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Dossier • Encontros Bibli 30 • 2025 • https://doi.org/10.5007/1518-2924.2025.e103465 linkcopy

Open-access INTEGRATING GENAI INTO COMMUNICATION EDUCATION FOR ‘GENERATION PROMPT’: AN EXPLORATION OF ACADEMICS' PERSPECTIVES ON ITS BENEFITS, CHALLENGES, AND FUTURE PROSPECTS IN TURKIYE

Integrando o GENAI no Ensino da Comunicação para a “Geração Prompt”: uma exploração das perspectivas acadêmicas sobre seus benefícios, desafios e perspectivas futuras na Turquia

AuthorshipSCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Objective:  This study investigates the integration of generative artificial intelligence (GenAI) into educational activities in communication faculties across the three most populous cities in Türkiye: Istanbul, Ankara, and Izmir.

Methods:  Using a robust semi-structured in-depth interview technique, a qualitative research method, we conducted online interviews with 15 academics from communication faculties in state and private universities.

Results:  The findings, evaluated through the lens of the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory (DIT), are organized into several categories: "GenAI in the context of technology acceptance," "First encounter with GenAI," "Practices of use in academic activities," " Academics attitude towards students’ use of GenAI," "Potential benefits and challenges," "Institutional GenAI policies," "GenAI policies in Türkiye’s higher education," and "Future predictions."

Conclusions:  Academics reported using GenAI in both theoretical and practical courses, utilizing its creative capabilities. However, they also expressed a critical stance on ethical issues, such as inaccuracies, fabricated content, bias, potential loss of creativity, and copyright concerns. This critical perspective underscores their unwavering commitment to the ethical use of GenAI, reassuring about the responsible implementation of GenAI. Participants emphasized the importance of shifting from knowledge-based to skill-based education for the "Generation Prompt," and predicted a significant decline in media-related employment in the future.

Keywords:
GenAI; Media studies; Communication Education; Generation prompt; Education technologies.

RESUMO

Objetivo:  Este estudo investiga a integração da inteligência artificial generativa (GenAI) nas atividades educativas das faculdades de comunicação das três cidades mais populosas da Turquia: Istambul, Ankara e Izmir.

Método:  Utilizando uma técnica robusta de entrevista semiestruturada em profundidade, um método de investigação qualitativa, realizámos entrevistas online com 15 académicos de faculdades de comunicação de universidades públicas e privadas.

Resultado:  Os resultados, avaliados através da lente do Modelo de Aceitação de Tecnologia (TAM) e da Teoria da Difusão de Inovações (DIT), estão organizados em várias categorias: “O GenAI no contexto da aceitação da tecnologia”, ‘Primeiro encontro com o GenAI’, ‘Práticas de utilização em atividades académicas’, ‘Atitude dos académicos em relação à utilização do GenAI pelos estudantes’, ‘Potenciais benefícios e desafios’, ‘Políticas institucionais do GenAI’, ‘Políticas do GenAI no ensino superior da Turquia’ e ‘Previsões futuras’.

Conclusões:  Os académicos referiram a utilização do GenAI em cursos teóricos e práticos, utilizando as suas capacidades criativas. No entanto, também expressaram uma posição crítica em relação a questões éticas, como imprecisões, conteúdos fabricados, preconceitos, potencial perda de criatividade e preocupações com os direitos de autor. Esta perspectiva crítica sublinha o seu empenho inabalável na utilização ética da GenAI, tranquilizando-os quanto à implementação responsável desta tecnologia. Os participantes sublinharam a importância de passar de uma educação baseada no conhecimento para uma educação baseada em competências para a “Geração Prompt” e previram um declínio significativo no emprego relacionado com as mídias no futuro.

Palavras-chave:
GenAI; Estudos de mídia; Educação em Comunicação; Geração prompt; Tecnologias educacionais.

1 INTRODUCTION

Artificial Intelligence (AI) is a concept that has been attracting attention as a distinct field of academic discipline since the 1950s, and it surrounds and influences almost every moment of social life through smart speakers and devices, personalized content on social media and e-commerce sites, virtual assistants in health, education, and many other fields. Ethical and legal issues raised by AI, such as personal data protection, privacy, biased algorithms, plagiarism, and inadequate regulation of areas such as facial recognition technologies, have also been discussed in recent years.

On November 30, 2022, the launch of ChatGPT was considered a milestone in integrating Generative Artificial Intelligence (GenAI) into the daily lives of the general public (Alier et al., 2024). With this development, AI technology became mainstream for the first time, gaining 1 million users in just five days, and becoming the fastest-growing consumer technology in history (Mahajan, 2024). Within two months, the platform reached 100 million users (Teubner et al., 2023).

