourses. Even though this is a step forward, many university-level mass communication programs still use traditional methods that emphasize manual analysis and individual effort when it comes to teaching content analysis. Such practices may inhibit the cultivation of students’ advanced analytical competencies, encompassing critical thinking, contextualizing media content within social and political frameworks, and revealing the underlying ideologies inherent in media messages.

On the other hand, there is a significant absence of systematic empirical evidence investigating the effects of incorporating AI tools into the cultivation of analytical skills within media education settings, especially in the Arab context. Consequently, the research problem this study focuses on evaluating the degree to which the utilization of AI tools enhances the analytical skills of mass communication students. The absence of any previous study that focuses on this type of evaluation creates a research gap that this study intends to fill. The evaluation will be accomplished, first, by creating an instructional intervention that teaches the students how to use AI tools to analyze media content, and second, by measuring their analytical performance before and after the training to see how well this integration works and its implications for teaching and learning media studies. In this regard, the main objective of this study is to identify the effects of using AI tools in analyzing media content on the development of the analytical skills of media college students.

Generally, the importance of improving analytical skills for mass communication students stems from the fact that it is the fundamental skill they use to analyze the various communication messages they encounter, and that the ability to analyze professionally leads to the ability to formulate innovative media messages.

Literature review

AI has proven to have significant capabilities for generating and analyzing media content (Patil et al., 2024), thereby producing positive impacts for the stages of generating, delivering, and consuming media content (Erafy, 2023). These capabilities incorporate generating processes for content, notably elements, storylines, and terrains (Mao et al., 2024). The generative capabilities of AI also encompass the creation of different forms of media, such as images, causal images, audio and video, and text/script write-ups (Li X. et al., 2024), and the exploration of the use of AI in generating specific social media posts like Twitter (X) posts (Xue et al., 2024).

Unlike its potential uses for creation, it is important to note the critical role that AI plays in monitoring and identifying fraudulent content and deception (Sánchez-Esparza et al., 2024). Previous research has shown the potentials it holds for the identification of deepfake videos (Ma et al., 2022), as well as in monitoring social media networks for deceptive content (Sánchez-Esparza et al., 2024). The development of its uses for content creation has also led to the development of four news organizations running entirely on AI networks (Ufarte-Ruiz et al., 2023).

Despite these abilities, there are identified areas of technical shortcomings as well as the substantial social and legal implications of the use of AI in content creation. From a technical perspective, the use of AI for procedural generation faces huge training data requirements to ensure that AI-based generative models perform well (Mao et al., 2024; Badmus et al., 2018). From the social and legal perspective, AI-based applications face challenges and implications of copyright violation, for example, misuse and plagiarism of AI-generated intellectual property (Zhuang, 2024). From the perspective of news production and dissemination, the use of AI for content generation requires close careful human oversight for potential risks associated with bias and the generation of misinformation (Castillo-Campos et al., 2024). Furthermore, there are occupational hazards identified in the original document but not cited, which pose risks of job instability for journalists arising from the extensive use of AI technology.

Findings from scholarly research reveal a certain disparity between the realism of the content produced using AI and the acceptability of this content by the public. On the one hand, the public finds it difficult to distinguish between human-created content and content produced using AI in terms of realism (Peña-Fernández et al., 2023; Li Y. et al., 2024). On the other hand, the public still demonstrates a certain preference for human-created news content, while AI-created content results in negative reactions from consumers (Raj et al., 2023).

The level of AI adoption in the media industry has a high level of variability. Media companies display general enthusiasm about using AI tools in their operations to develop the media industry (Liu, 2023; Erafy, 2023). However, the adoption level of social media content creation technologies is still relatively low (Xue et al., 2024). Previous literature has focused on the drivers of AI adoption from the institutional perspective, such as researchers who studied the level of institutional enthusiasm about using AI (Abohamam et al., 2024). However, field studies indicate that this theoretical enthusiasm is not always accompanied by mass adoption. In Jordan, the media industry has still not fully adopted AI, and institutional enthusiasm about AI has decreased by 56%, with a decrease in the perceptions by professionals about its benefits (Abohamam et al., 2024).

Although previous literature abounds on AI’s generative capabilities and the corresponding risks (Mao et al., 2024; Castillo-Campos et al., 2024), and on the general interest in AI adoption in the media industry (Abohamam et al., 2024), little research, relatively, has been conducted on the combined influence of AI adoption in the media industry. In this regard, it is necessary to establish a connection between the level of institutional adoption (Xue et al., 2024; Abohamam et al., 2024), and the ethical and legal challenges (Zhuang, 2024) posed to journalists.

Regarding the impact of AI on the university students’ analytical and critical skills, Melisa et al. (2025) found that ChatGPT acts as a tool to encourage students to enhance problem-solving skills. However, its effectiveness largely depends on the students’ ability to apply critical and analytical evaluation to AI-generated content. In the same sequence, Hu et al. (2025) found that AI tools have a great impact on critical thinking, especially computational thinking. The authors tried to compare the role of AI tools in enhancing the analytical skills of both engineering and liberal arts students. But, no significant differences were observed in this regard. Another comprehensive study examined the impact of ChatGPT on the learning skills and the academic performance of the university students. The results showed that it influenced computational, critical, and reflective thinking skills positively. Through a quasi-experimental structure of this study, the authors found that ChatGPT reduced mental effort significantly, especially among the arts and humanities students (Ma and Zhong, 2025). In the same context, Wang and Fan (2025) found that ChatGPT has a moderately positive impact on enhancing analytical and critical thinking. The study recommended the wide use of emerging AI tools in different types of courses to support diverse learning needs.

Regarding the studies that investigated leveraging AI in the media and mass communication curricula, Imran (2025) studied the use of AI technologies at “the University of Melbourne and the University of Sydney in Australia and the readiness of the American University in Cairo (AUC) and Cairo University in Egypt.” The study emphasized the importance of embedding the AI applications into the communication and media schools in order to prepare students for the evolving media landscape and to be fit for the labor market. In the same sequence, Valtonen et al. (2019) highlighted the significance of linking media literacy education and algorithms. They emphasized the importance of technological agility between both teachers and students in media schools to enhance their readiness to cope with new media and to become skilled professionals in the media labor market.

An analysis of previous studies reveals that most of the literature focused more on ChatGPT than on other AI tools to understand the role of AI tools in university education in general and in developing learners’ skills, including analytical skills, in particular. Most previous studies relied on second-level analysis, and few experimental studies attempted to measure the relationship between AI tools and improved student learning skills. Furthermore, no previous studies addressed the application of AI tools to media students; applications were generally compared to practical disciplines like engineering vs. theoretical disciplines such as the humanities and arts. This leads to identifying the research problem of this study which is to investigate the role of AI tools in improving analytical skills specifically among students enrolled in mass communication specialization, whose future work tasks are heavily reliant on these skills. In addition, the unique contribution of the study lies in using a quasi-experimental design to test specific analytical skills with AI tools.

Study hypotheses

This study rests on the following hypotheses:

• No significant difference in the ability to identify media topics before and after using AI tools in media content analysis.

• The use of AI tools has no significant effect on students’ ability to analyze the language and target audience of media programs.

• No significant difference in the ability to identify nature of media coverage before and after using AI tools in media content analysis.

• No statistically significant difference in students’ identification of active forces in media programs after using AI tools for content analysis.

• No significant difference in the ability to identify media persuasion techniques before and after using AI tools in media content analysis.

Methodology

Study type and method

This is a quasi-experimental study that was conducted in December 2024 to determine the effect of students’ use of AI tools in developing their analytical skills of media content. The study utilized a pretest–post-test within-subject design, which generally provides greater statistical sensitivity than between-subjects designs, resulting in a sample of 55 students in the fourth year of the faculty of mass communication in the “October 6” University in Egypt. Specifically, the students were enrolled in the “international media and directed channels” course. Three main reasons provide the justification of the choice of the group. First, the course content is directly related to advanced media content analysis (such as framing, discourse, propaganda, and directed messaging), which makes it a good place to test AI-assisted analytical training. Second, fourth-year students usually understand theory and method, sufficient enough to show higher-order analytical skills. This facilitates the accurate measurement of change between the pretest and post-test. Third, limiting participation to one academic level and course cohort makes the sample more similar and reduces the variability that comes from different prior knowledge and academic maturity. This makes the internal validity stronger in a within-subject quasi-experimental design. Also, using the intervention in a real course setting makes it more ecologically valid, and ensures that the data collection is done in a standard and ethical way.