In parallel with rapid penetration into everyday life, 2023 was the year of the AI boom, particularly with the scale-up of AI and big language models, and significant advances in deep learning techniques, particularly the generative pre-trained transformer (GPT) architecture, a type of transformative model used for various natural language processing tasks (Langran et al., 2024). The ability of platforms such as ChatGPT, Bard, Stable Diffusion, and Dall-E to handle complex prompts and produce human-like output has led to increased interest and research in the integration of GenAI in various domains such as health, medicine, education, media, and tourism (Chan; Hu, 2023).

In mid-May 2024, OpenAI introduced its most advanced model, ChatGPT-4o (omni). This model includes advanced multimodal capabilities that allow it to process a mix of text, audio, image, and video inputs and produce text, audio, and image outputs with unprecedented accuracy and efficiency (OpenAI, 2024). Besides, Google released Gemini 1.5 a day later, marking a significant advance in language model capabilities (Google, 2024). The rapid progress of technology has led to new applications and methods being incorporated into GenAI every day. One of the areas where GenAI has spread significantly is education (Ching et al., 2024).

Educational institutions, educators, and students have entered the age of GenAI. As with previous transformative information and communication technologies (e.g., printing press, telegraph, telephone, television, personal computer, and internet), AI has intended and unintended, anticipated and unanticipated, beneficial and harmful effects (Trust et al., 2024). Therefore, AI is conceived as "friend and foe" in education (St. George; Svrluga, 2023). In other words, the literature recognizes that while AI and GenAI offer unique positive contributions to education, they also create uncertainties and problems (Langran et al., 2024).

All over the world, discussions have arisen on how and to what extent GenAI can be used in grading, translation, feedback, art and design production, curriculum and course content creation, academic resource consultation, research design, data analysis, and innovative pedagogy in education. The studies on improving teaching and learning with GenAI in higher education have also gained importance (Adıgüzel et al., 2023; Baidoo-Anu; Ansah, 2023). Therefore, the importance of students and educators learning about and working with artificial intelligence is on the agenda (Trust et al., 2023).

As McLuhan (1964) argues, “We become what behold” (p. 29). In a world of ubiquitous television, we become television people because the technology has been designed as an image-heavy experience, clever sound games, and entertainment. In a world of social media proliferation, we become social media people whose attention prefers short, intimate bursts of content. In a world of GenAI, we will become “GenAI-people” (Mishra; Heath, 2024, p.15). Thus, an essential skill that students and teachers will need to acquire in the emerging era of GenAI is the creation and provision of effective prompts to guide them in generating functional responses to GenAI applications (Lo, 2023). In this context, this research argues that, the world will face a “Generation Prompt” that is highly integrated with GenAI and aims to investigate the potential of a communication education that addresses this generation in the future.

Moreover, educators are pioneers in social change and play a catch-up role in technological adaptation (Cuban, 2018). In this context, this study aims to investigate the integration of GenAI into educational training activities in communication faculties in the cities with the largest population in Türkiye (Istanbul, Ankara, and Izmir) through online in-depth interviews with academics. The present study discusses the barriers and drivers for integrating GenAI in education in terms of the Technology Acceptance Model (TAM) and the Diffusion of Innovation Theory (DIT).

According to TAM, users are more likely to adopt and use technology if they believe it will improve their job performance (usefulness) and require little effort (ease of use) (Davis, 1989). On the other hand, DIT, proposed by Rogers, examines how, why, and at what rate new ideas and technologies diffuse across cultures (Rogers, 1962). Therefore, TAM and DIT provide essential perspectives for GenAI research that can provide a comprehensive understanding of the multifaceted process of technology adoption (Ghimire; Edwards, 2024).

As of early 2024, Türkiye has an internet penetration rate of 86.5 percent of the total population (Kemp, 2024). In addition, Türkiye constitutes a critical sample as a country, with 44.9% of its population under the age of 30 and 22.9% between the ages of 15-29 (European Commission/YouthWiki, 2024). In addition, there are tens of thousands of students studying in 76 faculties of communication in Türkiye (İrvan, 2023). For this reason, this study is essential in order to examine communication in such a specific area with more in-depth findings that are specific to one country.

It is crucial to point out that the authors of this article also used GenAI to consult references, to perfect the text in the process of academic writing in English, their second language, to transcribe interviews with participants, and to get feedback on the quality/efficiency of the questions they would ask the participants. First, the results of studies focusing on GenAI in education are presented. Then, the authors categorize and analyze the in-depth interview data obtained from communication scholars in Türkiye under prominent headings.

1.1 GenAI in Education

“From chalkboards to educational films, from Massive Open Online Courses (MOOCs) to Internet, the technologies of each era have been greeted with hype, hope, and disappointment - that is, with similar rhetoric” (Mishra; Heath, 2024, p.14). The literature on the use of GenAI in education is similar to this trend.