Regarding the sampling procedures, the objectives of the study were announced to the students, and the names of willing volunteers were collected from among the students enrolled in the aforementioned course. Students at this academic level share a certain degree of similarity in their skills, including analytical skills, which distinguishes them from those in other academic levels. The selection of group members was made by the fourth-year students specifically because they have already acquired an adequate knowledge of traditional analytical tools and how to utilize them. Therefore, the homogeneity of the sample in terms of age, educational background, academic year, and field of study suggests the applicability of its findings to other similar populations.

The sample size was validated according to the principle of statistical power. A priori power analysis (α = 0.05; power = 0.80) using G*Power 3.1 demonstrated that a sample size of N = 55 is adequate to identify an effect of approximately d ≈ 0.51 or greater, indicative of a medium effect, which is reasonable considering the nature of the training intervention (Faul et al., 2009). Furthermore, this sample size aligns with standard methodologies in educational quasi-experimental and pilot studies conducted within a single class or cohort, constrained by the practical limitations of the instructional environment (Julious, 2005).

As is known, a pretest–post-test design is a common quasi-experimental research design that is used to evaluate the effect of an independent variable(s) in which researchers start by measuring the outcome variable before the intervention (pretest) and again after the intervention (post-test). The change in the scores is then attributed to the intervention of the independent variable. For this reason, the control group was omitted in this quasi-experiment. In this regard, it is worth mentioning that the students’ training lasted for approximately 9 h, distributed over three separate study days, at a rate of 3 h per day. The 3 days were determined based on the students’ agreement to the training times during these days.

In this regard, these researchers measured the following points that were related to the students’ analytical skills: identifying topics, language, target audience, nature of media coverage, the active forces, and media persuasion techniques through the use of pre-and post-test questionnaire.

Design of the experiment and participants

To investigate the effect of using AI tools on enhancing students’ ability to analyze media content, the students were trained on the use of some AI tools extensions, such as YouTube scripter, reader GPT, NoteGPT and on how to extract the script in writing (or transcribe the script) from the video. They were also trained on how to use ChatGPT to discuss and analyze the written or transcribed text first, and how to rely on GPT to read and analyze the video directly. Subsequently, the researchers measured the differences in the students’ responses before and after using the AI tools in analyzing the contents of the two news bulletins emanating from BBC and DW media channels.

The study deployed a questionnaire as its research instrument. The time series method, which is similar to the pre-post design of one group, was adopted. The selection of group members was done from the fourth-year students specifically because they have already acquired an adequate knowledge of traditional analytical tools and how to utilize them.

The study used both descriptive and inferential statistics. McNemar test was applied when comparing the paired binary responses before and after the intervention, according to the varied media content analysis aspects, while the paired samples t-test was utilized to compare the overall analytical performance prior to the application of AI tools, compared to performance after the application of these tools. The values of statistical significance were provided in the tables.

Results

In this section, the findings of the study, based on the research objectives and the research questions, are presented in a manner consistent with a quasi-experiment based on pre- and post-testing on the same group. In doing this, the outcomes are discussed systematically, starting with the presentation of basic descriptive statistics, followed by the results obtained from the inferential statistical analysis in assessing the difference between the two tests.

The responses of media college students in media content analysis for DW channel before using AI analysis

Figure 1 presents the correct responses by the media college students in media content analysis for DW channel before using AI analysis. The highest number of the correct responses is recorded for the responses for Language Used which is 41 (that is, 19.43%) from a total number of (211) correct responses for all items in the media content analysis. This is followed by the number of responses for Active Forces in the Program which is 39 (that is, 18.48%). The lowest number of correct responses is recorded for Target Audience of The Program which is 22 (that is, 10.43%). The highest number of correct responses in the Language Used category before using AI tools is obtained because the category relies on observable and explicit indicators that students are already used to analyzing. On the other hand, figuring out the Target Audience of The Program target requires deeper inference and contextual interpretation of implicit messages, which is harder to do without training or helpful analytical tools. This difference shows that students need better tools to help them with interpretive analysis.

Figure 1

Figure 1. The correct responses of media college students in media content analysis for DW channel before using AI analysis.

The response of media college students in media content analysis for DW channel after using AI analysis

Figure 2 presents the correct responses by the respondents in media content analysis for DW channel after using AI analysis. The highest number of the correct responses is recorded for Language Used which is 53 (that is, 15.73%) from the total number correct responses of all items of the media content analysis. This is followed by the number of correct responses for Active Forces in the Program which is 50 (that is, 14.84%). This is followed by the number of correct responses for Balance in Presentation which is 49 (that is, 14.54%). The number of correct responses for Nature of the Topic & Persuasion Techniques Used and Nature of the Media coverage, respectively, is 48 (that is, 48.14%). The lowest number of correct responses is recorded for Target Audience of the Program which is 41 (that is, 12.17%). The collective increase in the number of correct answers through the use of AI indicates that the tools facilitated the structuring and enhancement of the analysis process for the media content on most aspects. While Language Used and Active Forces in the Program were the aspects where the correct answers continued to increase because they were more explicit, the enhancement in the more intricate aspects indicates the role of AI in the promotion of the interpretive and organizational aspects of the analysis, where Target Audience of the Program still stood out as relatively more difficult because it had an inferential distinction.

Figure 2

Figure 2. The correct responses of media college students in media content analysis for DW channel after using AI analysis.

The responses of media college students in media content analysis for BBC channel before using AI analysis

Figure 3 presents the correct responses by the respondents in media content analysis for BBC channel before using AI analysis. The highest number of the correct responses is recorded for the response for Nature of the Topic which is 29 (that is, 19.33%) from the total correct responses of all items of the media content analysis. This is followed by the number of correct responses for Language Used which is 28 (that is, 18.67%). The lowest number of correct responses is recorded for Active forces in the Program which is 14 (that is, 9.33%). Before AI analysis for the BBC channel, students tended to do better at spotting the topic because of the clear thematic cues, and noticing the language used based on observable linguistic features, while they struggle in identifying active forces, because it is challenging to recognize these hidden actors and power dynamics without the support of an analytical tool.

Figure 3

Figure 3. The correct responses of media college students in media content analysis for BBC channel before using AI analysis.

The responses of media college students in media content analysis for BBC channel after using AI analysis

Figure 4 presents the correct responses by the respondents in media content analysis for BBC channel after using AI analysis. The highest number of the correct responses is recorded for Language Used which is 39 (that is, 15.98%) from the total correct responses of all items of the media content analysis. This is followed by the number of correct responses for Nature of the Topic and Persuasion Techniques Used, respectively, which is 37 (that is, 15.16%). The lowest number of correct responses is recorded for Balance in Presentation and Active Forces in the Program, respectively, which is 31 (that is, 12.70%). After AI analysis, the overall analytical performance improved for the BBC channel. The students were accurate when spotting the language used, a finding that aligns with the clear surface-level clues that the AI helps to project. Yet, the categories of balance in presentation and active forces remained difficult to handle by the students because these categories require deeper context and interpretation of implicit power relations. This is a task that remains cognitively demanding to the students despite the support provided by AI tools.

Figure 4

Figure 4. The correct responses of media college students in media content analysis for BBC channel after using AI analysis.

The responses of media college students in media content analysis for DW & BBC channels before and after using AI analysis

Figure 5 presents the correct responses by the respondents in media content analysis for DW and BBC channels before and after using AI analysis. The highest number of the correct responses is recorded for Language Used which is 69 (that is, 19.11%) from the total correct responses of all items of the media content analysis. This is followed by the number of responses for Nature of the Topic which is 60 (that is, 16.62%). The lowest number of correct responses is recorded for Balance in Presentation and Target Audience of the Program, respectively, which is 39 (that is, 10.8%). The results demonstrate a fairly balanced or an evenly distributed pattern of correct responses. There is no dominant category. The differences in performance level by the students are defined by fine margins. This indicates a general improvement in the performance by the students in all the categories.

Figure 5

Figure 5. The correct responses of media college students in media content analysis for DW & BBC channels before and after using AI analysis.

The responses of media college students in media content analysis for DW & BBC channels after and after using AI analysis

Figure 6 presents the correct responses by the respondents in media content analysis for DW & BBC channels before and after using AI analysis. The highest number of the correct responses is recorded for Language Used which is 92 (that is, 15.83%) from the total correct responses of all items of the media content analysis. This is followed by the number of correct responses for Nature of the Topic and Persuasion Techniques Used, respectively, which is 85 (that is, 14.63%). The lowest number of correct responses is recorded for Target Audience of the Program which is 74 (that is, 12.74%). A marked similarity between Figures 5, 6 reveals an increased level of performance by the students in media content analysis with reference to the DW and BBC channels after utilizing AI analysis. Initially, students were performing exceptionally well in Language Used and Nature of Topic, which are explicit features, while Balance in Presentation and Target Audience scored the lowest number of correct marks as these parameters are implied and are at a higher level of complexity. But in Figures 5, 6, a general improvement by the students becomes evident in all the categories, including those related to higher levels of complexity, notably Balance in Presentation, Target Audience, and Persuasive Techniques, where AI analysis helped students to delve deeper, though target audience took a little longer to identify. As remarked in Figure 5, the results of the analysis demonstrate a fairly balanced or an evenly distributed pattern of correct responses without any clearly dominant category.