In recent years, studies focusing on students’ and academics’ perceptions, benefits, and challenges related to GenAI (Chan; Hu, 2023; Yusuf et al., 2024; Van Wyk, 2024) have attracted attention. For example, Chan; Hu (2023) investigated university students’ views on GenAI in higher education. Students expressed a positive attitude towards technologies such as ChatGPT and appreciated its support in academic writing. This contribution was also highlighted by Malik et al. (2023), who articulated the potential benefits of GenAI in enhancing writing creativity and facilitating the creation of original works of art and literature.

Most current policy debates focus on the "what works" agenda, such as whether universities have allowed or banned the use of GenAI in student work, the use of AI detection tools, and the potential effectiveness of specific policy recommendations (Luo, 2024, p. 3). On the other hand, despite efforts, there needs to be more reliable AI detection tools (Lodge et al., 2023; Peres et al., 2023). In addition, both international policy organizations and universities from different countries worldwide have been publishing guidelines for the use of using GenAI by students and academics (Chan, 2023). The Council of Higher Education published "Ethical Guidelines on the Use of GenAI in Scientific Research and Publication Activities of Higher Education Institutions" in May 2024, similar to those in the world in Türkiye (YÖK, 2024). In this context, diversification and prevalence are observed in studies examining policies and guidelines for using GenAI in higher education (Luo, 2024; Schiff, 2022).

Some studies highlight the transformative opportunities that GenAI offers in education, including personalized learning, research support, and enhanced problem-solving skills. However, they also highlight the importance of addressing challenges such as “biases, ethical considerations, and the need for responsible practice” (Yusuf et al., 2024, p.4). It would be pertinent to further examine the literature on the opportunities created by the use of GenAI in education and the potential problems it causes/may cause.

These technologies can enrich the educational process, from creating customized educational materials to supporting different learning strategies, thereby increasing the effectiveness and accessibility of education. Moreover, GenAI’s ability to analyze and generate complex data can significantly contribute to research methodologies and enable educators and students to explore new frontiers of knowledge and learning (Ghimire; Edwards, 2024). In addition, Stojanov (2023) highlights the potential of GenAI to tailor education to individual needs. Besides, Kohnke et al. (2023) examined English teachers’ perspectives on generative AI and emphasized GenAI's ability to promote individualized learning and educational support.

Yılmaz; Yılmaz (2023) reported on the potential of GenAI to improve students’ coding skills through rapid coding feedback. Furthermore, Mizumoto; Eguchi (2023) investigated the accuracy and reliability of ChatGPT as an automated essay grading tool. Their results show that ChatGPT reduces grading time, maintains scoring consistency, and provides immediate feedback and scores on students’ writing skills. According to this research, GenAI has the power to transform higher education by improving student outcomes and revolutionizing the teaching and learning process.

However, the literature has also identified concerns such as bias, privacy issues, the potential for misinformation, and academic misconduct caused by GenAI (Chan; Hu, 2023; Malik et al., 2023). For example, a student may transfer a unique insight from ChatGPT in an assignment, but may not be able to reproduce the same result for later verification. This type of variability can inadvertently lead to academic inconsistencies. GenAI distracts students from focusing on core curriculum topics and leads them to endless information tangents. This becomes especially problematic when students need more information literacy and prioritize engaging but superficial AI-generated concepts and discussions over core curricular concepts. In a recent study by Chan; Tsi (2023), a particular concern is the development of holistic skills such as creativity and critical thinking. Other areas of concern include dependency due to over-reliance on generative AI, the problem of accuracy of information, lack of reasoning, a possible decrease in cognitive and logical skills, homogenization of thoughts, information overload, and the potential for plagiarism (Chan; Colloton, 2024; Ipek et al., 2023). Academic integrity is another primary concern when it comes to the use of AI tools. Students’ ease with obtaining detailed answers can blur the boundaries of independent research and trust in AI-generated content (Chan; Lee, 2023). Last but not least, the growing energy footprint of artificial intelligence is also a major concern (De Vries, 2023).

Lim et al. (2023) provide compelling evidence that GenAI encourages academic misconduct by revealing a self-plagiarism index of 59%. Such a high similarity index highlights “the importance of critically evaluating textual responses generated by GenAI” (Yusuf et al., 2024, p. 4). Concerns have also been growing at the start of the 2023 academic year, as educators have reacted to reports that GenAI has been able to pass medical and MBA exams (Kelly, 2023; Purtill, 2023). Other caveats include recognizing that GenAI tends to produce inaccurate and overly authoritarian responses (Ipek et al., 2023). Consequently, while GenAI can be a valuable tool, it is crucial to maintain a balance and preserve its capabilities (Hsu; Ching, 2024). In support of these findings, a recent study of high school students in Türkiye found that when solving maths practice problems, students using ChatGPT solved 48 percent of the practice problems more correctly, but ultimately scored 17 percent lower on a test related to the subject they had learned. It is clear, therefore, that care must be taken in using GenAI to ensure that humans continue to learn critical skills to maintain long-term productivity (Bastani et al., 2024).

For second language learners, over-reliance on GenAI tools may jeopardize learners’ genuine efforts to improve their writing competence (Warschauer et al., 2023). Furthermore, if the dataset on which GenAI is trained contains problematic elements, the content produced by GenAI may be biased, inaccurate, or harmful (Harrer, 2023). For instance, AI-generated images may contain nudity or obscenity and may be generated for malicious purposes such as deepfakes (Maerten; Soydaner, 2023).

Finally, although there are no direct academic studies on GenAI and communication education, studies focusing on using GenAI in communication/media-related fields have become widespread in recent years. For instance, a study focusing on the use of GenAI in the digital communication and marketing sector found that professionals use GenAI to produce high-quality content quickly and effectively (Rolán et al., 2024). Similarly, some studies focus on using GenAI in journalism (Cecil, 2024; Onebunne, 2024; Bayer, 2024). On the other hand, the present study has the potential to contribute to the field because it focuses on the use of GenAI in communication studies education, not in the media sector.

2 METHODOLOGY AND SAMPLING

As of the 2023-2024 academic year, there are communication faculties in 62 universities in Türkiye, of which 41 are state and 21 private. If the 5 communication faculties in the Turkish Republic of Northern Cyprus are added to these figures, the number rises to 67. With 4 inactive faculties and 5 newly established faculties, the number reaches 76. In the 2023-2024 academic year, 11,353 quotas have been allocated to communication faculties in state universities and 4,723 quotas to communication faculties in private universities (İrvan, 2023). In the present study, which was conducted using the semi-structured in-depth interview technique, one of the qualitative research methods, online in-depth interviews were conducted with 15 academics from communication faculties of public and private universities in the three most populous cities of Türkiye (Istanbul, Ankara and Izmir), whose information is presented in Table 1.

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Table 1
Information About the Participants (In alphabetical order according to surname)

Purposive convenience sampling, a non-probability sampling strategy in which participants are selected from the target population based on pragmatic factors, was used to select the participants. These factors may include availability, willingness to participate, accessibility, and proximity to a location (Neuman, 2007). As the main aim of the study was to learn more about the specific group rather than to extrapolate the findings to the population at large, the purposive convenience sampling approach was chosen.

All the academics selected in the sample were included in the study because they teach theoretical and practical courses in areas such as digital technologies and new media. All participants have been teaching undergraduate and postgraduate courses in communication faculties for 5-20 years (Table 1).

The interviews focused on the process of academics learning about GenAI technologies in the context of TAM and DIT, the integration of these technologies into course materials, how students benefit from GenAI tools, the main benefits of GenAI in education, challenges faced, ethical issues, institutional support and policies, Türkiye’s educational policies on GenAI, and predictions for future. The research seeks to address the following questions:

RQ1 When and how do the communication academics encounter GenAI, and how do they integrate these technologies into their educational activities?

RQ2 What are the potential benefits and harms of using and integrating GenAI in university education?

3 FINDINGS

Both TAM (Davis, 1989) and DIT (Rogers, 1962) provide essential findings in the context of GenAI as widely used frameworks on how new technologies are adopted and used. Thus, centering on TAM and DIT as the theoretical framework, we conducted a comprehensive evaluation of the research data, covering a wide range of categories such as “GenAI in the context of technology acceptance”, “first encounter with GenAI”, “practices of use in academic activities”, “academics attitude towards students’ use of GenAI”, “potential benefits and challenges”, “institutional GenAI policies”, “GenAI policies in Türkiye's higher education,” and “future predictions.”

3.1 GenAI in the context of technology acceptance

The academics interviewed frequently use popular GenAI applications such as ChatGPT, Google Gemini (formerly known as Bard), and Grok, as well as sites such as GPT-Consensus, Scite, Elicit, and Paperpal, which are used directly in academic production. In addition, participants also use AI extensions such as Firefly, which is integrated into programs such as Adobe Photoshop and Illustrator, which are used in the field of visual communication design; translation and language competence tools such as DeepL Translate and Grammarly; paraphrasing tools such as QuillBot and DeepL Write; visual creation tools such as Leonardo.Ai and Midjourney; and sound creation tools such as SunoAI. Besides, GenAI applications integrated into data analysis tools such as MAXQDA; and tools that convert audio to text such as Transcriptor are popular among the participants. Furthermore, the academics also use presentation preparation platforms such as Gamma and Canva, student feedback tools such as Slido, and GenAI examples of health communication imagery.

The research data were rigorously evaluated within the framework of TAM, a model that emphasizes the importance of users’ perception in technology adoption. According to TAM, if users believe that the technology will enhance their work performance (usefulness) and is easy to use (ease of use), they are more likely to adopt the technology (Davis, 1989).

In terms of “perceived usefulness,” a significant number of the participants think that GenAI provides efficacy and productivity, especially in the context of language translation and data analysis, opens minds by increasing the quality of work, improves their professions in terms of automation and organization, provides refined data. On the other hand, some participants expressed concerns about the decline in creativity and memory.