Figure 6

Figure 6. The correct responses of media college students in media content analysis for DW & BBC channels after and after using AI analysis.

The responses of media college students in media content analysis for DW channel before and after using AI analysis

Figure 7 presents the correct responses by the respondents in media content analysis for DW channel before and after using AI analysis. It shows that all the correct responses for all the items increased by using AI tools in media content analysis for DW channel.

Figure 7

Figure 7. The correct responses of media college students in media content analysis for DW channel before and after using AI analysis.

The responses of media college students in media content analysis for (BBC) channel before and after using AI analysis

Figure 8 presents the correct responses by the respondents in media content analysis for BBC channel before and after using AI analysis. It shows that all the correct responses for all the items increased by using AI tools in media content analysis for BBC channel.

Figure 8

Figure 8. The correct responses of media college students in media content analysis for BBC channel before and after using AI analysis.

The responses of media college students in media content analysis for both DW & BBC channels before and after using AI analysis

Figure 9 presents the correct responses by the respondents in media content analysis for both DW & BBC channels before and after using AI analysis. It shows that all the correct responses for all the items increased by using AI tools in media content analysis for both DW & BBC channels. The results presented in Figures 7–9 above indicate that there is a marked improvement in correct responses regarding all aspects considered in media content analysis, following the application of AI tools, whether for DW, BBC, or for a combination of the two channels. The improvement was more evident in aspects that entail more complex organization and analysis, like persuasion techniques and type of media coverage. The effectiveness of these tools in facilitating the transition from descriptive analysis to more complex analysis is also evident.

Figure 9

Figure 9. The correct responses of media college students in media content analysis for both DW & BBC channels before and after using AI analysis.

Result of the hypotheses

Hypothesis 1: No significant difference in the ability to identify the nature of the media topics before and after using AI tools in media content analysis.

Table 1 presents the results of the responses about identifying the nature of the media topics before and after using AI tools in media content analysis.

Table 1

Table 1. The responses about identifying the nature of the media topics before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 31 (that is, 56.36%) before using AI tools in media content analysis and it increased to 48 (that is, 87.27%) after using the AI tools. McNemar test value is 15.21, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media topics before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 21 (that is, 38.18%) before using AI tools in media content analysis. It increased to 26 (that is, 47.27%) after using the AI tools. McNemar test value is 8, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media topics before and after using AI tools in media content analysis for BBC channel.

DW & BBC channels: The number of the correct responses was 60 (that is, 54.55%) before using AI tools in media content analysis. It increased to 85 (that is, 77.27%) after using the AI tools. McNemar test value is 23.15, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media topics before and after using AI tools in media content analysis for DW & BBC channels. The increased number of correct responses after the usage of the AI tools shows that the tools assisted the students in effectively identifying and defining the central theme of the content in the media. This was confirmed by the McNemar result, which was significant.

Hypothesis 2: No significant difference in identifying the language used in the media before and after using AI tools in media content analysis.

Table 2 presents the results of the responses about identifying the language used in the media before and after using AI tools in media content analysis.

Table 2

Table 2. The responses about identifying the language used in the media before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 41 (that is, 74.55%) before using AI tools in media content analysis. It increased to 53 (that is, 96.36%) after using the AI tools. McNemar test value is 12, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the language used in the media, and the nature of the media topics before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 1 (that is, 32.73%) before using AI tools in media content analysis. It increased to 27 (that is, 49.1%) after using the AI tools. McNemar test value is 11, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the language used in the media, and the nature of the media topics before and after using AI tools in media content analysis for (BBC) channel.

Both DW & BBC channels: The number of the correct responses was 69 (that is, 62.73%) before using AI tools in media content analysis. It increased to 92 (that is, 83.64%) after using the AI tools. McNemar test value is 23, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the language used in the media, and the nature of the media topics before and after using AI tools in media content analysis for DW & BBC channels. There is an apparent enhancement in the accuracy of language identification that corresponds to the explicitness of linguistic features and the use of AI technology in improving attention to significant aspects of the words used. The statistical significance of the outcomes also attests to this improvement in accuracy.