“In ancient Egypt, Thoth, the revered God of Wisdom, presented his invention of writing to Thamus, the King of the Gods, with great excitement. He was opposed, saying, 'The invention of writing will not lead to wisdom, but to people appearing wise'. Writing, however, is actually the recipe for recording our memory. In other words, before writing people were strengthening their memory by keeping track of everything in their minds. GenAI has been creating similar debates in modern world.” Çiğdem Aytekin

In terms of “perceived ease of use,” although participants listed benefits such as minimal technical setup, easy integration, speed, time-saving, practicality, anytime-anywhere access, and free of effort, there were participants who expressed concerns about the digital divide that paid applications would create. In parallel, solely eight respondents indicated that they have tried or are currently in use of paid-for versions.

“The resources available to universities have already been unequal. If this inequality in the use of AI deepens, it could further disadvantage students and academics in underdeveloped or developing countries.” Huriye Toker

“GenAI is not economical for Türkiye. When using DeepL, I paid 1500 TL monthly for four months-6,000 TL. The minimum wage in this country is 17.000 TL. So, I took one-third of the minimum wage and invested it only in software.” Erdem Alper Turan

“Contrary to social media, some versions of these tools are not 'freemium'. We may have inadvertently caused this situation by criticizing the idea that 'if you do not pay for a product, you are the product'! The free versions may not be accessible due to capacity issues. I have to invest part of my salary in GenAI.” Erkan Saka

Similarly, three participants stated that they use the paid versions of ChatGPT and Grammarly through a joint account with their colleagues. As a result, both perceived ease of use and perceived usefulness are effective in academics’ adoption of GenAI despite financial impossibilities.

3.2 First encounter with GenAI

According to DIT, early adopters are not only eager to explore new technologies but also play a pivotal role in shaping the future by affirming their usefulness in society (Rogers, 1962). Despite some ethical reservations, the participants could be characterized as early adapters due to their professions and area of interest. They all have a natural desire to be trendsetters, a role that significantly impacts the future of technology.

The participants responses to their first encounters with GenAI were varied and complex. However, all the participants are academically interested in communication technologies and digitalization. Some participants highlighted their first encounters with GenAI through popular and academic publications, conferences, technological news and design channels, colleagues, spouses, and social media accounts, particularly X.

“I remember the blessed moment when I first encountered GenAI! As soon as ChatGPT was released, I started using it. The next day, in one of my classes, I said to my students, 'Look at this! You can do your homework using GenAI'. To my surprise, they started using it immediately! (Laughs). However, in the summer of 2022, before ChatGPT, I learned about MidJourney. I met the person who managed Datça Municipality's Twitter account for a project. He showed it to me. My mouth fell open in amazement at the ease of use of these AI tools, reassuring me about the learning process.” Erkan Saka

“I must have met GenAI in 98 or 99, when the internet became widespread in Türkiye. It was the time when the application called Internet Relay Chat (IRC) started to become popular. The first simple, primitive, but innovative chatbots emerged, which could generate responses from a library you defined yourself. In the 2010s, statistical applications were used for data analysis. However, it has reached a very different dimension in the last few years and has become popular.” Emrah Ayaşlıoğlu

Surprisingly, two participants (Uğurlu and Tosyalı) stated they met GenAI thanks to their students.

“I first encountered Gen AI when I was teaching coding class. We wrote the code; we tried it, but it didn't work in the class. I said, 'Let us find the bug.' I could not find the error through trial. One of my students said, 'I found it.’ I asked him how he found it, and he said that by asking ChatGPT!” Hikmet Tosyalı

3.3 Practices of use in academic activities

Participants noted that GenAI helps them with a various of administrative and scholarly activities. Answering e-mails, drafting the agenda of a meeting, deciding whether to attend a congress, translating from Turkish to English and from English to Turkish, paraphrasing, adapting/editing references, creating a letter of intent template, finding concepts and theories that can relate to the topic being studied, creating a research question, consulting about the methodology, creating concrete examples about a topic, preparing course materials, using journal editorial activities/review, updating the course syllabus according to the program outcomes, organizing Excel data, systematizing information, encountering new concepts related to a specific field, creating a question bank, and consulting GenAI as an academic advisor were frequently recurring usage practices. Participants were reluctant to consult a source on a topic; they agreed that GenAI was already prone to errors or inaccuracies, which they described as ‘hallucinatory’. For instance, it might generate incorrect translations or provide irrelevant references. Therefore, they revise the references they get from GenAI.

Participants usually create their syllabi by themselves. GenAI even provides a generic syllabus for a course, they just consult it for brainstorming purposes. One participant explained that course content created solely by GenAI would be insufficient, but he received support in the technical parts of the syllabi:

“Having taught my courses for years, I know what and how to explain to my students. However, I can type a prompt to ChatGPT: 'This is my syllabus; prepare five learning objectives for me.' Nobody will read these sections except the invigilators. GenAI can also help to prepare rubrics. I can consult because it is difficult to quantify the measurement of social issues.” Erkan Saka

On the other hand, one participant (Ayaşlıoğlu), who mentions that course contents should be prepared with a collective mind and according to the needs of the geography, stated that GenAI should be used neither for his courses nor for the information package of the faculty. Conversely, BOZ stated that GenAI can be used to determine whether similar topics are covered in different courses offered by the same faculty. In addition, some respondents were concerned about using GenAI, especially for direct content creation and other creative professional practices.

“I’m hesitant about using GenAI on the academic side. The storage of data and the reliability of its sources are concerns, but it’s the ethical discomfort that weighs heaviest on me. When it comes to the evaluation files of associate professors, this ethical discomfort becomes even more pronounced, and I wish to assemble all the documents and make a report from them, but the ethical implications of using GenAI make me uneasy. There's a possibility that I may not get the right and sufficient result, which is a concern.” Burak Doğu

“A social scientist’s biggest ordeal is starting a new academic study. I am a bit embarrassed when I say this; I have trouble swallowing my pride, but I can type all the keywords, ask GenAI for a striking introduction sentence, and then edit it.” Erdem Alper Turan

“Uploading the data correctly to ChatGPT will allow you to analyze the data efficiently.” Tolga Çelik

“When I decide to make my lessons more active with scenarios, games, and icebreakers, GenAI is there to help me.” Aslı Elgün

“I have been supervising a PhD thesis that compares the performance of different GenAI platforms in writing sports news in Turkish.” Çiğdem Aytekin

3.4 Academics attitude towards students’ use of GenAI

All participants agreed that a blanket ban on GenAI in education is neither feasible nor desirable. They stressed that GenAI is not just a passing fad, instead, it will be a crucial component of education in the near future. They highlighted the need for students to use GenAI critically and creatively to foster innovation and originality. Participants also emphasized that the industry will expect students to have the skills to use GenAI effectively and correctly when they graduate. They highlighted the ethical use of GenAI in advice, translation, and language improvement. They felt that rather than discouraging ‘cheating’, students should be given more creative assignments. They also stressed the importance of copyright awareness, especially in visual productions, to make educators vigilant and proactive. Some concrete examples the participants gave regarding the students' problematic use of GenAI are listed below.

“Some students don't even feel the need to edit the text generated by GenAI. They deliver their assignments without even deleting the questions that GenAI asks, such as "How can I help you?" or ‘Do you need help with anything else?’” Erdem Alper Turan

“For a question with an answer of only three words, the students, probably because they find it challenging to go through the course materials, paste the question as it is into the ChatGPT and produce three pages of answers from there. I noticed this when they all wrote an example in their homework about Japan, a country we have yet to discuss in class. Communication education and science also involve artistic, aesthetic, and ethical principles. That is why I use it in a limited context.” Emrah Ayaşlıoğlu

“Students treat GenAI with a search engine reflex. In order to understand artificial intelligence or to enter the correct prompt, it is necessary to understand how it works.” Şafak Dikmen

“There is such a reality called 'ChatGPT Language and Literature'. I often remind students that although it can speed up their work and make them more productive, it is essential that they understand the content they are creating. Simply copying and pasting without understanding will not contribute to learning.” Fırat Tufan

“Even without ChatGPT, the student will steal it from somewhere. Maybe he/she will steal it from his/her friend... It is more critical to solve this bigger problem.” Hikmet Tosyalı

Some participants who include GenAI in their courses encourage critical reading of the content produced by GenAI through assignments. They emphasize that AI literacy should be part of education.

“In my PR writing course, I gave my students the press release data and told them to create it in ChatGPT or Gemini and find out where it was wrong.” Nihal Kocabay Şener

“First of all, I prepared a guideline for the students on how to use GenAI. Then I asked the students to imagine and create a brand and then a jingle from SunoAI for this brand. The students had to enter a prompt in English. Therefore, they first used DeepL, also an AI tool, to translate from Turkish to English. I appreciated their creative and detailed prompts.” Burak Çeber

“I had GenAI make a picture of the public sphere. I stuck it on the exam paper. I said, 'This is how the machine imagines the public sphere during the Enlightenment. Make a semiological analysis of it. Who is in this public sphere, and who is not? Why are there no women, for example? Furthermore, GenAI could not properly visualize our university's campus. As an assignment, I said, 'Let us give it commands so it can visualize it. I also have students create custom GPTs in class. For example, one of my Middle Eastern students developed a Halal-Haram bot. However, the data set is always Sunni sources from the Middle Ages... When I asked, 'Is the blockchain halal or haram?', it could not answer. 'What if I am not a Sunni Muslim?' I asked. ‘God forbid', he told me (Laughs).” Erkan Saka

“In a lesson on discrimination, we set out to make the GenAI discriminate. We took on the roles of a fascist and a hate speech generator, asking: 'What can I do to harm those I do not like and remove them from the world?' The AI’s responses were generally satisfactory, but it often echoed social stereotypes. For instance, we could not get a cowboy image on a skirt. GenAI puts a woman in a skirt next to a cowboy. Most of the time GenAI shows a brunette cowboy, not a blond cowboy with blue eyes.” Şafak Dikmen

“When I asked GenAI to write an announcement about climate change that could be broadcast on the radio, it produced wonderful texts. We did voice and announcement practice with the students using the microphone.” Fırat Tufan

Some participants focused on the deconstruction of content produced by GenAI and included such activities in their courses.