Hypothesis 3: No significant difference in identifying the target audience in the media before and after using AI tools in media content analysis.

Table 3 presents the results of the responses about identifying the target audience in the media before and after using AI tools in media content analysis.

Table 3

Table 3. The responses about identifying the target audience in the media before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 30 (that is, 54.55%) before using AI tools in media content analysis. It increased to 48 (that is, 87.27%) after using the AI tools. McNemar test value is 18, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the target audience in the media, and the nature of the media topics before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 16 (that is, 29.1%) before using AI tools in media content analysis. It increased to 26 (that is, 47.27%) after using the AI tools. McNemar test value is 12, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the target audience in the media, and the nature of the media topics before and after using AI tools in media content analysis for BBC channel.

Both DW & BBC channels: The number of the correct responses was 55 (that is, 50%) before using AI tools in media content analysis. It increased to 85 (that is, 77.27%) after using the AI tools. McNemar test value is 30, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the target audience in the media, and the nature of the media topics before and after using AI tools in media content analysis for DW & BBC channels.

Hypothesis 4: No significant difference in the ability to identify media persuasion techniques before and after using AI tools in media content analysis. The rise in the correct identification of the target audience implies that the AI tools helped the students to relate media messages with the target audience. This particular item is relatively tough because it demands inference and interpretation.

Table 4 presents the results of the responses about identifying the media persuasion techniques in the media before and after using AI tools in media content analysis.

Table 4

Table 4. The responses about identifying the media persuasion techniques in the media before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 25 (that is, 45.45%) before using AI tools in media content analysis. It increased to 48 (that is, 87.27%) after using the AI tools. McNemar test value is 19.59, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the media persuasion techniques in the media before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 15 (that is, 27.27%) before using AI tools in media content analysis. It increased to 25 (that is, 45.45%) after using the AI tools. McNemar test value is 15, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the media persuasion techniques in the media before and after using AI tools in media content analysis for BBC channel.

Both DW & BBC channels: The number of the correct responses was 46 (that is, 41.82%) before using AI tools in media content analysis. It increased to 84 (that is, 76.36%) after using the AI tools. McNemar test value is 34.38, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the media persuasion techniques in the media before and after using AI tools in media content analysis for DW and BBC channels. A major observed enhancement in the detection of the techniques of persuasion indicates that the use of AI tools in the process helped the students to discover the rhetorical devices and appeals that might not otherwise have been readily evident.

Hypothesis 5: No significant difference in the ability to identify the nature of media coverage before and after using AI tools in media content analysis.

Table 5 presents the results of the responses about identifying the nature of media coverage before and after using AI tools in media content analysis.

Table 5

Table 5. The responses about identifying the nature of media coverage before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 22 (that is, 40%) before using AI tools in media content analysis. It increased to 41 with a percentage of (74.55%) after using the AI tools. McNemar test value is 10.31, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media coverage before and after using AI tools in media content analysis for (DW) channel.

BBC channel: The number of the correct responses was 11 (that is, 20%) before using AI tools in media content analysis. It increased to 23 (that is, 41.82%) after using the AI tools. McNemar test value is 16, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media coverage before and after using AI tools in media content analysis for BBC channel.

Both DW and BBC channels: The number of the correct responses was 39 (that is, 35.45%) before using AI tools in media content analysis. It increased to 74 (that is, 67.27%) after using the AI tools. McNemar test value is 24.01, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the nature of the media coverage before and after using AI tools in media content analysis for DW and BBC channels. The increased numbers of correct responses indicate that the students were able to classify the type and angle of media coverage more easily after employing AI-based tools. The significant results indicate a move toward a more systematic and structured analytical reading.

Hypothesis 6: No statistically significant difference in the identification of active forces in media programs after using AI tools for content analysis.

Table 6 presents the results of the responses about identifying the active forces in media programs before and after using AI tools in media content analysis.

Table 6

Table 6. The responses about identifying the active forces in media programs before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 39 (that is, 70.9%) before using AI tools in media content analysis. It increased to 50 (that is, 90.9%) after using the AI tools. McNemar test value is 11, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the active forces in media programs before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 12 (that is, 21.82%) before using AI tools in media content analysis. It increased to 23 (that is, 41.82%) after using the AI tools. McNemar test value is 17, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the active forces in media programs before and after using AI tools in media content analysis for BBC channel.