“In some practical lessons, we examine and discuss some specific prompts with the help of PromtHero platform.” Emrah Ayaşlıoğlu

“I asked the students to find a news article written by an artificial intelligence. What kind of algorithm did it use? What data source did it draw on?” Çiğdem Aytekin

It is striking that one participant (Tosyalı) emphasized that ChatGPT first answered his open-source exam questions, and the platform could get only 60. This finding supports the argument that GenAI is the 'new C-work' (Bowen; Watson, 2024).

In conclusion, the participants make creative and intensive efforts to meet the ‘Generation Prompt’ needs in both theoretical and practical courses. These efforts, which are still mainly at the individual level, are promising for the future of communication education.

3.5 Potential benefits and challenges

In addition to the general and technical benefits of GenAI, it also has a significant potential to deliver benefits in a wide range of areas. These benefits include content categorization, summarization, perspective-shifting, real-time information awareness, validation, proofreading, translation, language proficiency, news writing, visual, verbal, and written content production, radio announcements and podcast creation, reference consulting, and foreign language skills enhancement.

Moreover, participants also expressed uncertainty about how and to what extent GenAI can be used in academic production. Most participants emphasized that translation is a more innocent practice. In addition, some participants drew attention to the margin of error of the programs developed for the detection of GenAI use.

“It is vital to keep an eye on new initiatives like Turnitin AI, which measures the use of AI in academic texts. Despite some professors' early interest, I am against its use. The potential for 'similarity false positives’ is a significant concern, as it could demotivate students or researchers.” Erkan Saka

Another participant explained that he used GenAI detection tools when he encountered an assignment that was inconsistent with a student’s performance during the course:

“I recognize my students by their performance in class. Sometimes, the assignments seem 'too good to be true.' Then I get suspicious.” Nevfel Boz

On the other hand, digital inequalities in access to paid versions, inaccurate/fictitious and biased content production, inability to disseminate alternative content due to access to solely open sources, plagiarism problems, privacy, death of creativity, Western centrism due to the databases GenAI feeds on, dissemination of false information by reproducing false information, and inadequacy of content production in Turkish compared to English were also highlighted.

3.6 Institutional GenAI policies

The GenAI policies of the institutions where the participants work vary. For instance, Doğu stated that they will add articles on the use of GenAI to the code of ethics document within the faculty, that they produce digital content and provide employment in a laboratory they have established, and that they have been preparing to use chatbots for students on websites by collaborating with local software developers. On the other hand, Turan has a distinct approach, emphasizing a corporate culture that values human contact. He does not consider it is appropriate for the Faculty of Communication to expose its students to artificial intelligence, reaffirming his commitment to personal interaction.

Participants stated they would like their institutions to include GenAI when consulting with academics about the annual library resource list/database request. Dikmen stated that they had purchased editorial software and AI extensions under license and expect to use them in the middle of the semester. Boz stated that applications that can facilitate the work of administrative staff can be integrated. In addition, Çeber mentioned that they have integrated a chatbot to the activity tracking system. Turan and Saka also stated that their university had established an AI committee/center within the university.

Many participants emphasized that they had included GenAI as an optional course in their curriculum or planned to do so shortly. It was also stressed that the content of existing courses should be organized to cover GenAI. All participants pointed out that the university organizes events and trainings, but Elgün raised the issue that trainers need to be trained.

3.7 GenAI policies in Türkiye’s higher education

A significant portion of the participants demonstrated a commendable level of awareness of the document ‘Ethical Guidelines on the Use of GenAI in Scientific Research and Publication Activities of Higher Education Institutions’ (YÖK, 2024). This document, published and sent to universities by the Council of Higher Education in Türkiye, was well-known among the participants. Only 3 participants stated that they had never heard of it, and 3 stated that they could not examine it in detail due to their administrative and academic workload. This guideline emphasizes that academic activities carried out with the support of GenAI should be reviewed, errors should be corrected, contextual consistency should be ensured, and it reminds the authors that they have legal and ethical responsibility for the resulting product. It is underlined that care should be taken during the collection, protection, storage, and transfer of personal data. The data sources that GenAI feeds on may not be fully open and may be biased due to factors such as the demographics of the data source or the algorithms used. It is crucial that researchers indicate at which stages and how they benefit from GenAI, as this can help to ensure that the use of GenAI is justified and ethical. Institutional up-to-date ethical guidelines and checklists are called for. Information on the use of GenAI should be provided in the documents submitted to universities’ ethics committees. It is emphasized that data generated directly by GenAI (such as surveys) cannot be used. To sum, researchers should act responsibly towards individuals, societies, and living things from which data are obtained. The participants who were aware of the guidelines emphasized that the document is thoughtful, detailed, pioneering in timing, proactive, and aligned with the guidelines and strategy texts worldwide. They underlined the urgent need for regular and frequent updates to the document, considering the rapid development of technology.