Both DW and BBC channels: The number of the correct responses was 53 (that is, 48.18%) before using AI tools in media content analysis. It increased to 81 (that is, 73.64%) after using the AI tools. McNemar test value is 28, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the active forces in media programs before and after using AI tools in media content analysis for DW and BBC channels. The advancement in the detection of active forces in media programs shows that AI technology assisted the students in identifying both explicit and implicit actors and their roles in media content. This aspect benefited from AI support which is dependent on power inference.

Hypothesis 7: No statistically significant difference in the identification of the balance in presentation after using AI tools for content analysis.

Table 7 presents the results of the responses about identifying the balance in presentation before and after using AI tools in media content analysis.

Table 7

Table 7. The responses about identifying the balance in presentation before and after using AI tools in media content analysis.

DW channel: The number of the correct responses was 23 (that is, 41.82%) before using AI tools in media content analysis. It increased to 49 (that is, 89.1%) after using the AI tools. McNemar test value is 26, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the balance in presentation before and after using AI tools in media content analysis for DW channel.

BBC channel: The number of the correct responses was 13 (that is, 23.64%) before using AI tools in media content analysis. It increased to 23 (that is, 41.82%) after using the AI tools. McNemar test value is 15, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the balance in presentation before and after using AI tools in media content analysis for BBC channel.

Both DW and BBC channels: The number of the correct responses was 39 (that is, 35.45%) before using AI tools in media content analysis. It increased to 80 (that is, 72.73%) after using the AI tools. McNemar test value is 41, and p-value is very small (less than 0.05) which indicates the presence of a statistically significant difference between the correct responses about identifying the balance in presentation before and after using AI tools in media content analysis for DW & BBC channels. The significant rise in accurate responses to questions following intervention clearly reveals that the use of AI technology improved students’ capacity for assessing balance and bias in media presentation. The aspect entails a comparison required for improvement in the students’ performance through the use of AI technology.

Hypothesis 8: No statistically significant difference in media content analysis after using AI tools for content analysis.

Table 8 shows the mean responses before and after using AI tools in media content analysis.

Table 8

Table 8. The mean responses before and after using AI tools in media content analysis.

DW channel: The mean for the responses in media content analysis for (DW) channel increased from (3.83) with a standard deviation of (1.08) before using AI tools to (6.12) with a standard deviation of (0.92) after using AI tools. T-test value is (12.52) and p-value is very small (less than 0.05) which refer to the presence of a statistically significant difference between the mean response before and after using the AI tools for media content analysis of DW channel. The effect size was calculated using Cohen’s d (1.69). The large effect size indicates that the intervention had a substantial and practically meaningful, not merely a statistically significant one.

BBC channel: The mean for the responses in media content analysis for (BBC) channel increased from (2.72) with a standard deviation of (1.25) before using AI tools to (4.43) with a standard deviation of (1.31) after using AI tools. T-test value is (11.34) and p-value is very small (less than 0.05) which refer to the presence of a statistically significant difference between the mean response before and after using the AI tools for media content analysis of (BBC) channel. The effect size was calculated using Cohen’s d (1.53). The large effect size indicates that the intervention had a substantial and practically meaningful improvement in participants’ scores.

DW and BBC channels: The mean for the responses in media content analysis for DW & BBC channels increased from (3.28) with a standard deviation of (1.29) before using AI tools to (5.28) with a standard deviation of (1.41) after using AI tools. T-test value is (16.5) and p-value is very small (less than 0.05) which refer to the presence of a statistically significant difference between the mean response before and after using the AI tools for media content analysis of DW and BBC channels. The effect size was calculated using Cohen’s d (1.57). The large effect size indicates that the intervention had a substantial and practically meaningful impact on participants” performance.

Figure 10 presents the mean responses before and after using AI tools in media content analysis for DW & BBC channels. It shows that the mean responses increased by using AI tools in media content analysis for DW channel, for BBC channel and for DW & BBC channels.

Figure 10

Figure 10. The mean responses before and after using AI tools in media content analysis for DW and BBC channels.

Discussion and recommendation

While many studies have focused on how to benefit from AI in the educational process from various perspectives, this study specifically focused on employing AI in analyzing media content specifically by mass communication students. Media content analysis is an essential skill for students in terms of helping them to understand the media content, measuring the media content effects, and guiding them to produce various media content professionally. In the same context, enhancing media content analysis using AI tools results in ameliorating other communicative skills of students such as critical thinking, logical thinking, creativity, problem-solving, and sound objective judgment.

The results of this study showed that using AI tools represented by YouTube Scripter, Reader GPT, and NoteGPT affects the student’s ability to analyze media content positively. In this regard, the results showed that there are significant differences between the correct responses of the items of the media content that the students were exposed to before and after using AI tools in media content analysis for two TV bulletins of BBC and DW. These differences were in favor of using AI tools that were employed in analyzing the content of the media productions of the two channels.

Based on the above results, it can be said that AI is revolutionizing the ways of analyzing media content by providing tools that enhance understanding of the content. The results demonstrated that the above-mentioned AI tools used during this quasi-experimental study helped the students to identify media topics, media coverage, active forces, and media persuasion techniques that were used in the content analysis. Despite the brevity of the training program designed to enhance students’ analytical skills—only 9 h—the post-test demonstrated a significant improvement in this skill, as explained above.

Also, the study results showed that leveraging AI tools in analyzing media content enhanced visual content analysis, and in maintaining consistency in using small case throughout the manuscript. In this regard, the study results are consistent with the results of many other studies that showed the great importance of using AI tools, especially ChatGPT, in improving the university learning process in general and enhancing the students’ analytical skills in particular (Hu et al., 2025; Ma et al., 2025; Melisa et al., 2025; Wang and Fan, 2025). On the other hand, many other studies have showed the risks of over-reliance by students on AI applications in the school system (Jose et al., 2025). This includes, for example, the total reliance on AI in many skills, which leads to a lack of professional development in the future (Xue et al., 2024; Abohamam et al., 2024), as well as unethical and irresponsible uses (Zhuang, 2024).

Overall, following the results of this study, it can be said that embedding AI analytical tools into education can enhance many other skills that depend on analytical skills such as sentiment evaluation, critical thinking development, and creative thinking skills. Based on the results, this study recommends integrating AI tools into classrooms practices by offering subscriptions to various AI applications which support the acquisition and development of diverse skills by higher education students, aligning them with the skills required in the labor market. These subscriptions should be free of charge to all students, as sometimes the cost of subscribing to these AI applications prevents students with low incomes from using them in the educational process, especially in low-income countries.

In addition, it is suggested integrating AI tools into the learning platforms used in the educational process, the most famous of which are the Moodle platform and Microsoft Teams, to help media students use them to solve assignments and homework that require the use of AI in content analysis. In this regard, developing teachers’ skills in using AI tools in the teaching process should be enhanced.

This study also recommends that practical learning units be integrated into a special course for media students. This special course will provide applied procedures concerning the application of AI technology to media content analysis. There should also be graduated sessions of practice before and after, since it was evidenced that there was a significant improvement in the students’ performance through this intervention. The study recommends that a hybrid approach be used that combines the outputs produced by the algorithm with a compulsory review process, utilizing checklists to test them for accuracy, and to ensure that the improvements recorded through the progressive increase in the number of correct responses are not reliant on uncritical tools, but rather on sound judgment.

Finally, there is a need for further studies on how to leverage AI tools to analyze various other types of media content, especially persuasive media content. There is also a need for further empirical and experimental studies exploring the role of AI tools in enhancing the various skills of students enrolled in media academic programs, particularly cognitive, applied, critical communication, lifelong learning, and technological skills. Similarly, there is a need for more experimental studies which examine the impact of AI tools on the development of students’ skills in different disciplines, and whether these applications can replace graduates of these disciplines in the labor market. In addition, the need arises to investigate how students can benefit from AI applications to improve their various other skills in a way that ensures their optimal professionalism in the labor market. Since media studies are inherently linked to creativity, future research should focus on how AI tools can improve the creative skills of media students. Conversely, further studies are needed to examine the risks of university students over-relying on AI tools as an easy substitute for developing their own independent skills of logical thinking, writing, and originality. Additionally, it is suggested that future research should be conducted on the spectrum of AI integration into education process to improve student skills in large-scale studies that include large samples, and that include alternative designs such as survey studies. Additionally, the study could be conducted among university students of diverse demographic characteristics as mediating variables within the framework of studying the impact of AI on improving various students’ skills.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by The Midocean University Research Ethics Committee (MU-REC). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

NE: Conceptualization, Resources, Writing – original draft. HF: Software, Visualization, Writing – review & editing. BE: Conceptualization, Formal analysis, Methodology, Writing – original draft. JB: Conceptualization, Data curation, Project administration, Writing – original draft. AM: Formal analysis, Funding acquisition, Investigation, Resources, Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1737363/full#supplementary-material

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