Moreover, Elgün pointed out that the Digital Transformation Office of the Presidency of the Republic of Türkiye also published a strategy document called ‘National Artificial Intelligence Strategy 2021-2025’ (2021), while Çelik emphasized that artificial intelligence studies are encouraged as a priority area by TÜBİTAK in Türkiye. Besides, Tufan stated that he heard that the Turkish Council of Higher Education will introduce a compulsory course on artificial intelligence in all departments. On the other hand, Saka pointed out that the lack of Turkish data in Türkiye limits country-specific productions. In contrast, Turan pointed out that academics cannot devote enough time to such guides and documents, which are essential, due to unnecessary information constantly coming through the electronic document management system. Kocabay Şener stated that many events are organized in Türkiye about AI, but he expressed his concerns that strategy documents/result reports are not kept and circulated correctly. Aytekin underlined the need for a standard guideline for using GenAI in journals indexed in Türkiye, such as DergiPark.

3.8 Future predictions

Pointing out that GenAI affects almost every stage of higher education, such as curricula, education and training systems, academic production and dissemination, assessment and evaluation, data collection, and data analysis, the participants highlighted the need for a collective effort. They identified the need for intercultural studies focusing on ethical issues, different usage practices, and GenAI platforms produced directly for educational purposes will be needed. Emphasizing the need for a working horizon that embraces the search for community-based solutions and progress as much as technological competence, they underlined the need to support the convergence between social scientists and computer sciences. Noting that GenAI cannot fully take over the role of educators, one participant said:

“GenAI cannot take our place. As human beings, we can understand when a student’s mood is down, that he/she is sad, and that he/she has a problem, even from his/her eyes.” Özge Uğurlu

Participants stated that while voice-over, translation, and acting in the media sector would be heavily affected by GenAI, they also highlighted the potential for new opportunities. They emphasized that the need for sound and image production, editing, and broadcasting expertise will decrease, creating space for other business lines to be derived with GenAI. Some participants even predicted that human-machine content, with artificial intelligence as the second or first author, could be observed in academic literature, suggesting a hopeful future for the media sector.

4 CONCLUSIONS

This study explored academic attitudes and usage practices in communication faculties across three significant provinces in Türkiye towards GenAI in education. The findings revealed that despite the financial barriers posed by paid GenAI platforms, participants actively utilize them as early adopters. Most participants learned about GenAI through social media, technology websites, and their colleagues, with factors such as speed, ease of use, practicality, and productivity driving its adoption.

Participants employed GenAI in theoretical and practical courses, benefiting from its creative capabilities. However, they also maintained a critical stance on ethical issues, including inaccurate or fabricated content, bias, the potential loss of creativity, and copyright concerns. This critical approach reassures the participants’ commitment to the ethical use of GenAI.

Institutions have adopted various methods to integrate GenAI into course content and academic activities. While Türkiye’s policies on GenAI in higher education are evaluated positively, there is a consensus that it requires further enhancement. Participants emphasized the importance of shifting from knowledge-based to skill-based education for the “Generation Prompt,” while predicting a significant decline in media-related employment in the future.

Future studies should broaden their scope to include academics from various disciplines and conduct cross-cultural comparisons to understand how different contexts influence GenAI adoption. Future research could also benefit from a longitudinal approach to track changing attitudes, alongside exploring student perspectives on GenAI’s impact on learning. Examining educational outcomes and the influence of institutional and national policies would provide insights into its effectiveness. Additionally, studies could focus on increasing ethical concerns, addressing access barriers, and designing skill-based curricula to better prepare students for future employment challenges.

  • PUBLISHER
    Universidade Federal de Santa Catarina. The ideas expressed in this article are the responsibility of the authors and do not necessarily represent the opinion of the editors or the university.

REFERENCES

  • EDITORS
    Edgar Bisset Alvarez, Genilson Geraldo, Jônatas Edison da Silva, Mayara Madeira Trevisol, Edna Karina da Silva Lira e Luan Soares Silva.

Publication Dates

  • Publication in this collection
    07 Apr 2025
  • Date of issue
    2025

History

  • Received
    12 Oct 2024
  • Accepted
    06 Dec 2024
  • Published
    14 Mar 2025
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